Hype Cycle for CRM Technologies, 2025

9 July 2025 - ID G00827302 - 141 min read By: Irina Guseva, Ilona Hansen, Olive Huang Initiatives:CRM Strategy and Customer Experience Discover how AI is transforming CRMs, enabling real-time decision making and cutting time to market. This Hype Cycle showcases

transformative cross-CRM and AI innovations to inform applications leaders’ strategic investments and drive improved

customer experiences.

Strategic Planning Assumptions

By year-end 2028, at least 40% of agentic-AI-for-CRM projects will fail or flounder due to lack of quality and consistency of customer data, although this data is foundational to AI success. By 2028, agentic AI will cut customer relationship management user adoption time by 50%, revolutionizing user responsiveness and customer adaptability. By 2027, more than 50% of CRM projects in large organizations will have a multidepartmental or enterprisewide scope. Through 2027, agentic AI will become the No. 1 technology newly deployed to improve customer experience. By 2027, CRM users will reduce their screen time by 50%, driven by AI-enhanced graphical user interfaces, AI assistants, productivity tool integration and AI agents. By 2027, agentic AI will power more than 50% of all customer engagement interactions.

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Analysis

What You Need to Know

The 2025 Hype Cycle for CRM Technologies marks a significant milestone as the first CRM Hype Cycle since 2005. Over the past two decades, the CRM market has diversified into distinct submarkets such as customer relationship management (CRM) for marketing, sales, customer service, digital commerce and cross-CRM technologies. The emergence of AI — in many of its forms, including generative AI (GenAI) and agentic AI — are all transformative technologies poised to revolutionize CRM. This evolution necessitates a comprehensive evaluation across these submarkets.

This Hype Cycle represents the maturity, adoption and social application of technologies. It helps organizations anticipate the trajectory of innovation, from initial hype through disillusionment to eventual mainstream adoption.

This Hype Cycle report enables enterprise application leaders to understand new, innovative, AI-driven technologies to determine where to make investment decisions. As the hype around AI grows rapidly, confusion may arise, leading organizations into uncharted territory when assessing, experimenting with and adopting these innovations.

This year, AI innovations are moving swiftly from generative to agentic, with action clustering at the Peak of Inflated Expectations. Adjacent themes areas — such as customer data management and AI ethics — are becoming increasingly critical, demanding robust governance and responsible adoption. Organizations must act now by evaluating readiness, upskilling and reskilling teams and aligning AI strategies for the next wave of CRM transformation.

The Hype Cycle

AI in CRM is rapidly emerging as a transformative force, representing the most significant technological shift since the rise of SaaS. Over the past year, agentic AI has moved from startups and R&D labs into mainstream CRM products: Most CRM vendors now offer some of these capabilities. Early experiments with large language models (LLMs) revealed limitations such as unreliable outputs, which agentic AI now addresses through advanced reasoning and action loops. These goal-driven entities operate autonomously, leveraging memory, planning and guardrails to dynamically complete tasks and integrate foundational AI models into the execution layers of CRM for more human-centric interactions.

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Gartner

CRM is particularly well-suited for any type of AI due to its rich, underutilized data from customer interactions, mature automation frameworks and controlled environments with established privacy and trust protocols. Organizations have already gained valuable insights from earlier AI deployments aligned to CRM processes — especially those that are high velocity but low stakes and ideal for AI augmentation. Notably, agentic AI enables hyperpersonalization throughout the customer journey.

Key use cases for applying AI in CRMs:

■ Marketing automation and personalization

■ Advanced chatbots with enhanced reasoning

■ Automated knowledge management in commerce and service

■ Sales and employee productivity and support tools

■ Managerial insights through proactive analytics

These applications are rated as “transformational” in terms of their impact on CRM.

Market momentum is strong, with high client interest and rapid adoption.

In the short term, adoption will focus on augmenting existing CRM systems for highimpact, low-risk use cases, with many CRM customers expected to start implementing agentic AI now.

Figure 1: Hype Cycle for CRM Technologies, 2025

The Priority Matrix

The Priority Matrix is designed to identify technologies with significant potential for benefit and impact, focusing on those likely to reach maturity or achieve widespread adoption within a defined time frame. In short, the Priority Matrix organizes technologies in this Hype Cycle by their benefit and time to mainstream adoption. In the CRM domain, AI technologies are at the forefront, driving substantial advancements and transformations.

AI-Driven Innovations in CRM AI, including GenAI and agentic AI, is recognized as a pivotal force propelling CRM technologies. These innovations promise to redefine CRM by addressing existing challenges and unlocking new capabilities, thus attracting considerable interest from major vendors.

Technologies farther from the mainstream but expected to have significant benefit to the CRM domain include:

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■ Customer Engagement Center (CEC): A CEC is an integrated, omnichannel platform that leverages AI and automation to deliver personalized, seamless customer interactions across all communication channels. Rapid advances in AI, cloud adoption, and data integration will make AI-driven CECs the industry standard, enabling faster, more efficient, and highly tailored customer service.

■ GenAI Virtual Assistants: GenAI virtual assistants are advanced AI-powered tools that use large language models to understand, generate, and automate natural language interactions for tasks such as customer support, content creation, and workflow automation. Their rapid maturation over the next two years is driven by breakthroughs in AI model capabilities, expanding enterprise adoption, and increasing demand for scalable, humanlike digital assistance.

■ Prescriptive Analytics: Prescriptive analytics leverages AI and advanced algorithms to recommend specific actions or strategies by analyzing data, predicting outcomes, and optimizing decision-making processes. The technology is set to mature rapidly within the next two years due to advancements in machine learning, growing volumes of actionable data, and increased integration with business operations for real-time, data-driven recommendations.

Agentic AI Capabilities Agentic AI addresses the limitations of traditional AI models by employing advanced reasoning and action loops, allowing for more sophisticated task management and decision making. This capability is crucial for high-impact use cases within CRM.

Strategic Recommendations for Enterprise Application Leaders ■ Adoption Strategy: Stay informed, assess, experiment and adopt AI solutions to remain competitive.

■ Talent Development: Invest in upskilling and reskilling employees to build internal AI expertise and foster a culture of innovation.

■ Data Readiness: Establish robust data governance, quality and integration practices to ensure AI solutions are fueled by accurate and comprehensive information.

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Table 1: Priority Matrix for CRM Technologies, 2025

(Enlarged table in Appendix)

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On the Rise AI Agents for Commerce Operations Analysis By: Sandy Shen

Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience

Maturity: Embryonic

Definition: AI agents for commerce operations assist business users in automating digital commerce operational tasks, leading to better productivity and business outcomes. These agents have multiple skills to support business roles, including marketing, sales, service and supply chain. AI agents use conversational UI to interact with users and are typically powered by LLMs but can also use other technologies such as rule engines and machine learning.

Why This Is Important AI agents can improve productivity in digital commerce operations by automating tasks such as content generation, customer segmentation and campaign setups. These agents can also enhance business outcomes by suggesting optimization strategies that may not be obvious to business users. Organizations can apply rules and guardrails to constrain the actions taken by agents. Today, AI agents are emerging in digital commerce, but they will become a standard capability in vendor solutions within the next two years.

Business Impact AI agents can make business users more productive and help improve digital commerce outcomes by automating tasks and suggesting optimization strategies. AI agents, when improperly implemented, can negatively impact the business and CX by delivering misleading answers, exposing sensitive data to unintended users or taking wrong actions.

Drivers ■ GenAI features embedded in commerce platforms, such as product description generation, search optimization, review summaries and data insight analysis, are the stepping stones to AI agents.

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■ Conversational UI of AI agents makes the above embedded capabilities more accessible and easier to use for business users.

■ AI agents become the gateway to business systems supporting various tasks, reducing the number of tools business users need to interact with. These tasks include arranging campaigns, developing webpages, segmenting customers, suggesting personalization strategies, generating data insight and customizing reporting.

■ Organizations have high awareness and willingness to invest in AI agents to improve commerce productivity and performance.

■ Digital commerce vendors are deploying AI agents in their solutions, leveraging the extensive data residing in the platform. This drives customer adoption.

■ Low-code agent builder platforms are being extended to commerce platforms that allow business users to customize agent skills and workflows based on specific needs, and apply security policies and guardrails to improve the agent output.

Obstacles ■ There is a lot of noise about AI agents in the market. Vendors are “agent washing” solutions by calling automation or AI-enabled functionalities “AI agents.” Organizations get confused between AI agents, virtual assistants, chatbots and robotic process automation, and can invest in technologies designed for nonagentic use cases.

■ Organizations don’t always have sufficient or accurate data (e.g., customer preferences, inventory availability), nor robust data management to enable agents to fulfill tasks.

■ Insufficient or lack of security controls and guardrails can lead to security breaches or misconduct by AI agents, or unintended use of AI agents by employees.

■ Multiple agent model protocols are competing to become the standard to enable agents to connect with organization’s internal data and systems. These protocols will take time to mature and be adopted by organizations. Security management of these protocols and their interactions with enterprise systems are not yet defined.

User Recommendations ■ Use Gartner research to understand the scope and capabilities of AI agents and what they can reasonably achieve.

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■ Ensure that accurate and sufficient data is present for key commerce entities, such as product, pricing, inventory, order and customers. Take stock, organize and enrich the data for agents to easily access and retrieve. ■ Experiment with agentic capabilities embedded in digital commerce applications to assess their impact on productivity and business outcomes. When available, use low-code agent builders to construct your custom agent skills and workflows. ■ Set up security mechanisms and guardrails to regulate the input and output of AI agents to ensure compliance and accuracy. Put employees in the loop when needed to verify the behavior and output of AI agents. ■ Communicate the deployment of AI agents to employees and equip them with skills to work with agents in data preparation, prompt formulation, monitoring and feedback. Encourage them to focus on higher-value tasks such as goal setting, use cases and CX designs that cannot yet be undertaken by AI agents.

Sample Vendors KIBO; Salesforce; SAP; Shopify; VTEX

Gartner Recommended Reading AI Agents Assist Humans to Enhance Digital Commerce Performance

Innovation Insight: AI Agents

Customer Technology Platform Analysis By: Gene Alvarez, Mike Lowndes, Saul Brand, Andrew Gianni

Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience

Maturity: Embryonic

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Definition: A customer technology platform (CTP) is the integration of all customer-facing technology and applications into a platform. CTP aligns the customer’s “outside in” view of the organization’s customer experience (CX) with the “inside out” delivery of the organization’s CX vision, strategy and technology. It enables an organization to support a holistic and complete view of the CX that benefits both the customer and the organization.

Why This Is Important CTP is created using business capabilities and the customer application ecosystem. It helps organizations:

■ Connect their CX objectives to CRM strategy delivery

■ Identify necessary system interactions to support CX and CRM strategy for positive customer sentiment

■ Improve CRM systems moving toward a CTP

Business Impact Digitalization of the CX has exposed process gaps and disconnected customer-facing processes to customers. This is due to CRM applications implemented solely to automate individual processes within functional silos. Organizations need to address these gaps by viewing CRM applications in the context of CX-centric application strategy. Using a CTP approach to CRM applications can resolve these customer-facing gaps and lead to improved CX.

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Drivers ■ The delivery of a positive CX is a pivotal differentiator in the era of digital transformation. CTPs serve as the cornerstone for achieving this differentiation by seamlessly integrating customer-facing technologies and applications.

■ In their digital transformation journey, organizations often encounter fragmented CRM applications, which inadvertently position the customer as the coordinator of their experience across various points of interaction (POIs) such as voice, chatbots, websites, mobile applications, stores and field sales activities. CTP addresses these challenges by unifying the disparate elements, thereby relieving customers from the burden of managing their own CX across multiple touchpoints.

■ Organizations aiming to scale their CX capabilities increasingly rely on CTP to provide a relevant and integrated experience that is intelligently coordinated across all POIs. By leveraging CTP, these organizations can ensure that customer interactions are not only consistent but also contextually aware, enhancing the overall customer journey.

■ CTP enables organizations to deliver integrated experiences, such as “campaign to contract,” by facilitating seamless integration of applications like campaign management, lead management, sales force automation platforms and configure, price and quote systems. This integration empowers organizations to create intelligent, coordinated experiences across all POIs, thereby driving both customer satisfaction and organizational efficiency.

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Gartner

Obstacles

■ Major investments in CRM applications that are already live and operational in organizations are making it hard to orchestrate CRM applications into great CX.

■ It can be difficult to determine how to integrate CRM applications with the organization’s entire IT portfolio.

■ Investment in strategic vendor relationships has made the integration of many CRM applications a requirement that vendors must support. However, organizations may not be able to wait until then due to a need to improve their CX today.

■ Customer dissatisfaction or frustration can come from organizational inertia. Customers are exposed to new ways of doing things from competitors or organizations in other industries. They view the organization as lagging behind in helping customers with their “job to be done.” This organizational inertia can come from a variety of sources, such as the mindset that views change as a risk rather than a tool to improve CX.

User Recommendations

■ Apply Gartner’s CX CORE (see Rethink Customer Experience With the CX CORE Model: A Gartner Trend Insight Report) to develop a business capability model, ensuring alignment between the organization’s business and operating models for seamless integration.

■ Prevent CRM misalignment by aligning applications and technology with the business model, avoiding scenarios like self-check-out in luxury stores, maintaining brand integrity and CX objectives.

■ Utilize architecture that integrates business capability with the CRM application ecosystem, enabling intelligent coordination of key technologies within the CTP to deliver superior CX.

Sample Vendors Adobe; HubSpot; Microsoft; Oracle; Salesforce; SAP; Zendesk

Gartner Recommended Reading Improve CX With a Customer Technology Platform Reference Architecture Model

Quick Answer: How to Get Started With the CTP Reference Architecture Model for CX CORE

Enable Great Customer Experiences Using Gartner’s Customer Experience CORE Model

Adaptive Experiences Analysis By: Brent Stewart, Will Grant

Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience

Maturity: Emerging

Definition: Adaptive experiences refer to UIs and systems that dynamically modify their behavior, content and structure in real time based on user context, behavior, preferences and environmental signals. Unlike static or preconfigured products, adaptive experiences continuously learn from interactions and adjust elements, such as UI layout, feature availability, messaging, and workflows, to better align with user needs and preferences.

Why This Is Important Adaptive experiences are critical for developers, as they enhance user engagement, retention and satisfaction. Digital product teams value adaptive experiences for their potential to drive conversion rates and revenue, while businesses leverage them to differentiate their offerings in highly competitive markets. As users increasingly expect seamless, intuitive and hyperpersonalized experiences, digital product teams must build adaptive experiences that can process real-time data efficiently and responsively.

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Business Impact Adaptive experiences significantly improve customer satisfaction and loyalty by delivering content and experiences that feel tailored and relevant. Businesses adopting this transformative approach to user centricity see higher engagement, conversion rates and customer lifetime value. Adaptive experiences also enable new revenue streams through personalized recommendations, dynamic pricing and automated cross-selling, allowing companies to maximize user interactions while minimizing friction in the customer journey.

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Drivers ■ Rise of GenAI and GenUI: AI-driven generation of content and experiences enables rapid, individualized messaging, UI elements and media.

■ Advancements in AI and machine learning (ML): Improved deep learning models enable real-time analysis of user behavior, intent and preferences.

■ Big data and real-time analytics: Organizations now collect vast amounts of user data across multiple touchpoints, which can be processed instantly for adaptive experiences.

■ Edge computing and 5G: Faster data processing at the edge reduces latency, enabling more data-intensive interactions at run time.

■ Behavioral biometrics: Tracking user behavior (e.g., typing speed, browsing habits and accessibility options) allows for continuous adaptation of UI and content.

■ API-first and microservices architecture: Modern software architectures facilitate the rapid integration of adaptive and hyperpersonalized features across platforms.

■ Contextual awareness: AI-powered applications now use location, device, time and previous interactions to tailor experiences dynamically.

■ Consumer expectation shifts: Users now demand intuitive, anticipatory experiences where software adapts to their needs without manual input. For example, when a user gets in a vehicle at 5:30 p.m., the UI asks, “Driving home?”

■ Privacy-compliant personalization: New frameworks (e.g., federated learning and zero-party data) allow hyperpersonalization while maintaining user privacy and regulatory compliance.

■ Digital accessibility: Real-time adjustments can aid various disabilities (e.g., simplifying layouts for cognitive impairments or increasing color contrast for color- blind or low-vision users).

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Obstacles ■ Data privacy regulations: Stricter laws (e.g., General Data Protection Regulation and California Consumer Privacy Act) limit the use of personal data, requiring compliance-driven strategies.

■ Computational complexity: Real-time AI-driven adaptive experiences and hyperpersonalization demand high processing power and optimized architectures.

■ Limited off-the-shelf options: Plug-and-play software services to enable adaptive experiences are still in their early stages, but we expect this to mature and commercialize rapidly.

■ Data silos and integration challenges: Inconsistent data across platforms can hinder effective adaptive experience efforts.

■ User trust and ethical concerns: Overpersonalization may feel invasive, leading to user discomfort and brand distrust.

■ Scalability issues: Managing adaptive experiences for millions of users while maintaining speed and reliability is a technical challenge.

■ Bias in AI models: Poorly trained models may reinforce stereotypes or create unfair user experiences.

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User Recommendations ■ Invest in AI infrastructure by ensuring ML models are optimized for real-time inference and scalability. ■ Experiment with adaptive experiences, testing new concepts regularly with users to ensure their needs are met. ■ Adopt a privacy-first approach by implementing differential privacy, edge processing and transparent data practices. ■ Leverage multimodal data by combining behavioral, contextual and historical data to refine personalization algorithms. ■ Use analytics and AI feedback loops by continuously refining adaptive and/or hyperpersonalization models based on live user data. ■ Ensure ethical AI governance by regularly auditing AI models to mitigate bias and enhance fairness. Avoid deceptive patterns at all costs. ■ Optimize for omnichannel consistency by maintaining seamless adaptations and personalization across web, mobile, Internet of Things and voice interfaces. ■ Educate users on adaptation and personalization controls by offering clear options for users to manage their adaptive experiences and data preferences.

Sample Vendors Adobe; Salesforce Gartner Recommended Reading Innovation Insight: Generative AI for User Interfaces Predicts 2024: AI Will Change Customer Acquisition for Tech CEOs Podcast: Demystifying Personalization Magic Quadrant for Personalization Engines Critical Capabilities for Personalization Engines Digital Twin of a Customer Analysis By: Lizzy Foo Kune Gartner, Inc. | G00827302 Page 17 of 125 This research note is restricted to the personal use of kylie@polyai.com.

Benefit Rating: High

Market Penetration: 1% to 5% of target audience

Maturity: Embryonic

Definition: A digital twin of a customer (DToC) is a dynamic virtual mirror representation of a customer that organizations can use to simulate, as well as to emulate and anticipate, behavior and/or fit. Customers can be individuals, enterprise customers, personas, groups of people or machines. DToCs enable organizations to raise the strategic value of data and analytics investments by expanding the range of insights derived from — and reducing risks associated with — customer data.

Why This Is Important DToCs help organizations better understand their customers and anticipate behavior, given certain combinations of data and parameters (e.g., products and services). They increase efficiency and provide a personalized, optimized service to customers, many of whose buying habits repeatedly change. Organizations can use a DToC to modify and enhance customer experience (CX) and support new digitalization efforts, products, services and opportunities.

Business Impact Today, organizations use digital twins to monitor how a product performs and to determine the next best action. They can now use DToCs to simulate how a customer will react, given a specific set of ecosystem parameters, conditions, and control or input signals. DToCs can transform the way organizations sell products or services by providing customers with better experiences, leading to increased revenue and lasting customer relationships.

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Drivers ■ High data costs: The CX and regulatory costs of invasive data collection techniques fuel the drive to find alternative sources of insight for continued business growth or competitive differentiation.

■ Journey orchestration: New methods engage and anticipate a customer’s journey more effectively. DToCs can help by simulating and optimizing how specific cohorts will respond before the journey is built in a campaign workflow.

■ Emerging AI techniques: Organizations can define and observe new journey use cases, limiting the need for active customer data collection or live testing. The rapid acceleration of interest in agentic AI, generative AI (GenAI), emotion AI and influence engineering technologies brings more complex solutions incrementally closer to realization.

■ Operational efficiency: Declining marketing budgets force marketers to do more with the same, or a smaller, budget. DToCs could limit waste, reducing the costs of imprecise marketing or misaligned products, and reducing the time spent on testing underperforming journey interventions.

■ Product and experience development: New data-driven business models could emerge as marketers find new ways to serve and capture customers in a privacy- constrained environment.

■ Customer empathy: DToC promises CX professionals the ability to reduce data collection, like voice-of-the-customer surveys, while also sharpening the insights about customers’ emotions, attitudes and beliefs. Combined with GenAI and AI assistant interfaces, DToC could soon emerge from the data science lab and influence brand and CX strategy.

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Obstacles ■ The technology behind digital twins has focused on organizations and products. A customer focus is emerging, and a lack of clear KPIs and other success measures limits the potential use of DToCs.

■ DToC technology still addresses only highly technical audiences, limiting adoption.

■ Privacy concerns exist for simulations not originally agreed to by the customer. There’s also increased compliance risk.

■ DToCs could potentially be misused, constraining use cases due to stakeholder concerns about adverse consumer reaction, regulatory scrutiny or other risks.

■ Organizations need to establish trust with customers for them to agree to share their information. Customers will need transparency about what data is collected, how it will be used, and the privacy and data controls that will be applied. Unless paired with data ethics, DToC tools and techniques may increase risks to brand reputation and performance.

User Recommendations ■ Identify use cases for which DToCs could help deliver a better CX, and where suitable data is available, by examining customer journeys and failure points.

■ Define the benefits to customers and establish trust. Explain how they can control or cancel data usage. Use technology to collect consent and manage data subject rights requests, all while working across functions including operations, compliance, legal and communications to establish policy.

■ Start by running a pilot and comparing results with and without a DToC over an adequate period using statistically significant data, whether you choose to build or buy a DToC. Establish benchmarks for your pilot to better develop and scale DToC. Establish a holdout group as a control before you begin.

■ Integrate DToCs with marketing technology systems for maximum utility.

■ Establish a trust center to house privacy and security documentation, as well as documented expectations. Incorporate suggestions from a customer advisory panel to avoid risk.

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Gartner

Sample Vendors

Edge Total Intelligence; Fetch.ai; Infogain; Nstream; Salesforce; ServiceNow; Tata Consultancy Services

Gartner Recommended Reading

A Digital Twin of a Customer Predicts the Best Customer Experience

Quick Answer: Privacy Basics for a Digital Twin of a Customer

Quick Answer: What Are the Capabilities of a Digital Twin of a Customer?

Supply Chain Executive Report: Drive Growth & Elevate Experiences With Digital Twin of the Customer

Gartner Five-Year Outlook: The Future of Digital Commerce in 2030

Machine Sellers

Analysis By: Luke Tipping, Daniel Hawkyard

Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience

Sales organizations can achieve scale using machine sellers to automate deal workflows that would require significant human resources to complete. Machine sellers satisfy buyer preferences by automating deal activities to provide a frictionless experience. They unburden human sellers from low-value activities, enabling them to refocus on high-value tasks. Machine sellers can also provide the vast datasets and instant response times that machine customers, deployed by buying organizations, expect.

Maturity: Emerging

Definition:

Machine sellers are nonhuman agents that automate end-to-end sales workflows for simple transactions or complete specific deal activities on behalf of human sellers during more complex sales processes. Currently, machine sellers are predominantly used to facilitate routine, predictable transactions.

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Business Impact Sales organizations deploying machine sellers will gain a competitive advantage, locking in recurring revenue by satisfying buyer preferences for seamless purchases. Machine sellers enable organizations to meet the expectations of machine customers at scale and unlock seller productivity by unburdening them from involvement in nonstrategic purchases and low-value activities. Organizations that do not adopt machine sellers risk wasting resources, decreasing efficiency and missing revenue goals.

Drivers ■ Machine sellers present an opportunity for sales organizations to drive recurring revenue and shorten sales cycle times by automating repurchases. They provide revenue and margin enhancement opportunities by surfacing buyer needs that may not be immediately obvious to sellers or buyers, and product recommendations optimized for conversion rate or improved profit margins.

■ Buyers increasingly expect suppliers to deliver an effortless and frictionless customer experience. According to Gartner’s 2024 B2B Buyer Survey, 61% of B2B buyers state that they prefer rep-free sales experiences. Machines are better equipped than humans to make instant, data-driven decisions that meet buyer demands for streamlined purchasing processes, cost-efficiencies and productivity gains.

■ According to Gartner’s 2025 CEO and Senior Business Executive Survey, 29% of CEOs are working on a strategy to engage with machine customers and AI agents, and half of them will have a strategy within two years. Machine sellers will be critical to this. With the increasing level of sophistication and autonomy of agentic AI, machine sellers and machine customers will interact with each other to make complex purchase decisions and transact among themselves.

■ Machine sellers offer productivity gains to sales organizations by unburdening sellers from overseeing routine, nonstrategic transactions, and automating more complex deal activities, enabling sellers to focus on high-value tasks.

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Obstacles ■ Machine sellers are emerging from a technology perspective and do not represent a single market. Instead, sales organizations leverage software agents or technology embedded within connected products to support specific tasks or outcomes for customers, such as automated reordering or subscriptions.

■ Sales organizations deploying machine sellers must establish trust in the technology across internal and external stakeholders. Sales leaders must build confidence that tasks executed by the technology deliver optimal outcomes for all parties.

■ The impact of machine sellers will not be evenly distributed, varying by industry, geography, business model and use case. Businesses involved in highly routine, predictable transactions represent the most immediate opportunity and will be early adopters in both B2B and B2C markets.

■ Sophisticated, complex industries will be less likely to adopt machine sellers in the short term as buyers prefer to receive guidance, validation and confidence from humans.

User Recommendations ■ Incorporate machine sellers into your go-to-market strategy roadmap within the next two to three years. Regardless of your industry or solutions, buying organizations will increasingly expect suppliers to offer efficient, frictionless purchase experiences.

■ Establish a cross-functional team to explore the business potential of launching your own machine sellers to drive revenue and customer retention. Evaluate your offerings and customer segments to identify those most suited to routine, predictable transactions that can be serviced via the autoreplenishment capabilities of simple machine sellers.

■ Conduct an assessment of complex deal processes, identifying the most burdensome activities for sellers and buyers, to surface opportunities to automate these tasks via AI-supported machine sellers.

■ Pilot machine sellers to facilitate tasks, such as solution building, quote creation and contract writing, to determine productivity and effectiveness gains compared to human-seller-led processes.

Gartner Recommended Reading Machine Customers Are Coming to Disrupt B2B Sales

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What Should CSOs Do About Machine Customers Right Now?

Adapt Machine Customer Sales Engagements or Risk Revenue Loss

CSOs Must Adapt Sales Engagement Strategies for AI-Driven Buyers

Perceptive Analytics Analysis By: Deepak Seth, Afraz Jaffri

Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience

Maturity: Emerging

Definition: Perceptive analytics leverages AI technologies, including AI agents, to deliver context- aware strategic insights and executable recommendations that continuously adapt to business conditions by monitoring and responding to analysis of structured and unstructured data, business goals and user needs. It empowers organizations with deep situational awareness that proactively optimizes outcomes through autonomous or collaborative planning, action and feedback.

Why This Is Important Existing analytics processes often suffer from being reactive and backward-looking and struggle to keep pace with the speed and complexity of modern business and market dynamics. They rely heavily on predefined metrics and human interpretation, which leads to delays in crucial shift identification, missed opportunities for proactive intervention and limited ability to understand the underlying context that drives organizational value. The result? Businesses too often base decisions on outdated information, react slowly to emerging threats or opportunities and lack the agility to thrive in volatile environments.

Business Impact Perceptive analytics will be transformative, enabling organizations to move from reactive to proactive decision making that grants significant competitive advantages. By anticipating future trends and adapting strategies autonomously, businesses can optimize resource allocation, enhance customer experiences, mitigate risks more effectively, and identify new growth opportunities with greater speed and accuracy.

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Drivers ■ Advances in AI, particularly generative AI, give rise to large language models (LLMs) that possess a substantial underlying representation of business processes, industry trends and strategic foresight. This knowledge is crucial for decision making in environments with rapidly changing conditions.

■ Progress in creating autonomous and semiautonomous AI agents moves them beyond passive analysis. It allows orchestration of LLMs and other analytical tools to proactively monitor data, identify anomalies, trigger analyses and even execute actions based on perceived insights.

■ Cloud platforms’ scalability and cost-effectiveness enable them to provide the necessary infrastructure to store and process the massive datasets required for training and deploying LLMs and running complex AI agent systems that underpin perceptive analytics.

■ Advancements in real-time data processing technologies provide the continuous information flow needed for perceptive analytics to identify emerging trends and adapt in real time.

■ The global business landscape’s increasing volatility and complexity demand faster, more adaptive decision making.

■ Advancements in predictive modeling, natural language processing, knowledge graphs and reinforcement learning provide a strong foundation of tools and methodologies to integrate with LLMs and AI agent frameworks to create comprehensive perceptive analytics solutions.

Obstacles ■ Few vendors offer mature perceptive analytics capabilities, and organizations must decide between building in-house solutions or waiting to procure off-the-shelf solutions.

■ Perceptive analytics requires creation and management of trusted, context-rich data to deliver actionable insights. Ensuring this level of data quality and semantics is difficult.

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■ Many advanced LLMs and complex AI agent systems have a black-box nature that conceals the reasoning behind their recommendations. Building trust and ensuring transparency in how these systems arrive at conclusions are crucial for widespread adoption, especially in critical business applications.

■ Current shortages of experienced data scientists, AI engineers and domain experts who possess the interdisciplinary skills required to effectively build, deploy and maintain these complex systems pose a challenge. New types of roles will be required to create and maintain these systems.

User Recommendations ■ Identify specific business problems or opportunities where a more proactive, context- aware analytical approach could yield significant impact. Develop a roadmap for future work that shows long-term business benefits of perceptive analytics.

■ Initiate projects to improve data governance, address data silos, preserve and enrich semantic information, and develop strategies for augmenting existing datasets with external information and metadata.

■ Encourage collaboration among data scientists, domain experts and IT teams to ensure understanding of business needs and the technical capabilities of perceptive analytics. Create joint project teams to facilitate knowledge sharing.

Fluid Knowledge Analysis By: Tim Nelms, Stephen Emmott, Rachel O'Farrell, Marko Sillanpaa, Jed Cawthorne, Darin Stewart

Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience

Maturity: Embryonic

Definition: Fluid knowledge is the information that has been suitably conditioned to seamlessly flow between enterprise applications and people, enabled by AI agents and assistants. This information is conditioned to be accurate, pertinent and trusted. The conditioning of fluid knowledge leverages many aspects of information governance.

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Why This Is Important Employees are struggling with information overload and the ability to locate, retrieve and synthesize knowledge. These challenges mean that organizations fail to capitalize on critical knowledge that only exists in people’s heads or that has been stored in repositories few employees know exist. Fluid knowledge — enabled by AI — improves employees’ ability to share their expertise, contextualize their insight and discover new knowledge.

Business Impact Fluid knowledge unlocks sources of information for human and machine intelligence, operationalizing it for decision and action. Knowledge and information management have a profound impact on productivity, but the application of AI to these challenges remains in its infancy. According to the 2022 Gartner Service and Support Knowledge Management Governance Survey, 79% of leaders cite knowledge as extremely or very important to their mission. A little over a third (34%) of digital workers report that at least half the time they struggle to find the information necessary to do their job, according to the 2024 Gartner Digital Worker Survey.

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Drivers ■ Accelerating insight: Large language models (LLMs), combined with retrieval- augmented generation (RAG), ground their answers in the context of the enterprise information, which breaks down barriers to accessing knowledge and gaining insight.

■ Increasing automation: Organizations want more automation, yet find traditional approaches to defining and executing business processes slow to develop and deploy. AI agents can enable an easier creation of automation but rely on high- quality information to ground their insight, decisions and actions.

■ Improving quality and accuracy: Not all knowledge is equally valuable. Early experience found that without conditioning using ongoing remediation to assure pertinence and relevance as a means of building trust, AI assistants and agents struggle to ground their answers in facts. Knowledge is specific to business domains, and fluid knowledge concepts are best when applied in domain-specific ways.

■ Growing multimodal use cases: The availability of AI to understand different forms of human communication is expanding rapidly. Machine learning techniques, semantic indexing and deep reasoning augmented by LLMs mean that technology can accomplish the bulk of work to classify and categorize content, including conversational exchanges.

■ Promoting broad AI adoption: AI assistants will promote the widespread adoption of AI by employees — Everyday AI. We can now tap into the conversational exchanges that happen in peer networks and meetings that have always been elusive to capture.

■ Adopting knowledge graphs: Knowledge graphs use relationships between nodes in the graph to surface more relevant information. Tacit and explicit networks can provide real-time inputs to knowledge graphs that can guide how knowledge and insights related to peer groups and communities of practice can be captured and synthesized, where work is dynamic, situational or in flux.

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Obstacles ■ Information growth: Knowledge repositories are more diverse and spread out than ever due to the formal and informal interactions between employees, and the tools and information associated with their tasks.

■ Information quality: Redundant, obsolete and trivial (ROT) information means people and applications struggle to find relevant information to support their work. Failures in information governance that lead to ROT will have long-term consequences for those seeking to use fluid knowledge as a transformation agent.

■ Information governance maturity: Organizations aren’t aware of what information they have, where it is stored, how long they need to keep it or how to secure it. A lack of information governance maturity leads to redundant, obsolete and trivial information.

■ AI adoption: Adoption of AI into the everyday working practices of employees can be challenging. Employees lack skills, like prompt engineering, to take advantage of innovations that enable fluid knowledge practices. Widespread adoption of AI to enhance knowledge practices is still some way off.

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User Recommendations ■ Prioritize investments in AI by targeting high-value operational processes where fluid knowledge will deliver the greatest returns; for example, customer service. ■ Determine leadership responsibilities for fluid knowledge by identifying decision makers across enterprise applications, data and analytics, AI, and business management. ■ Identify high-value user communities by analyzing which users are accessing information assets, their frequency and use cases. ■ Benchmark information governance/management maturity by measuring current practices using Gartner’s Information Governance Maturity Model. ■ Evaluate which AI platforms will need to be added to your existing enterprise applications portfolio by assessing semantic indexing, RAG, LLMs, AI classification and natural language queries. ■ Curate and surface knowledge in the context of the employees’ work by embedding AI into enterprise applications to help. ■ Bridge the AI skills deficit by seeking to build specialized skills in AI platforms and applications, and making sure employees are appropriately enabled to capitalize on these tools to improve their work.

Sample Vendors Box; Glean; Google; Microsoft; Salesforce; Squirro Gartner Recommended Reading How Generative AI Impacts Knowledge Management Rethink Enterprise Search to Power AI Assistants and Agents Video: Is There a Difference Between Knowledge and Information? AI Agents for Marketing Analysis By: Nicole Greene, Olive Huang, Lizzy Foo Kune Benefit Rating: Transformational

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Market Penetration: 1% to 5% of target audience

Maturity: Emerging

Definition: AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals based on logic and reasoning. AI agents will help marketing better meet customers where they are, with information that guides decisions based on the unique value that the brand provides. AI agency exists on a spectrum, ranging from current systems that act on user guidance to future systems that learn and perform tasks independently.

Why This Is Important AI agents are revolutionizing marketing by automating tasks, making informed decisions and interacting intelligently with their digital environments, allowing teams to do more with less. AI agents promise operational and martech efficiency and agility, ongoing optimization of omnichannel experiences, driving personalization and purchase, and customer journey enhancement, analyzing behavior and preferences to suggest content and interactions that build trust and increase relevance.

Business Impact AI agents have the potential to:

■ Act independently to sense and address an ongoing goal, eliminating redundant work and allowing marketing teams to focus on more strategic work.

■ Automate competitive research, customer insight development, marketing operations, content creation, workflows, personalization, e-commerce, sales enablement, advertising and more.

■ Interact with B2C brands and B2B business processes on behalf of a customer, automating engagement and negotiation.

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Drivers ■ Commercial impact: AI agents can execute on behalf of business and customers, selling or purchasing products, composing bundles or solutions to meet unique requirements, and supporting customer service. AI agents can learn, plan and execute in such environments with a direct impact on e-commerce purchasing and lead generation.

■ GenAI breakthroughs: Low-code/no-code products are democratizing the space, allowing users with nontechnical skills to develop agents. AI chatbots and virtual agents are also changing customer behaviors, impacting traditional marketing channel strategies across search, websites and more.

■ Vendor hype: Vendors are touting the capabilities of their AI agents. The reality is that most agents can only execute simple tasks and require human oversight. Vendors are working to close the gap that remains until AI agents are able to take independent actions with the risk requirements of most enterprises.

■ Multimodal understanding: The ability to use diverse modalities like vision, audio and language enables more general and flexible AI agents. This allows for automatic changes to workflows that can reduce time to market and democratize content creation. This also supports real-time messaging optimization that can support contextual relevance and dynamic advertising creative.

■ Vibe marketing: AI agents can draft campaigns, test voice and tone, and generate messages without waiting on traditional design and approval cycles.

■ Competitive and customer research: AI agents are increasingly used in real-world analysis, assessment and summarization of information.

■ Composite AI: AI agents are improving due to models that improve planning and problem solving, and reduce hallucinations with rule-based processing. These composite techniques make model outputs more explainable, trustworthy and actionable. Interoperability is increasing as standards, like Model Context Protocol, enable communication between AI applications, AI agents, applications and data sources.

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Obstacles ■ Reliability: Due to the complexity of AI agents, there are various security, data security and governance vulnerabilities. Privacy and ethics concerns are compounded as agents begin to act independently.

■ Lack of trust: Users are unsure whether they can trust AI agents to predict and execute tasks. Agents may take multiple actions in rapid succession with significant impact before a human notices, which may negatively impact the customer experience or the organization’s bottom line.

■ Oversight: Agent sprawl occurs when agents are created without parameters to constrain them from using deceptive means to achieve their goals. Action policies may be opaque and have poor explainability.

■ Pace of change: AI-agent-washing and rebranding existing products with limited enhancements are rampant. This leads to inconsistent expectations of what can be achieved.

■ Cost: Unpredictable runaway costs from API and LLM usage will be a barrier to approval of efforts where value is hard to quantify.

User Recommendations ■ Incorporate AI agents into strategic planning by investing in understanding their capabilities and potential applications across marketing, including data and analytics, content creation, advertising, e-commerce and sales enablement.

■ Be mindful that not every problem is best solved by an AI agent, or even GenAI. Map existing human-led workflows and understand decision-making logic, objectives and the tools used. This forms a framework to determine how an agent can automate or augment workflows.

■ Promote the development and integration of the use of a variety of AI practices, enabling learning, workflow automation and decision-making capabilities.

■ Build a secure, internal simulation environment for testing agentic applications and for controlled, limited pilots that have passed trials to build confidence before deployment.

■ Work with vendors to explore multiagent systems (MAS), capable of operating both collaboratively and independently, supporting decentralized decision making.

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Sample Vendors Amazon Web Services; CrewAI; HubSpot; Microsoft; OneReach.ai; Oracle; Salesforce; Zapier Gartner Recommended Reading Develop an AI Strategic Roadmap for Marketing Generative AI Use-Case Comparison for Marketing Maturity Model for Generative AI in Marketing What Marketing Can Teach the Enterprise About AI Start With Use Cases to Drive Martech Effectiveness

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At the Peak Agentic AI Platform for Multidomain CRM Analysis By: Olive Huang

Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience

Maturity: Adolescent

Definition: Multidomain CRM vendors position an agentic AI framework as part of their application development platform. It includes CRM AI agent templates, a low-code design environment to design AI agents and a testing environment. Unique characters include a foundational model-based reasoning engine, a metadata-driven data fabric, access to CRM functions, workflows and APIs, emerging multiagent orchestration capabilities, and configurations supporting security, privacy and trust.

Why This Is Important For end-user organizations, building AI agents on top of existing CRM workflows and functionalities becomes low-hanging fruit for organizations advancing their agentic AI capabilities. Ninety percent of vendors in the CRM market plan to have GenAI products or GenAI features built into their existing products by the end of 2025. As such, the race of innovation has quickly moved to providing AI agents supporting CRM use cases and agentic-AI platform as part of the CRM application platform.

Business Impact ■ End-user organizations can quickly leverage their existing CRM data, knowledge and workflows to use them in agentic AI use cases to enjoy the early adopter’s competitive advantage while having a managed risk exposure.

■ Multidomain CRM vendors will see AI agents impacting the number of human seat- based licenses consumed. They need to offer agentic AI capabilities within their platform to protect their revenue and create an AI-agent-based monetization model.

Drivers

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■ CRM systems have established customer data, actions, workflows, APIs, and security and privacy controls. Organizations with a mature CRM applications portfolio and a center of excellence (COE) generally have low adoption effort in experimenting with prebuilt use cases and existing data in CRM apps. Although there was a lot of suspicion among CRM practitioners at the beginning, the doubt was soon replaced by a wave of experimentations.

■ Large multidomain CRM vendors race to release new product features around prebuilt AI agents and agentic AI platforms. These solutions typically include prebuilt AI agent templates that support CRM and adjacent operational use cases, a low- code design environment to design AI agents and access to CRM data, workflows, functions and APIs, a testing environment and configurations to manage security, privacy and trust. The list of vendors and products includes Salesforce’s Agentforce, Microsoft’s Copilot Agents and Agent Studio, ServiceNow’s AI Agents, AI Agent Orchestrator and AI Agent Studio, Hubspot’s Breeze Agents, Adobe’s Adobe Experience Platform Agent Orchestrator for Businesses, Creatio’s AI-native CRM and more.

■ Popular use cases promise a paradigm shift of CRM processes and significant productivity gains in marketing data and content operation, lead nurturing automation, sales and service coaching, sales and service chatbots, customer self- service and automation. These support employees to generate proposals, manage orders and work on cases.

■ Consulting firms and systems integrators are also pumping training to their consultants and their customers.

■ We estimate for largest CRM vendors, there will be between 5000-10000 organizations experimenting on their Agentic AI stack within the first six months of releasing the functionality. For midmarket CRM vendors such as Hubspot, the number will be much higher, as prebuilt AI agents are easier to adopt. Gartner estimates that within the next 12 months, there will be some degree of agentic AI for CRM being adopted by the first 20% of current customers of large CRM vendors.

Obstacles ■ Agentic framework, implementation and DevOps toolings, testing automations, resources and skills are still at the emerging phase. Organizations face challenges to manage the output of AI agents to be accurate and reliable. It is less of a challenge for midmarket and SMB organizations as their solutions (e.g., Hubspot) are often out-of-the-box and require minimal agentic AI design work.

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■ Today, data quality is a problem plaguing many organizations from sales, marketing and services to other data siloes. ■ The reasoning engine lacks interpretability and output has an uncertain nature. This will further hinder deploying AI agents to high-stakes processes and in regulated industries. ■ In the long term, the advance of agentic technologies push applications to move from stateless, short-memory, structured data-centric to stateful, long-memory and context, and data of all modality. New challenges will emerge in data management, privacy, security and AI agent orchestration.

User Recommendations Applications leaders supporting CRM should:

■ Recognize that agentic AI is not a cure-all to solve all business problems in CRM. ■ Understand that agentic AI technologies behind these platforms are not mature. The speed of innovation adds complexity of managing applications effectively for the early adopters. ■ Build business cases of significant new CRM implementation or consolidation investment based on a hybrid approach of SaaS, using conventional configuration, customization and integration methods plus agentic AI. ■ Own their decisions in terms of security and privacy. Software vendors have a higher risk tolerance than end-user organizations. ■ Closely monitor the capabilities of model-first and agent-first CRM startups, especially in areas where your current CRM solution is burdened by technical debt, making it difficult to adopt agentic AI from the same vendor. Additionally, pay attention to advanced, industry-specific agentic AI CRM capabilities offered by these startups.

Sample Vendors Adobe; Creatio; Hubspot; Microsoft; Salesforce; ServiceNow

Gartner Recommended Reading How Should You Get Started With Agentic AI for CRM?

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Agentic Analytics Analysis By: David Pidsley, Anirudh Ganeshan, Souparna Palit

Benefit Rating: High

Market Penetration: 5% to 20% of target audience

Maturity: Emerging

Definition: Agentic analytics is the process of data analysis that applies AI agents across the data-to- insight workflow, orchestrating tasks semiautonomously or autonomously toward stated goals that support, augment or automate insights. Agentic analytics represents the evolution of the augmented analytics market by incorporating agentic pipeline automation and generative AI (GenAI) that often uses large language models (LLMs). AI agents help to deliver a generative analytics experience for data analysts.

Why This Is Important Agentic analytics evolves from GenAI-powered self-service experiences, which once augmented data-to-insight workflows. Today, machine execution of automated data analysis is possible without upfront deterministic planning or continuous human input in analytics operations. It will impact the stagnant state of analytics and business intelligence (BI) platform adoption, which hovers at only about 30% of employees. It makes development and consumption of data-driven dashboards, reports and summaries more accessible without intermediates. Outputs become digestible in adaptive forms and immediately actionable via agents for data engineering, analysts and data scientists. As organizations face increasing data variety while needing faster decisions, agentic analytics enables organizational agility at scale.

Business Impact ■ Agentic analytics significantly improves efficiency and productivity by automating routine analytical tasks, reducing reliance on large, centralized data analysis, engineering or reporting teams.

■ Financial services firms will develop holistic customer financial profiles by analyzing behaviors beyond traditional credit scoring. Government agencies will perform real- time monitoring of public safety data, enabling rapid threat response. Healthcare providers will automate complex patient data analysis for personalized care recommendations.

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Drivers ■ The increasing variety of data types — structured, semistructured and unstructured — requires more sophisticated analysis approaches that can handle multimodal inputs and produce multistructured outputs.

■ Current analytics and BI tool adoption remains stubbornly low at 25% to 32% of employees, despite decades of tool improvements, creating demand for more accessible analytical approaches.

■ GenAI accelerates the adoption of data-driven insight generation that simplifies the human orchestration of complex data tasks. It helps to uncover hidden patterns through natural language processing of data sources and metadata and to generate actionable insights at scale. By automating the analysis task planning for vast and varied datasets, agentic AI enables organizations to contextualize analytics content for intelligent applications. This helps bridge the gap between raw data and analytical content outputs like dashboards, reports and data stories.

■ Human-AI delegation preferences are evolving. Many decision makers are currently comfortable with AI’s recommending decisions and, within two years, expect to be comfortable with AI’s making decisions with human approval.

■ Analytics and BI platforms are evolving from the augmented era to the agentic era. The agentic approach shifts from tools that augment the data pipeline to systems that automate data analysis without continuous human input.

■ Emergence of vendor-managed data ecosystems, particularly from cloud hyperscalers, creates integrated environments that support resource-intensive agentic analytics use cases with AI agent and machine learning infrastructure.

■ Social media discussions about using agentic analytics increased by over 500% between May 2024 and December 2024, indicating rapidly growing market interest.

■ Recently, model context protocol (MCP) became an emerging standard to enable two-way communication between AI models and other applications and data sources. It provides a standardized way for applications to share contextual information with LLMs and expose tools and capabilities to AI systems. Nascent standards facilitating communication and collaboration between AI agents are also a driver.

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Obstacles ■ Effectiveness of agentic analytics depends heavily on AI-ready data quality and consistency, requiring organizations to implement robust semantic layers that provide consistent business definitions, metrics, relationships and data lineage.

■ Lack of transparency in how insights and recommendations are generated creates a black box that hinders trust and adoption, particularly prohibitive in regulated industries where compliance risks emerge.

■ Challenges of pricing transparency and flexibility complicate organizations’ ability to predict and budget for agentic AI costs, especially when usage patterns can vary widely based on data queries processed or volume and variety of data analyzed.

■ Organizational resistance to automation of analytical tasks creates cultural obstacles, requiring significant change management and skills development to transition analytics professionals into new roles and responsibilities managing and governing the AI-powered analytics continuum.

User Recommendations ■ Identify high-value, low-effort use cases where agentic analytics solutions and platforms deliver clear business value, and establish AI teaming protocols for human involvement to build quick wins and stakeholder trust. Plan and align D&A architectural choices with enterprisewide agentic AI platform adoption and broader business goals.

■ Implement a robust D&A governance framework, including a semantic layer for data quality and consistency, with clear definitions, metrics, relationships and validation processes.

■ Prioritize trusted vendors offering strong explainability and transparency features such as bias detection, data lineage tracing and detailed insight drill-downs. Assess for greater alignment with hyperscaler cloud data ecosystems.

■ Invest in data and AI literacy programs to prepare users for semiautonomous systems, and adapt to agentic workflows by delegating tasks to AI agents, monitoring outputs and refining data analysis processes through ongoing human- agent collaboration.

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Sample Vendors Aible; Alibaba; Google; Microsoft; Narrative BI; Pyramid Analytics; Qlik; Salesforce (Tableau); Tellius; ThoughtSpot Gartner Recommended Reading Market Guide for Agentic Analytics Augment D&A Workflows With Agentic Analytics Innovation Insight for the AI Agent Platform Landscape A Journey Guide to Transform Self-Service Analytics With GenAI Quick Answer: How Is Generative AI Transforming Self-Service Analytics? Innovation Insight: AI Agents Digital Experience Composition Analysis By: Mike Lowndes Benefit Rating: High Market Penetration: 1% to 5% of target audience Maturity: Emerging Definition: DXC is the “head for headless,” composing digital experiences backed on to API-first content and applications. It can include front-end cloud, visual page builders and templating tools with design system integration. DXC enables developers to manage digital experience UIs at scale and assign them to business users for day-to-day, no-code management. DXC employs API connectivity to headless services such as content management system (CMS), search, personalization and digital commerce.

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Why This Is Important Monolithic “head-on” apps with integrated UIs deliver wide functionality but are slow to update, release, adapt and scale. The shift to “headless” architecture brought agility, velocity and flexibility to front-end development, but forgot about the business user. Digital experience composition (DXC) brings experience orchestration and “what you see is what you get” (WYSIWYG) back to the headless world, providing a stand-alone modular capability for business users and developers to rapidly compose and iterate digital experiences.

Business Impact Fast innovation and adaptation are needed to facilitate digital business ambition. Orchestrating experiences via DXC enables business and technical users to innovate, relying on and retaining the integrity of decoupled underlying apps. Front-end innovations can be released faster due to this decoupling. DXC platforms are not digital experience platforms (DXPs) but are a component of DXP that acts as a point of composition in a no- code/low-code environment. DXC is consolidating with the DXP/CMS markets.

Drivers ■ Until recently, a “headless” or composable approach to DXP required significant development and architectural expertise. This often resulted in business users being unable to manage the digital experience directly in a way they were used to via a monolithic or “head-on” solution.

■ Front-end cloud has commoditized the codebases and runtimes for presentation layers. Its focus is on the front-end developer and DevOps but lacks the business tooling for no-code management for resulting front ends and API integration frameworks.

■ Experience builders (“page builders,” “visual builders”) enable business users to control the layout and composition of web content and functionality from multiple underlying systems via a drag-and-drop or WYSIWYG UI, with the ability to configure component behaviors and presentation.

■ As front-end delivery remains code-heavy, low-code and no-code platforms need to package these capabilities and make them easier to consume. DXC contains a templating engine and/or integration into design systems, providing an integrated development environment for developers to create and orchestrate experiences at scale.

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■ An API integration and orchestration layer between multiple technologies powers the digital experience. This enables the connection and transformation of relevant assets (such as content, products and images) or interactions (such as forms, search, access control and asset/portfolio basket) from their source system to a unified digital experience. API integration can also be used to orchestrate between components. Some vendors differentiate by focusing on this, maintaining productized, prebuilt connectors to leading vendors in core digital experience segments.

Obstacles ■ DXC is not necessary for simple, mainstream “brochureware” needs where packaged, head-on solutions such as web content management (WCM) offer a good fit.

■ DXC can be misused: Avoid considering it from a purely technical or architectural perspective. DXC enables businesses to manage “headless,” API-first experiences with full consideration for business users, but this is not always required.

■ B2B penetration of API-first approaches and DXC has been slower due to the requirement for deeper integration with multiple internal systems and more complex UIs and workflows.

User Recommendations ■ A high level of digital maturity is still required to manage UI decoupling and integration. Tools, design and implementation skills can vary greatly from a monolithic/head-on approach. IT leaders must have the expertise and resources available to implement and manage the new architecture.

■ DXC is unlikely to remain a stand-alone market: It is rapidly converging with and becoming part of composable digital experience tools. Via evolution and M&A, most “headless CMS” and DXPs have brought DXC into their suites. This is only an obstacle to DXC as a stand-alone capability — it is likely to be reduced to a feature but broadly accepted.

Sample Vendors Amplience; Builder.io; Conscia; Contentful; Contentstack; Deity; Magnolia; Netlify; Sanity; Uniform

Gartner Recommended Reading Innovation Insight for Digital Experience Composition

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Digital Sales Rooms Analysis By: Melissa Hilbert

Benefit Rating: Transformational

Market Penetration: 5% to 20% of target audience

Maturity: Adolescent

Definition: Digital sales rooms (DSRs) are a channel designed to increase buyer and seller engagement throughout the customer journey via a privately formed persistent microsite. DSRs use a combination of digital assets, e-commerce and workstream planning capabilities. Customers or prospects interact either asynchronously or live at critical decision points. Revenue teams can provide personalized and relevant insights at various touchpoints to facilitate the customer’s journey.

Why This Is Important Hybrid work is a permanent shift that heavily affects complex B2B buying and selling processes. DSRs support business development and customer retention by providing a collaborative platform where suppliers and buying groups can engage in ongoing collaboration across the buying journey and customer life cycle. This primary interface for synchronous and asynchronous digital interactions with buying teams can strongly improve customer experience (CX) and lifetime value.

Business Impact DSRs provide the following advantages:

■ Improved win rates with tailored and focused buyer-centric collaboration. ■ Accelerated pipeline conversion rates at key stages in a buying process. ■ Improved visibility into the buyer stakeholders’ engagement with content, process and tools. ■ Improved forecast accuracy with improved insight into buyer engagement. ■ Streamlined communication channels to capture vital information and boost decision making.

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Drivers ■ Buyers prefer to engage digitally and want to control how and when they interact with suppliers and sellers.

■ DSRs offer a streamlined platform for digital interaction and collaboration with buyers located globally, thereby ensuring sustained value throughout the customer relationship.

■ DSRs enhance the customer lifetime value by improving the buyer’s experience and consolidating digital channels, simplifying the interaction process with the supplier.

Obstacles ■ Incomplete DSR capabilities and inadequate integration with existing systems — such as sales force automation platforms, digital content management, configure, price and quote (CPQ), interactive demo applications, and digital commerce platforms — can lead to a subpar buyer experience.

■ Limiting DSRs to a single use case undermines their comprehensive value and utility.

■ Tighter budgets require DSRs to prove revenue growth, which may be difficult if the DSR does not have full capabilities.

User Recommendations ■ Compare and evaluate DSR capabilities offered by best-of-breed solutions and those offered by revenue enablement, digital commerce and salesforce automation platform vendors.

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■ Prioritize the following capabilities depending on your organization’s needs: ■ A personalized persistent microsite for the entire life cycle of the customer ■ Bidirectional content sharing for all forms of media types ■ Integrations with CRM platforms, videoconferencing tools and collaboration tools ■ Prebuilt templates offered by vendors and configurable by sellers ■ Call scheduling for buyers ■ Buyer engagement analytics for interactions within the DSR ■ Collaborative mutual action plans for sellers and buyers or customers ■ Integrations with e-signature, digital commerce and CPQ applications ■ Complex deal negotiation for sellers and buyers

■ Establish a business methodology with DSRs to support your B2B customers at customer life cycle inflection points where DSRs are effective, valued or helpful to deliver strategic outcomes.

Sample Vendors Aligned; Allego; ClientPoint; Dock; Flowla; Highspot; Mindtickle; SalesHood; Shopware; trumpet Gartner Recommended Reading Market Guide for Digital Sales Rooms Use Digital Sales Rooms to Improve the Digital Buying Experience Market Guide for Revenue Enablement Platforms AI Agents for Customer Service and Support Analysis By: Pri Rathnayake Benefit Rating: Transformational

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Market Penetration: 1% to 5% of target audience

Maturity: Emerging

Definition: AI agents for customer service and support are autonomous or semiautonomous software entities that use AI techniques to perceive, plan, make decisions, take actions and achieve goals in their digital environments within the customer service and support domain.

Why This Is Important AI agents for customer service and support represent a shift from earlier automation techniques to solutions powered by generative AI (GenAI) that can act with autonomy to fulfill service and support outcomes for customers. As customer service leaders struggle with cost of service, employee turnover, customer experience and operational efficiency challenges, the promise of agentic AI that can manage service processes and take actions with minimal or no human supervision is increasingly attractive.

Business Impact AI agents for customer service and support offer autonomous or semiautonomous execution of customer service and support processes with minimal human intervention. The potential impact includes lessening the supervisory burden, reducing mean time to resolution (MTTR) and improving quality and consistency of customer outcomes. By automating nuanced process flows in complex, dynamic circumstances, AI agents for customer service and support could upend the very nature of customer service, but organizations should exercise caution by starting with a proof-of-value approach.

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Drivers ■ Balancing cost of service and growth: Customer service and support leaders face pressure to reduce cost of service. As customer service request volumes grow, leaders cannot just add headcount, so increased automation of complex interactions becomes a priority. Capabilities of AI agents for customer service and support can support sustained growth while further reducing the labor dependency inherent in that function.

■ GenAI advancements: GenAI-powered AI agents can formulate and execute plans and use tools to achieve goals. They exceed the capabilities of AI assistants that simply respond to direct prompts. These superior capabilities raise the complexity threshold at which human agents must handle customer service requests, which means CSS AI agents can handle a greater proportion of these requests autonomously, and leaders need fewer human agents.

■ No-code agent builders: Customer service and support tech providers are introducing no-code platforms that simplify for business users and administrators the building of AI agents for customer service and support. Customer service and support organizations can empower users to build AI agent co-workers that work alongside subject matter experts and specialists, improving MTTR.

■ Multimodal models: Multimodal AI models trained with audio, video and image data, in addition to language data, broaden the availability of agentic AI capabilities to more customer service interaction channels. They enable deployment of AI agents for customer service and support for the voice channel, which continues to be the channel customers use most often to initiate a service interaction.

■ Retrieval-augmented generation: These techniques ground reasoning, responses and actions of AI agents for customer service and support in organizational knowledge bases, reducing hallucinations and ensuring appropriate guardrails to control the outcomes being delivered.

■ Multiagent systems: The emergence of standards such as the Model Context Protocol (MCP) and Agent2Agent (A2A) protocol enables modular groups of task- specific specialized agents from different vendors to manage complex service workflows.

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Obstacles ■ Data and knowledge management: High-quality, consistent data and knowledge assets are foundational to delivering successful outcomes with AI agents for customer service and support. Immature, outdated knowledge management and siloed, inconsistent data hinder the ability of AI agents for customer service and support to deliver value.

■ Cost uncertainty: Pay-per-use, token pricing and similar usage-based pricing models are the most common for AI agents for customer service and support, posing cost forecasting and budgetary control challenges.

■ Agent washing: Vendors’ rebranding of existing tools as “agents” without true agentic capabilities confuses market evaluation and raises skepticism that agentic AI presents a differentiated value proposition for customer service.

■ Immature technology: Despite vendor claims, truly autonomous AI agents for customer service and support that serve dynamic and complex use cases remain elusive. Current pilot projects and deployments will involve a high level of human oversight until action autonomy and outcome accuracy and effectiveness are proven. Similarly, functions such as agent DevOps and testing are at an early stage.

■ Risk level: Best practices for mitigating risks of using GenAI methods in agents are nascent. Uncertainty around methods related to guardrails, access controls and hallucination mitigation raise the risk and change the cost/risk equation.

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User Recommendations ■ Prioritize knowledge management: Update the state of knowledge management, replacing manual processes with AI-powered knowledge automation tools. Ensure integrations are in place with back-end systems and data repositories that workflows depend on to fulfill customer service and support outcomes. ■ Focus on cost-benefit assessment: Start a proof of value by choosing use cases too complex for conventional no-touch self-service, but not so complex or sensitive that they require a human agent’s handling. ■ Leverage prebuilt AI agents or no-code agent builders: Choose prebuilt AI agents and no-code agent builders from incumbent CCaaS, CRM CEC, or other customer service and support tech providers. Or select new entrants offering no-code AI agent builders. Choose the approach with the best back-end integrations that promote user adoption critical to demonstrating early success. ■ Define agent governance: Define roles for the AI agents with clear boundaries, explainable capabilities and limits. Manage access to no-code agent builders and set build parameters and constraints to prevent agent sprawl. ■ Consider hybrid agent-assistant architectures: In many cases, let conversational AI assistants handle the conversation flow and delegate autonomous tasks to pools of task-focused AI agents via A2A, MCP or APIs. The agents can be developed using agent frameworks while the conversational assistant maintains the conversation flow.

Sample Vendors Amazon Web Services; Kore.ai; Microsoft; PolyAI; Salesforce; ServiceNow; Sierra

Gartner Recommended Reading Ensure Benefits Outweigh Costs of AI Agents in Customer Service and Support

What Is Agentic AI and How Will It Impact Customer Service?

Innovation Insight: AI Agents

When to Use or Not to Use AI Agents

AI Agents for Sales Analysis By: Adnan Zijadic, Dan Gottlieb, Michele Buckley

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Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience

Maturity: Emerging

Definition: AI agents for sales are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take action and achieve goals in their digital environments and are specific to the sales domain.

Why This Is Important AI agents, driven by advancements in large language models (LLMs), can transform sales by autonomously planning and executing tasks, moving beyond traditional AI assistants. For sales organizations, this represents a shift toward proactive AI partners capable of independent work and human-like reasoning to achieve revenue goals. More broadly, AI agents present a key technological alternative to overcome the inherent limitations of solely relying on human capital to drive revenue growth.

Business Impact LLM-based AI agents are elevating enterprise apps, acting as systems working for sales teams.

■ Driven by LLMs, they automate processes, such as but not limited to, lead qualification and configuration, and offer 24/7 support. ■ By executing workflows, they free sales ops for more strategic supervisory tasks, improving efficiency and competitiveness. This lets sales teams focus their efforts on more relationship-building tasks and deal negotiation, for example.

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Drivers ■ Scaling sellers and retention: Sales organizations inquire with Gartner to augment or automate sales roles with AI, aiming to lower cost of sales and boost velocity across channels. This reflects significant human capital challenges, including the high cost and difficulty of effectively training, retaining and coaching staff. AI agents offer potential growth by reducing resource dependency throughout the sales organization and performing work autonomously — impacting roles such as representatives, managers and enablement — while augmenting skills. Gartner anticipates that these drives for efficiency and effectiveness will fuel significant future AI agent investments across the sales process.

■ Evolution from assistants to agents: LLM-based agents surpass traditional and LLM-based AI assistants through their ability to formulate and execute plans rather than merely responding to direct prompts, enabling true sales process execution and automation.

■ Architectural advancements: Current agent architectures enable orchestration of interactions between input interfaces, language models, memory systems and external tools (like CRMs), creating integrated systems capable of executing simple and more complex sales workflows.

■ Retrieval-augmented generation (RAG): RAG techniques ground agent responses in organizational knowledge bases, ensuring recommendations align with company sales methodologies and product information, for example.

■ No-code agent builders: No-code platform tools are emerging from incumbent CRM providers to democratize the agent-building experience for citizen developers or business users. This shift is imperative for sales organizations, as it empowers users to create AI agents without the need for extensive developer skills.

■ Multiagent ecosystems: The introduction of multiagent modularity, where specialized agents handle different sales tasks — from lead generation to contract management — with clear roles and handoff protocols, is expected to be an aspirational end state for many sales organizations.

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Obstacles ■ Data quality: As with all AI, poor or incomplete foundational data severely limits AI agent usefulness and outcomes.

■ Sales ops maturity: Immature operations are a major barrier. Organizations struggling with basic, undocumented processes (like quoting/pricing) or lacking internal tech expertise/center of excellence cannot effectively leapfrog to advanced agent implementations. Foundational maturity is a prerequisite.

■ Implementation maturity: Agentic technology remains unproven at scale in production environments, creating significant risk for early adopters.

■ Trust and validation: Difficulty establishing confidence in agent behavior, critical in sales due to direct customer/revenue impact, is a primary deployment blocker. This requires cultural shifts to overcome seller intimidation.

■ Agent washing: Vendors rebranding existing tools as “agents” without true agency capabilities, confuses market evaluation.

■ Security and identity: Robust identity controls and guardrails are essential for agents accessing sensitive data to prevent leaks or unauthorized actions.

■ Cost management: Token usage costs can escalate rapidly, especially with multi- agent systems, posing significant budgetary challenges for scaled deployments.

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User Recommendations ■ Improve ops and skills: Enhance operational maturity and cultivate new internal skills focused on integrating diverse technology stacks and adopting a systems- thinking mindset for managing interconnected processes effectively. ■ Start by being focused: Begin deployment with well-defined, lower-risk use cases (e.g., lead qualification and nurture, and prospect research) before advancing to more complex automation scenarios. ■ Define clear agent roles: Create specialized roles for AI agents, with explicit boundaries and manageable capabilities that sales teams can confidently utilize. ■ Establish comprehensive guardrails: Implement validation on both prompts and agent responses to ensure sales information remains accurate and appropriate for customer interactions. ■ Leverage existing infrastructure: Implement AI agents using your current sales force automation (SFA) and no-code/low-code platforms to maximize existing investments and facilitate easier adoption.

Sample Vendors BUSINESSNEXT; HubSpot; Microsoft; Qualified; Regie.ai; Reply; Salesforce; SAP; SDRx by Klenty; Zoho Gartner Recommended Reading Innovation Guide for Generative AI in Sales Innovation Insight: AI SDR Agents for Inbound and Outbound Sales Communication 13 Generative AI Use Cases for B2B Sales 19 Artificial Intelligence Use Cases for B2B Sales Innovation Insight: No-Code Agent Builders Data Clean Rooms Analysis By: Eric Schmitt Benefit Rating: High

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Market Penetration: 5% to 20% of target audience

Maturity: Emerging

Definition: A data clean room is a secure, isolated platform that links anonymized marketing and advertising data from multiple parties. For advertisers, data clean rooms are distinguished from other data-sharing methods by the inclusion of detailed advertising impression data, with privacy-safe restrictions on outputting user-level results.

Why This Is Important Data clean rooms offer a privacy upgrade to advertising capabilities by allowing marketers to rebalance privacy with new data access and utilization policies that are fit for a consent-driven advertising world.

Business Impact At scale, the opportunity to access ad impression and interaction data presents digital marketing leaders with the potential to get more from advertising spend via analytic and operational applications. Examples include data governance, audience segmentation for advertising activation, campaign measurement, data enrichment and time-series analysis.

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Drivers ■ Data deprecation: The shift to consent-based data usage and the associated limits on the use of cookie and device ID data create an opportunity for new approaches to data-driven advertising and measurement.

■ Data appending and enrichment: Clean rooms can enrich first-party customer and prospect profiles with additional data attributes or elements. These attributes may be sourced from the clean-room provider or third-party data sources, or developed/derived via statistical modeling and processing.

■ Execution: Clean rooms offer a pathway for addressable and look-alike targeting in the data-rich retailer and publisher environments, without the need for those parties to expose any data.

■ Reach/frequency: On an anonymized basis, clean rooms can measure how many and which people were contacted, and how many times over the duration of the campaign. This sets the stage for target audience penetration analysis, unique unduplicated reach, and refined campaign activation.

■ Campaign performance: Clean rooms enable marketers to link ad impressions data to sales and engagement data. This can feed campaign performance analysis and attribution, and measurement reports and dashboards. Clean rooms can also inform media planning, financial and management reporting, and business forecasting.

■ Projections and forecasts: The ability to stack data for ongoing, time-series analysis has strong appeal for many marketing leaders. This can create data views for use in advertising and sales projections and forecasts.

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Obstacles ■ Privacy risks: As with any sensitive data processing, breaches or circumvention of privacy controls could result in reidentification, and therefore liability.

■ Disruption distractions: Although privacy-related data gaps may boost clean rooms in the long run, in the near term, many advertisers are likely to focus on tactical operational adjustments to media plans, and not speculative and resource-intensive data infrastructure investments.

■ Regulatory uncertainty: Key enforcement questions related to U.S. state laws remain unresolved, even as a patchwork of new regulations moves through legislatures. Firms may be hesitant to make bets on privacy-sensitive data processing without more governance clarity.

■ Scarce talent: The pool of big data processing and analytics talent available to advertisers is limited. Talent shortages combined with procurement inexperience can add costs and delays.

■ Cost: Complex database investments like clean rooms incur substantial technology costs.

User Recommendations ■ Define clean-room goals. The menu includes reach/frequency measurement, campaign performance analysis, projections and forecasts, governance, and campaign segmentation.

■ Explore the landscape of clean-room providers. Brands with heavy paid media investments in Google or Amazon should start by looking at these vendors’ clean- room offerings. Independent and agency solutions are a better fit for cross-media applications.

■ Embrace agile principles during clean-room development. Staging, ingesting and linking data — especially from new, high-volume sources — often takes longer than planned and requires multiple iterations.

■ Apply good governance and privacy by design. Despite technical safeguards such as aggregate reporting outputs with minimum cell sizes, risks like reidentification exist. Mitigate risks with documented use cases and detailed data flow diagrams that cover processing and storage, and by building up internal data skills like SQL and BigQuery.

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Sample Vendors Acxiom; Amazon; AppsFlyer; Decentriq; Dentsu (Merkle); Epsilon; Google; LiveRamp; Snowflake; WPP (InfoSum) Gartner Recommended Reading Navigate the Marketing Data Collaboration Landscape Don’t Let Cookies’ Reprieve Stop Your Ad Strategy Evolution The CMO’s Key to First-Party Data’s Triple Lock Quick Answer: Estimate the Costs of a First-Party Data Strategy 5 Steps for CMOs Exploring First-Party Data for Advertising and Targeting Machine Customers Analysis By: Don Scheibenreif Benefit Rating: High Market Penetration: 1% to 5% of target audience Maturity: Emerging Definition: Machine customers are nonhuman economic actors that obtain goods or services in exchange for payment. Examples of machine customers include virtual personal assistants, smart appliances, connected cars and Internet of Things (IoT)-enabled factory equipment. Machine customers act on behalf of a human customer or an organization. Why This Is Important Gartner estimates 3 billion B2B internet-connected machines can act as customers today, growing to 8 billion by 2030. These machine customers include virtual assistants and AI agents with varying autonomy. B2C devices and AI assistants will also reach billions. Machines are increasingly capable of buying, selling and requesting services. Moreover, machine customers are evolving from simple informers to advisors and decision-makers.

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Business Impact Over time, trillions of dollars are expected to be in control of nonhuman customers. This will result in new opportunities for revenue, efficiencies and managing customer relationships. Leaders seeking new growth must reimagine their operating and business models to take advantage of this emerging market of tens of billions of machine customers. Organizations that miss this opportunity will be marginalized, just like those retailers who missed the digital commerce wave.

Drivers ■ In the next few years, machine customers are expected to become significant players in industrial manufacturing, retail, and consumer industries.

■ Billions of connected products, powered by advanced technologies, will soon behave as customers, autonomously shopping for services and supplies for themselves and their owners.

■ Twenty-nine percent of CEOs are developing strategies to engage with machine customers and AI agents, with half expected to have a strategy within two years, according to the 2025 Gartner CEO and Senior Business Executive Survey.

■ Currently, machines inform, recommend and perform routine tasks, but are evolving into complex customers. For example, Amazon’s Dash Replenishment Service, integrated with Alexa, supports various consumer and inventory management devices, HP Instant Ink allows printers to order ink automatically and Tesla cars order spare parts.

■ Advances in generative AI and agentic AI, such as Amazon’s Alexa+, Google’s “Ask for Me” and OpenAI’s ChatGPT, are accelerating machine customer development. These technologies handle inquiries 24/7, offer quality responses and aid human agents. In B2B, AI-based contract negotiation systems like Pactum AI generate fair contracts, as used by Walmart for supplier negotiations.

■ Machine customers offer new revenue opportunities, boost productivity, enhance health and security, and benefit both selling and buying organizations.

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Obstacles ■ Operating model changes: Marketing, selling and serving machine customers will disrupt operating models. New definitions of customer experience and data access for machines are essential. Companies must scale operations to serve machine customers and respond in real time or risk losing them.

■ Lack of trust: Humans may distrust machine customer technology for privacy and accuracy. Conversely, machines may distrust suppliers.

■ Fear of machines: Some fear delegating purchasing to machines. Customers and organizations should assess governance for ethical, legal, fraud and risk standards in the machine customer world.

■ Security and governance: Increased AI usage may lack security, leading to misinformation and damaging reputations.

■ Cost: It involves complex implementations and maintenance. Adaptability to changing preferences requires significant investment in technology, software and support.

User Recommendations ■ Identify use cases where your products and services can be extended to machine customers. Initiate collaboration with your chief digital officer, chief data officer, chief strategy officer, sales leaders and chief customer officer to explore the business potential of machine customers.

■ Assess B2B customers’ tech purchase intent data to identify the machine customer capabilities and use cases customers are deploying.

■ Pilot the ideas to understand the technologies, processes and skills required to implement machine customers adequately.

■ Build your organization’s capabilities around digital commerce and AI— first generative AI and now agentic AI —, for the next few years. Use APIs and enterprise bots to enable machine customers for low-complexity transactions, such as autoreplenishment. Then, extend your capabilities for more complex purchases.

■ Follow examples from organizations such as Amazon, Google, HP Inc., iProd, NTT DATA, OpenAI and Tesla for evidence of capabilities and business-model impact.

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Sample Vendors Amazon; Anthropic; Google; HP Inc.; iProd; NTT DATA; OpenAI; Pactum; Perplexity; Tesla

Gartner Recommended Reading Top Strategic Technology Trends for 2024: Machine Customers

Adapt Machine Customer Sales Engagements or Risk Revenue Loss

Presentation Slides: 50 Emerging Examples of Machine Customers

Customer Experience Analysis By: Michael Chiu

Benefit Rating: High

Market Penetration: 5% to 20% of target audience

Maturity: Adolescent

Definition: Customer experience (CX) is the customer’s perceptions and related feelings caused by the one-off and cumulative effect of interactions with a company’s employees, systems, channels or products. It links to CX management: the discipline of understanding customers and deploying strategic plans that enable cross-functional efforts and customer-centric culture to improve satisfaction, loyalty and advocacy.

Why This Is Important CX is a top 10 priority for most at the executive table, and viewed as vital to helping the organization achieve business outcomes. As customer expectations have reshaped themselves faster than organizations’ abilities to respond, organizations are realizing that outside-in thinking — beginning with the customer’s needs — is required to weather continuous disruption. What started in the early 2000s as a rarity in a handful of B2C industries has gradually spread across many more industries.

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Business Impact The 2024 Gartner Customer Experience survey showed a relationship between CX performance and revenue growth: 92% of CX leaders reported that their company’s revenue increased from FY23 to FY24, versus 80% of mainstream companies and 50% of “trailers.”

However, CX maturity remains low. Of organizations completing Gartner’s CX Maturity diagnostic, 71% placed in Level 1 or 2 (43% and 28%, respectively) out of a max score of 5, indicative of significant opportunities to improve (data from September 2020 to December 2024).

Drivers Four primary trends have driven ongoing interest in CX initiatives:

■ Fewer forms of differentiation exist on the basis of product or service excellence and operational efficiency, which is driving a greater focus by senior executives on CX as a means of differentiation.

■ Greater customer access to information is resulting in a power shift that favors customers over suppliers. The multitude of channels by which customers can have (and share) great and poor CX compounds a rising customer willingness to switch providers.

■ There is more proof that CX delivers, with case studies and documented evidence of businesses delivering excellent CXs through demonstrable business benefits. For example, the American Customer Satisfaction Index (ACSI) has identified a strong correlation between customer satisfaction and stock performance.

■ Rapid technology advances, primarily in generative AI (GenAI) and, more recently, agentic AI, have emerged and will empower customers as well as organizations’ CX capabilities.

CX is just past the peak on this Hype Cycle:

■ While some organizations have had a CX leader for more than 20 years, the majority of organizations are still at lower levels of CX maturity. This is despite consistent investments in customer engagement technologies such as like CRM, voice of the customer (VoC) and customer analytics, and more recently GenAI and Agentic AI

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■ For those at Level 2 maturity, it will likely take two to five years to get to Level 3 (intermediate) maturity. It may take five or more years for organizations at the lowest level of maturity (Level 1) to reach Level 3.

■ Adoption of, and focus on, CX should continue to accelerate given the consistent CEO focus on customer-centric strategies to handle the current climate of volatility, uncertainty, complexity and ambiguity (VUCA) and the rise of digital business.

Obstacles ■ Many organizations struggle to understand their customers, set a CX strategy and drive a customer-centric culture.

■ Organizations with lower maturity often lack an understanding of what customers want and need, as well as the ability to listen to the voice of the customer. As the organization matures, the focus shifts to faster operational decision making in response to this feedback.

■ Organizations of low CX maturity tend to not have formal organization and structure around CX (like a CX leader or team), a single CX strategy and a coordinated approach to customer journey mapping. They may also struggle with prioritizing CX projects and technologies to deliver on the CX strategy. Commitment to CX and budget may be lacking as well.

■ Most organizations struggle to break down functional silos to coordinate how they deliver improved experiences. It is challenging to drive an overall culture of CX into processes and decisions further removed from day-to-day customer interactions.

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User Recommendations ■ Remove employee barriers preventing an improved CX. The 2024 Gartner Customer Experience Survey found that surveyed organizations with an Executive Leadership Team (ELT) that removes employee barriers were 8.5 times more likely to agree that their CX program meets customer needs compared to organizations who do not say this — the single strongest driver by a wide margin.

■ Improve coordination by creating a CX strategic plan that specifies the target markets, customer personas and key customer journeys that your organization will and will not focus on.

■ Generate KPIs related to customer satisfaction, loyalty, advocacy and customer confidence. Consider the Customer Effort Score (CES), customer satisfaction (CSAT), and Net Promoter Score (NPS).

■ Produce customer journey maps to diagnose your most critical customer journeys and where they are in need of improvement.

■ Establish fusion teams to develop the roadmaps, project portfolios and product backlogs to execute CX initiatives.

Gartner Recommended Reading Use Gartner’s Maturity Model to Evolve Your Customer Experience

Adopt the Strategies of CX Leaders and Achieve Growth

Key Insights From Gartner’s Customer Experience Maturity Diagnostic

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Sliding into the Trough Digital Experience Analytics Analysis By: Adrian Lee

Benefit Rating: Moderate

Market Penetration: 5% to 20% of target audience

Maturity: Adolescent

Definition: Digital experience analytics (DXA) are applications that provide diagnostic insights into visitor activity and customer experience on responsive web and mobile apps. The tools provide advanced analytics with machine learning (ML), prescriptive recommendations enabled by generative AI (GenAI) technology innovations, session replay and heat- mapping technologies.

Why This Is Important DXA captures user behaviors from both client-side and server-side event data collection. Data captured includes user inputs such as keystrokes, mouse clicks in form fields, page scrolls and mobile gestures. Innovations include analyses of interactions in chatbots and virtual assistants for sentiment analysis. DXA solutions are converging with web and product analytics tools and provide diagnostic insights for improving the digital experience, leveraging session replays tied to digital journeys.

Business Impact Digital experience analytics tools complement web and product analytics for customers including:

■ Conversion rate improvement in retention and upsell scenarios for growth and success teams ■ Faster identification and resolution of web and native mobile app technical issues ■ Better customer service delivery by human support agents ■ Optimized campaigns in digital customer journeys by marketing teams ■ Quantitative data to guide design experimentation and optimization in product management

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■ Sentiment analysis and user intent from conversational insights

Drivers ■ Digital experiences are increasing in importance as a mode of customer acquisition, retention and engagement for organizations to harness the benefits of agentic AI and GenAI technologies.

■ Companies that used to analyze their users through the lens of point-in-time sessions seek more contextual capabilities in understanding multichannel and multisession user paths for better behavioral segmentation.

■ Companies are shifting toward account-based rather than isolated sessions to pivot and develop new business models and to create more personalized digital experiences for customers.

■ There is increased democratization by economic buyers and end users (such as business unit [BU] leaders and nontechnical staff) to analyze huge volumes of session data, uncover behavioral patterns and provide faster insights to businesses about the causes of customer experience problems. This was previously a capability constrained by IT and business analysts resource availability.

■ Convergence between product analytics and digital experience analytics is being driven by increasing sophistication and rapid AI innovations. Companies that may have traditionally used session replay and other diagnostic capabilities through observability and monitoring tools are looking for capabilities that allow them to understand digital experiences in a more commercial or strategic context.

■ Incumbent DXA vendors are repositioning, as an impact of the vendor AI race, in broader terms and converging and overlapping with product analytics offerings — while being challenged by vendors from conversational analytics and application performance management.

Obstacles ■ The DXA market is both consolidating and expanding into other categories, including product analytics, digital adoption, experimentation, voice of the customer (VoC), and back-end observability and monitoring.

■ DXA vendors are constantly expanding capabilities or shifting into adjacent markets through mergers or acquisitions, such as customer journey analytics or product analytics, that might present challenges to the way buyers select vendors.

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■ Usability and technical depth of digital experience analytics tools require careful balancing, as the addressable user base continues to expand from data scientists and analysts to less technical users. Many of the available DXA tools still present a relatively steep learning curve for business users.

■ DXA vendors embedding GenAI-enabled features on high release cadence have inadvertently introduced more buyer concerns on accuracy, performance and validity of insights gained through use of commercial LLMs for generating prescriptive insights and use recommendations.

■ Data privacy and ethical concerns constrain the ungoverned use of DXA, despite guardrails built in through anonymization and other privacy-enhancing features.

User Recommendations ■ Evaluate DXA tools as a part of a unified digital analytics strategy for tracking, measuring and improving the customer experience across the full life cycle of the digital customer journey. In addition, evaluate these tools across devices and across interactions with the organization’s digital touchpoints.

■ Review the current analytics stack to streamline the selection of analytics tools (DXA, web, product) to be used in combination to address the effectiveness and efficiency for functional teams as a setup for longer-term scaling toward organizationwide use.

■ Ensure that greater business value is realized by embedding the analytics practice into different teams’ workflows so that they engage over time. This will help to mitigate underutilization and encourage value realization from the selected DXA tool.

■ Conduct rigorous due diligence on DXA vendors’ security and privacy terms and policies. Evaluate each web page to determine which page is appropriate for session replay data capture. Provide ongoing reviews of the strength and accuracy of the redaction policies in place.

Sample Vendors Acoustic; Amplitude; Contentsquare; Fullstory; Glassbox; LogRocket; Medallia; Quantum Metric; SimplicityDX

Gartner Recommended Reading Emerging Tech Impact Radar: Customer Experience Analytics

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Use CX Analytics to Discover Insights That Drive Product Growth

Market Guide for Product Analytics

Customer Data Ethics Analysis By: Andrew Frank

Benefit Rating: High

Market Penetration: 1% to 5% of target audience

Maturity: Adolescent

Definition: Customer data ethics describes corporate policies that align business practices with fundamental principles that reflect a company’s avowed values, as distinguished from a reliance on legal compliance to set boundaries on customer data usage. Customer data ethics seeks a consistent foundation for decision making to counteract the paralyzing effects of rapidly shifting and fragmenting data privacy standards.

Why This Is Important As marketers race to adopt data-driven AI solutions for personalization, journey orchestration, advertising and customer analytics, progress on the foundations of customer data privacy has stalled. Fragmentation of laws and platform policies puts the onus on organizations to confront complex trade-offs in a rapidly changing technical environment where brand and legal risks are high.

Business Impact Personalization is one of the top value propositions for AI in marketing. The 2025 Gartner Marketing Personalization Survey shows personalization delivers significant commercial benefits, but only 4% of CIOs and tech leaders say their data is AI ready. Recent reversals in the trend toward tightening privacy restrictions are leading some organizations to deprioritize ethical data constraints, potentially accelerating AI marketing initiatives while also raising risks of legal hazards and consumer backlash.

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Drivers ■ Growing regulatory complexity and fragmentation. As privacy laws and data protection policies fragment across regions, organizations face challenges maintaining consistent standards for customer data ethics. This creates uncertainty and risks, especially for organizations operating globally. Lack of standards pushes organizations to adopt their own that can be consistently applied, regardless of local variations.

■ Evolving and conflicting consumer expectations. Public awareness of data rights and misuse has raised consumer expectations for companies to go beyond legal compliance to provide transparency and accountability in data practices. Failure to meet these expectations can damage reputations even when technically compliant. Meanwhile, consumer expectations of personalized relationships between customers and brands are also mounting.

■ Technological advancements outpacing regulation. Rapid technological progress, especially in AI, is outstripping existing legal frameworks and creating ethical gray areas where the law is silent or ambiguous. This forces organizations to proactively address ethical implications of emerging data uses rather than rely on regulations.

■ Investments in first-party data (FPD). When Apple, then Google, announced intentions to deprecate third-party cookies, organizations responded by prioritizing collection and use of FPD to fill gaps in ad targeting and measurement. Data governance became a complex cross-functional issue as organizations struggled to monetize FPD investments with AI, creating a gap in stewardship of FPD used in unanticipated ways.

■ Inconsistent platform policies. Rapid shifts in vendor policies are creating confusion and unpredictability around customer data. Google’s reversals on cookie policies in Chrome, in particular, upended many assumptions companies used to build operational capabilities. To reduce exposure to future disruptions, companies need to codify ethical policies that can adapt to volatile technology standards.

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Obstacles ■ Lack of skills. Data and AI ethics disciplines lack educational pipelines and career paths to satisfy a rapidly growing need. Consulting providers are scarce.

■ Distributed accountability. Hazards arise from the data’s collective use among agencies, vendors and affiliates. Lines of responsibility are blurry and systemic effects opaque. Vendor compliance validation and monitoring is lacking.

■ Governance and cost. Business units seek to keep indirect costs and vague accountabilities off their balance sheets. The potential cost of losing market share to less ethical competitors may also impede adoption, especially when economic pressure is rising.

■ Discord on fairness. Laws and norms of fairness vary among communities and within organizations, hampering attempts to encode principles in algorithms and practices.

■ Contextual ambiguities. Privacy tends to hide customer data, but this data is often needed to detect biases and misfires.

User Recommendations ■ Put customer data ethics at the core of AI adoption. Ensure AI projects that use customer data have ethical governance and workstreams attached. Keep in mind that AI agents have no ethical sensibilities.

■ Go beyond compliance. Share customer data ethics principles with customers, employees and other stakeholders in outbound communications. The public should know your priorities.

■ Tune out the noise. As geopolitical discord and technology volatility escalate, avoid losing focus tracking impactful news items. A sound ethical policy needs to be grounded in principles that are resilient against disruptive events.

■ Focus on the customer view. Craft transparent policies that align with customer expectations. Ask if data practices would disturb people if they knew all about them.

■ Cultivate alliances. Seek like-minded change agents in IT, data and analytics, legal and throughout your organization. Find opportunities to embed ethical initiatives in funded projects.

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Gartner Recommended Reading Top Marketing Trends for 2024: AI for Privacy, Personalization and Influence

Avoid Ethical Pitfalls Caused by Unscrupulous Digital Commerce Practices

Immersive Commerce Analysis By: Marty Resnick

Benefit Rating: High

Market Penetration: 5% to 20% of target audience

Maturity: Emerging

Definition: Immersive commerce blends the physical and digital worlds by using advanced metaverse-related technologies, such as spatial computing, augmented reality (AR) and virtual reality (VR), to enhance the customer experience. The goal of immersive commerce is improving product understanding, easing the purchase process and forming brand loyalty by building emotional engagement.

Why This Is Important Immersive commerce visualizations and interactions improve prospective customers’ ability to self-serve, rather than conduct lengthy sales calls and demos as part of a purchase decision. VR can provide an interactive 3D representation of complex products and contextualized environments. AR is used to show the look and feel of a product to the customer or in a physical environment. Spatial computing enables contextual hyperpersonalized shopping and information.

Business Impact Industries such as retail, consumer packaged goods, travel, automobile and real estate have been early adopters. VR can enhance the decision-making process by showing how the final product/destination looks to customers before they invest. AR use cases are more readily available through mobile phones and enable visualization of products in the customer’s physical environment. Spatial computing enhances location-based commerce by enabling unified personalized experiences across physical and digital channels. When integrated into digital commerce platforms, immersive technologies can be powerful in engaging and converting customers, and driving revenue.

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Drivers ■ Digital audiences are embracing new ways to conduct activities, previously served by a commerce storefront.

■ For complex products that require configuration, customers are able to view products in 2D and 3D by using configurators. Simple 360-degree videos provide compelling emotional experiences of products.

■ Evolving shopping and purchasing behaviors, both B2B and B2C, are driving the adoption of immersive commerce as part of an overall commerce strategy. ■ Virtual economies, if developed along with aspects of Web 3.0, could bring new paradigms of transactions, such as NFTs, to the mainstream. ■ The emergent metaverse is offering new opportunities for immersive commerce, and may bring down the cost of participation in VR/AR environments. ■ New devices (e.g., Apple Vision Pro and Meta Quest 3) are providing novel opportunities for location-based commerce transactions.

Obstacles ■ Immersive content is expensive to create and, typically, will be provided by vendors, not by in-house capabilities. ■ The costs of content creation and overall immersive experience remain high.

■ Accessibility challenges may be prevalent in immersive experiences.

■ The heterogeneity of consumer devices and acceptance of these new types of transactions may hinder adoption. ■ Overall adoption of head-mounted displays and other devices specific for immersive experiences has not reached expected levels.

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Gartner

User Recommendations

■ Treat spatial computing, AR and VR as different technologies that solve different problems.

■ Integrate metaverse technologies with the digital commerce ecosystem to leverage such assets as product catalogs, visual search, web content management, promotions and personalization, and to configure, price and quote.

■ Follow emergent and advanced metaverse solutions to track new opportunities in immersive commerce.

■ Take advantage of the upcoming spatial computing glasses market (i.e. Apple Vision Pro and Meta Quest 3), which will provide new opportunities and experiences for immersive commerce.

Gartner Recommended Reading

Gartner Futures Lab Podcast: Convergence of AI and Spatial Computing Will Revolutionize Human-Computer Interaction

Emerging Tech: Revenue Opportunity Projection of Spatial Computing

Emerging Tech Impact Radar: Immersive Experiences

GenAI Virtual Assistants

Analysis By: Danielle Casey, Bern Elliot

Benefit Rating: High

Market Penetration: 20% to 50% of target audience

GenAI virtual assistants (VAs) represent a new generation of VAs that leverage LLMs to deliver functionality not obtained with traditional conversational AI technology. GenAI has advanced Q&A support, supported generation features and modalities, and improved task automation and value outcomes.

Maturity: Early mainstream

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Why This Is Important LLMs have materially augmented VAs, with virtually all VA providers having already productized GenAI capabilities. LLMs and multimodal models can be embedded into a VA or can be accessed via an API call. There are multiple approaches to using an LLM — deployed out-of-the box, fine-tuned or retrained for customization, chained with additional LLMs across various tasks, or contextualized via prompt engineering. Each option must be evaluated against performance requirements, time and costs.

Business Impact GenAI-enabled VAs:

■ Operate with improved performance in conversational dialogue, enabling greater contextuality, reducing development effort, improved self-service and reduced operational support costs.

■ Extend automation by expanding from Q&A support to content discovery and creation features.

■ Improve user interactions and engagement by supporting multimodal interactions (beyond text and voice; for example, via images and videos).

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Drivers ■ Demand for GenAI value: GenAI-enabled VAs are a popular use case, as the use cases and KPIs of VA implementations are well-defined and measurable, and the need to improve customer or employee support may already be recognized. This makes VAs a good option for organizations piloting GenAI.

■ GenAI-enabled use cases: Organizations investing in GenAI VAs are looking for promised value outcomes, such as productivity improvement and cost reduction. Some of the associated use cases are Q&A support for customers, information discovery and generation features for employees and intelligent agent assist for call center operators. GenAI VAs are also becoming more specialized and offering deeper automation by use case, business role or industry domain.

■ Rapid time to market: GenAI VA offerings are focused on lowering the barrier to adoption via easy-to-use platforms where users can select their preferred LLMs, prepackaged use case integrations, etc. These platforms often lower the technical skills barrier by supporting the use of natural language prompts to create GenAI VA flows.

■ AI agent hype: The emergence of AI agents has reengaged interest with GenAI VAs, as many providers adjust their marketing to include “agent” terminology. Moreover, domain specialized models, large action models and multimodal architectures are advancing GenAI VAs to tackle more complex tasks and use cases. Most VA providers are working on advancing their offerings to support agentic automation, though adoption of AI agents is emerging.

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Obstacles ■ Explainability: Explainable GenAI outputs are key to addressing accuracy and reliability concerns. Providers are working to embed outcome explainability and performance reliability — often via combining GenAI with rule-based tech — but this remains an unsolved problem.

■ Customization requirements: To unlock value, GenAI VAs must be connected to customer data via prompt engineering, retrieval-augmented generation, model fine- tuning or other techniques, requiring customer education, budget, skills and data readiness.

■ Solution performance: Many GenAI VAs struggle to deliver strong value outcomes using general purpose LLMs. Addressing this challenge requires significant model or data customization, such as fine-tuning LMs for specific domains or use cases. Domain-specialized models will also help here, but remain scarce.

■ Privacy and security: Privacy and security concerns may necessitate stricter model hosting and data integration requirements.

■ ROI: Tokenization and generative inferences are changing VAs’ cost structure. Difficulty in proving ROI may slow adoption.

User Recommendations ■ Define and prioritize use cases. GenAI VAs offer low-risk, measurable use cases rendering it ideal for piloting and scaling GenAI within the organization.

■ Consider solutions that offer hybrid options — both LLM and rule-based flows — for better cost-effectiveness, responsiveness, explainability and robustness.

■ Measure implementation success by clearly identifying the business case and supported value outcomes. Regularly assess performance to identify model accuracy issues or business misalignment.

■ Improve GenAI VA performance and drive deeper automation by customizing your VAs using model fine-tuning, advanced RAG techniques or process-engineered workflow automation. This will improve VA knowledge support for particular use cases, business roles or vertical industries.

■ Prepare for agentic automation. Start with GenAI VAs before progressing to agentic automation and the technology, business and change management challenges this emerging tech introduces.

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Sample Vendors Aisera; Amelia; Google; IBM; Kore.ai; Leena AI; OneReach.ai; Openstream.ai; OrbyAI; Salesforce

Gartner Recommended Reading Emerging Tech: Market Risk Projection of Generative AI on Conversational AI

Market Guide for Conversational AI Solutions

Emerging Tech: Conversational AI Differentiation in the Era of Generative AI

Generative AI for Marketing Analysis By: Nicole Greene, Chad Storlie

Benefit Rating: Transformational

Market Penetration: More than 50% of target audience

Maturity: Emerging

Definition: Generative artificial intelligence (GenAI) technologies generate new, derived versions of content, data, designs and methods by learning from large repositories of original source content. GenAI has profound business impacts, including on content discovery, creation, authenticity and regulations; automation of human work; and customer and employee experiences.

Why This Is Important ■ GenAI adoption continues to accelerate, and marketers must navigate the hype while preserving the enthusiasm over its potential to create value. ■ GenAI supports marketing use cases that demand efficiency and scale, including content development, personalization, advertising optimization, social media engagement and customer experience enhancement. ■ Early success is supported by rapid development of GenAI applications and features from hyperscalers, martech vendors and point solutions.

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Business Impact GenAI supports the creation of content, data and experiences to influence user choices at scale. Many pilots and implementations are low-risk and easy to evaluate, like translation, augmented writing and analytics accelerators. With the rapid progress of productivity tools and AI governance, CMOs will shift focus toward differentiated and customer-facing use cases. Responsible AI and security will be necessary for the safe implementation of GenAI, as CMOs bridge the AI trust gap with consumers.

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Drivers ■ GenAI has had a proven impact on marketing productivity and creativity. According to the 2025 Gartner CMO Spend Survey, CMOs want GenAI to deliver improved time and cost efficiency, and productivity.

■ GenAI is a top competitive area among major technology vendors. They compete on foundation model offerings, enterprise readiness, usability, pricing, infrastructure, safety and indemnification.

■ New foundation models in new versions, sizes and capabilities are rapidly coming to market, making GenAI applicable for more marketing use cases. Tools include improved model robustness, such as vector databases, graph technologies, large language model (LLM) testing, security protection and open-source resources.

■ Multimodal models, such as Google Gemini or OpenAI’s GPT4 Video, are rapidly improving. They are trained to take in both images and text. For example, they allow users to ask questions about images and receive answers via text. Models can combine concepts, attributes and styles to create original images, video and art, or translate audio to different voices and languages. Text-to-image/video generation has advanced, with the ability to create detailed, realistic visuals from textual descriptions.

■ Improvements in synthetic media (i.e., deepfake technology), include audio cloning and facial mapping, digital humans and virtual influencers. They are becoming commercially accessible and allow customers to engage in their native language and with cultural relevance.

■ Organizations are learning to use their own data and assets with GenAI through prompt engineering and fine-tuning. AI-ready data and associated metadata have become central to executing GenAI marketing strategies.

■ Synthetic data, a class of data that is artificially generated rather than obtained from direct observations of the real world, allows marketers to optimize advertising and customer experiences by augmenting scarce data, ensuring compliance, mitigating bias and preserving data privacy.

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Obstacles ■ Democratization of GenAI brings new ethical and societal concerns. Government regulations vary, and may hinder GenAI adoption, requiring new risk mitigation technologies and guardrails.

■ According to the 2024 Gartner Marketing Analytics and Technology Survey, the largest hindrance to GenAI use-case adoption in marketing is the gaps in marketers’ skills and roles as well as data and technology.

■ GenAI can be used for deepfakes, fraud and disinformation. Accurate detection of generated content is unreliable, requiring focus on building brand trust, including content certification and transparency.

■ Unintended, biased, defective or offensive artifacts require human oversight for review and accuracy.

■ GenAI is embedded in enterprise applications and point solutions, which can result in duplicative capabilities and integration complexity. Accountability for training and outputs, licensing and pricing is inconsistent, which may cause buyer confusion and result in unplanned costs.

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User Recommendations ■ Identify low-risk use cases in partnership with governance teams that enhance marketing’s operational capabilities or improve customer experiences with GenAI. Look to a mix of vendors, point solutions and agency partners. Consult vendor roadmaps to avoid overlap. ■ Examine and quantify GenAI’s advantages and limitations. GenAI breakthroughs in marketing require reasoning skills, budget allocations and effective risk management. Conduct pilots to avoid subpar offerings that exploit the current hype without returns. ■ Explore synthetic data to accelerate the development cycle and lessen regulatory concerns. ■ Mitigate the impact of deepfakes by working with legal, security and fraud experts. Algorithmic detection and authentication of content provenance are evolving to meet this need. ■ Invest in people to sample and review GenAI output to detect and prevent dissemination of inevitable mishaps. Use composite AI approaches to combine GenAI with other AI techniques.

Sample Vendors Adobe; Amazon Web Services; Anthropic; Google; IBM; Meta; Microsoft; Mistral AI; OpenAI; Salesforce

Gartner Recommended Reading Generative AI Use-Case Comparison for Marketing

How to Pilot Generative AI to Support Marketing Strategy

Develop an AI Strategic Roadmap for Marketing

Tool: Checklist to Verify Generative AI Marketing Outputs to Manage Risk

Quick Answer: What Are the Capabilities of a Digital Twin of a Customer?

Generative AI for Sales Analysis By: Dan Gottlieb, Adnan Zijadic, Melissa Hilbert

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Benefit Rating: Transformational

Market Penetration: More than 50% of target audience

Maturity: Adolescent

Definition: Generative AI (GenAI) technology permeates the entire sales tech stack and the majority of verticals. Gartner defines GenAI as tech that “can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content.” It is disrupting tech usage patterns for sales organizations and buyers. The GenAI market for sales is composed of a series of applications with embedded large language models (LLMs) and image generation capabilities.

Why This Is Important GenAI can help facilitate a sales productivity paradigm shift by improving the productivity of expensive talent and reducing operating expenses in pursuit of revenue. GenAI’s potential to disrupt how sales teams interact with unstructured information, complete complex workflows and generate insights has many sales teams investing resources into the technology. GenAI redefines the user-technology relationship, unlocking the next level in sales productivity.

Business Impact Most sales leaders expect efficiency gains, and some experience sales velocity lift, such as increased coverage per rep. Sales organizations with experienced automation teams and mature data management are best positioned to maximize GenAI value. Less mature teams can still expect efficiency gains.

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Drivers ■ According to the 2025 Gartner CSO Priorities Survey, 23% of chief sales officers and senior sales leaders hold themselves accountable to driving their sales organizations’ AI initiatives, motivated by interest in using GenAI to deliver productivity gains.

■ Sales leaders are evaluating GenAI to make better use of the large amounts of unstructured and semistructured data available to sales teams. Use cases include market research, value message creation, sales assistants and RFP response automation.

■ Sales leadership teams see GenAI as a driver of better business and decision-making insights, especially creating executive visibility into the details of key deals.

■ Best practices are emerging among high-performing frontline sellers, with AI partnership skills highly determinant of a seller’s likelihood to exceed their annual quota.

■ Sales technology vendors are making it easier for sales organizations to get value from GenAI by embedding it into their products with design patterns like RAG and agentic AI.

■ Cross-functional AI councils emerged to guide GenAI use case analysis and selection, emphasizing how AI can address existing challenges. These efforts are often led by IT with sales contributors.

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Obstacles ■ Ineffective leadership: Chief sales officers suffer from a lack of clarity on which AI use cases will drive ROI, causing leadership decision-making a top barrier to realizing value from AI investments.

■ Talent limitations: The lack of AI skills or effective AI training is a highly prevalent barrier to realizing value from AI initiatives.

■ Persistent data management issues: GenAI strategy is a data strategy, so using enterprise data and content is critical for accurate and relevant GenAI sales outputs, requiring reckoning with data debt.

■ Tech adoption: Sellers are slow to adopt new technologies provided by their employer. Sales leaders are challenged by established internal best practices and resistance from skeptical employees about effectively integrating GenAI into sales workflows.

■ Compliance challenges: GenAI adoption can be hindered by compliance requirements, regulations and corporate privacy policies for both the seller and customer.

User Recommendations ■ Focus on go-to-market problems that GenAI can solve effectively. Use methods to identify, scope and prioritize GenAI use cases aligned to your revenue objectives and that offer a path to tangible benefits.

■ Bring a sales AI vision to AI councils and IT partners in order to reduce friction experimenting with AI across the sales organization.

■ Invest in a strategic progression of AI talent development. Give operations teams space to learn, then apply their AI learnings through go-to-market projects. Align AI literacy efforts for frontline teams with the introduction of new AI technologies.

■ Resource “GenAI literacy” education to teach responsibility, trust and security to sellers. These skills are necessary for the safe use of GenAI.

■ Partner with vendors that promote responsible deployment of models by publishing usage guidelines, enforcing those guidelines, documenting known vulnerabilities and weaknesses, and proactively disclosing harmful behavior and misuse scenarios.

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Sample Vendors Anthropic; DeepSeek; Google; Meta; Microsoft; OpenAI Gartner Recommended Reading Innovation Guide for Generative AI in Sales Benchmarking Generative AI Adoption in Sales Create an AI Strategy for Your Sales Organization Visual Search Analysis By: Aditya Vasudevan Benefit Rating: Moderate Market Penetration: 5% to 20% of target audience Maturity: Adolescent Definition: Visual search for digital commerce helps customers find products with relevant visual attributes, typically via the use of computer vision, natural language processing (NLP) and machine learning (ML) technologies. Solutions analyze catalogs to understand taxonomy and product attributes in addition to the visual features, and can support search via text, image or video, and sometimes via the use of filters to narrow down product choices. Why This Is Important Visual search can improve conversion rates, average order value and customer engagement by presenting products with relevant visual attributes. This innovation is popular in B2C retail sectors such as fashion, sports, beauty, furniture and home improvement. However, we see a growing need in B2B, specifically within OEM verticals for part search/replacement. Business Impact Visual search enhances customer experience by: ■ Improving search relevance and product discovery ■ Supporting innovative experiences such as “shop the look” and shoppable media Gartner, Inc. | G00827302 Page 85 of 125 This research note is restricted to the personal use of kylie@polyai.com.

■ Personalizing search results and product recommendations based on customer preferences, historic purchases and profile

Visual search improves productivity and data quality by:

■ Reducing manual efforts in catalog management with automated tagging

■ Standardizing and enriching product data

■ Ensuring better data compliance

Drivers ■ The adoption of visual search has accelerated as innovative ways to engage in search are becoming more effective than just text-based search. ■ Visual search performs detailed visual feature analysis of products to provide more relevant results than text-only analysis of product descriptions. This is driving demand in verticals such as fashion, beauty, home improvement and OEMs, where presenting the right results of visually similar and complementary/replacement products improves conversion. ■ Visual search technology is maturing specially with new large language model (LLM) techniques that are used to tag product images to make products more discoverable. ■ Most vendors offer SaaS solutions, making it easier to switch when better-suited technologies are available.

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Obstacles ■ Few digital commerce platform vendors offer visual search natively. Clients have to procure the services of specialized search providers that meet their use cases.

■ While some search and product discovery vendors offer visual search, it is usually an add-on with limited capability. For example, they might provide the ability to recognize images, but not image tagging or data extraction. Businesses that want to differentiate are still seeking specialized solutions.

■ Unlike text-based search, generic visual search capabilities won’t deliver desired results for commerce use cases unless the ML underlying the technology is trained on the specific product domain.

■ Vendors have different capabilities for tag management, search, merchandising and configuration tools, and have different expertise for verticals. Organizations face a steep learning curve in terms of understanding how the technology works and selecting the right vendor.

User Recommendations ■ Define functional requirements for your verticals and products, and for specific features such as visual filters, image hot spot, “shop the look” or shoppable media.

■ Combine visual search with browse (e.g., product finders) and other modalities such as voice, image and text to maximize the value of the technology.

■ Investigate vendor capabilities for visual search and “shop the look” feature in terms of tag management, search relevance, visual product finders and configuration options. Understand whether they support additional functions such as personalization, product recommendations and analytics.

Sample Vendors Bloomreach; Constructor; Google; HawkSearch; Lucidworks; Netcore Cloud; Nosto; Partium; Rezolve AI; Syte

Gartner Recommended Reading Magic Quadrant for Search and Product Discovery

Critical Capabilities for Search and Product Discovery

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Digital Customer Service Analysis By: Wynn White

Benefit Rating: Transformational

Market Penetration: 20% to 50% of target audience

Maturity: Early mainstream

Definition: Digital customer service (DCS) applications manage persistent dialogues and conversations in a holistic manner, enabling fluid back and forth engagement with customers on their preferred digital channels. The DCS market has evolved beyond merely servicing customers one interaction (and one channel) at a time, and instead focuses on managing ongoing conversations to provide a personalized and seamless customer experience.

Why This Is Important The proliferation of digital engagement channels has reinforced customers’ expectations around instantaneous, seamless and effortless service experiences. The desire for self- service, combined with the emergence of conversational artificial intelligence (AI), has led to an evolution of most engagement models. As such, Gartner sees the emergence of a new area of customer care referred to as “digital customer service.”

Business Impact Enterprises that seek to improve digital experiences are gravitating toward digital customer service solutions to better connect with customers and orchestrate automations while maintaining cohesive customer conversations. This approach helps mitigate the risk of disconnected conversations that lose context from one interaction event (and channels) to another, minimizing the need for repeating information as the customer is serviced by different groups in an omnichannel service environment.

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Drivers ■ Digital customer service vendors design their platforms to facilitate end-to-end customer engagements with a focus on achieving desired outcomes, while maintaining an ongoing conversation.

■ By managing persistent dialogues, channels become attributes of the interaction, with the option to use multiple channels at the same time within the same interaction. An example is sending a link, via text while on a live voice call, as part of the holistic service workflow and part of the customer conversation.

■ Digital customer service vendors focus on engaging customers through digital means, such as mobile devices, in a proactive, intelligent, and quick manner. They utilize machine learning (ML) and natural language processing (NLP) technologies to prioritize the personalization of ongoing conversations, regardless of the communication channel.

■ Increased customer expectations and the need for availability 24/7, have positioned DCS as a digital solution that can leverage AI-driven features and chatbot capabilities to provide coverage and autonomous and personalized service.

■ By moving to DCS applications, organizations can reduce the overall cost of servicing customers, through the automation of routine questions and topics, handling large volumes of interactions autonomously, and reducing the (over) reliance on human coverage during operating hours.

■ DCS systems are agile and scalable, and can be deployed quite quickly, when compared to larger or more traditional customer service platforms, such as contact center as a service (CCaaS) or customer relationship management (CRM) platforms.

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Obstacles ■ Failure to unify engagement channels, across the enterprise and customer touchpoints, results in a disjointed and siloed experience, often lacking context and guidance, for both the customer and internal user.

■ Organizations may face integration challenges when connecting DCS solutions to other key systems such as CRM platforms or ERP platforms, and may struggle with issues associated with integrations with legacy systems.

■ Processes often conflict with delivering top-tier digital customer service, lacking the agility for quickly addressing fast moving conversations. Internal consulting side conversations, and rigid internal linear procedures can delay efforts for speedy resolutions.

■ Digital customer service capabilities are composable and can be entrusted to service organizations for configuration service experiences. Customer service team members may not have IT administration or solution creation experience, which can pose a challenge.

■ Overlapping capabilities between customer service systems can cause confusion and duplication of efforts, challenging leaders when planning and assigning key functionality within the customer service conversation.

User Recommendations ■ Build customer service design around continuous orchestration and invest in technology supporting a blend of digital engagement channels, including digital voice, for a next-generation engagement strategy.

■ Sustain conversations at scale by establishing an AI-supported self-service and automation including machine learning, real-time data analytics and knowledge management.

■ Leverage the modular approach of digital customer service offerings to implement priority use cases with digital-first providers, maximizing ROI and minimizing technical debt.

■ Develop a data strategy focused on key data sources, system integrations and data quality, to enhance technology capabilities and provide insights across the organization.

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Sample Vendors Dixa; eGain; Genesys; Glance; Glia; LivePerson; NiCE; Salesforce; Sprinklr; Zendesk Gartner Recommended Reading Market Guide for Digital Customer Service and Support How Generative AI Expedited the Future of Customer Service Equip Your Customer Service Reps to Support Seamless Customer Journeys VoC Platforms Analysis By: Deborah Alvord Benefit Rating: High Market Penetration: 20% to 50% of target audience Maturity: Early mainstream Definition: Gartner defines voice of the customer (VoC) platform as one that integrates feedback collection, analysis and action into a single interconnected platform that helps inform and improve the customer experience. Sources of feedback extend beyond direct surveying to include other, more indirect and inferred sources. Why This Is Important VoC platforms enable leaders responsible for functions such as customer service, marketing or sales to better manage the customer experience (CX) through a deep understanding of customer needs, motivations, goals and behaviors. The resulting insights trigger recommendations and actions across the enterprise. Business Impact A centralized VoC platform will: ■ Enable organizations to manage brand perceptions and gain deeper insights on customer perceptions and issues impacting their experience. ■ Provide a centralized and aggregated view of feedback data collected from direct, indirect and inferred sources. Gartner, Inc. | G00827302 Page 91 of 125 This research note is restricted to the personal use of kylie@polyai.com.

■ Analyze speech, text and web interactions to provide insights into the customer’s journey, using structured and unstructured data.

■ Ensure that relevant insights and follow-up tasks are assigned to the right team member for proper resolution and to close the customer feedback loop.

Drivers ■ Revenue growth initiatives are accelerating the adoption of VoC platforms that support actions that drive, improve and increase buyer engagement.

■ Organizations with a matured approach to VoC focus on continuous improvement to the customer’s journey, from end to end, identifying opportunities and initiating actions along the way.

■ Enterprises continue to improve customer service and retention efforts, measuring service interaction performance with VoC applications.

■ Mature strategies include alignment and integration with HCM solutions to complement employee experience feedback and life cycle.

■ VoC applications are utilized to understand customer buying and growth behavior, as well as for conducting market, user and product research. This includes adoption by B2B and B2B2C enterprises.

■ Improved innovations in AI, along with other automations, streamline the close-the- loop process and provide interaction or topic summaries, as well as AI-generated responses and actionable recommendations, based on customer feedback.

■ Vertical industry specialization, along with stronger integrations into key enterprise systems, (such as CRMs, ticketing systems and workflow process systems), drive organizations to seek improved VoC applications.

Obstacles ■ The ideal full VoC suite is uncommon and not yet realized. This includes direct and inferred feedback gathering capabilities as part of the customer service technology stack. This is partly due to overlapping VoC capabilities provided through other solutions. Organizational maturity in adopting a full VoC platform remains low and the vendor landscape is still emerging.

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■ The marketplace is crowded and fragmented with more than 30 vendors with comprehensive VoC solutions.

■ Individual departments are reluctant to relinquish or centralize their tools for the sake of a unified cross-departmental approach. Organizations continue to collect feedback through multiple applications. As the number of data sources increases, aligning VoC with existing single-view-of-the-customer initiatives, such as a customer data platform or lake, becomes a contentious issue. While VoC vendors propose a quick time-to-value for businesses, IT departments perceive challenges in terms of cost, complexity and inherent duplication.

User Recommendations ■ Conduct an internal audit to assess current customer feedback capabilities and identify opportunities to reduce duplicate feedback requests.

■ Conduct an RFP to ensure the chosen solution best aligns with your CX and business requirements and works with your key existing systems.

■ Prioritize future initiatives that collect VoC data by balancing the quality of the feedback with the quantity of feedback requested.

■ Determine the most suitable data architecture and analytical models to extract key customer insights at the individual respondent level and aggregated across customer segments.

■ Distribute relevant insights and actions across the organization, including frontline staff and management, in a timely manner using workflow and operational integration.

■ Integrate VoC insights into core business processes, ideally in real time and embedded as part of the workflow process. For example, prompt the customer to ask if they would like to connect to an agent immediately. In doing so, a case or ticket can automatically be opened, routed and assigned to the most appropriate team member.

Gartner Recommended Reading 5 Steps to Build a VoC Program for Customer Service and Support

Benchmark Analysis of Customer Service Voice-of-the-Customer Programs

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Gartner

How to Align the Customer Service Function’s Voice of the Customer Initiatives With Business Objectives

Revenue Enablement Platforms

Analysis By: Melissa Hilbert, Doug Bushée

Benefit Rating: High

Market Penetration: 5% to 20% of target audience

Definition:

Maturity: Adolescent

Revenue enablement platforms (REPs) unify sales, customer-facing enablement and revenue functions, streamlining roles such as sales, customer success, marketing, partners and presales. They empower sales enablement and marketing leaders to deliver comprehensive enablement programs, leveraging digital content, learning, practice, coaching and engagement analytics. REPs utilize AI and conversational intelligence to enhance skills-building, training and coaching.

Organizations must enable revenue-generating roles to improve sales outcomes while enabling buyers to self-serve. REPs support all revenue-generating roles and selling situations, offering a single platform for content, training and coaching. They integrate into the CRM, keeping sellers in the flow of work. They offer machine learning (ML) recommendations, just-in-time training to help customer-facing sellers improve engagement with buyers, and role-play practice and coaching to improve seller skills.

Business Impact

■ Enhanced agility in selling across digital and traditional channels, using consistent, clear messaging to better guide customers through their buying journeys.

Enabling revenue-generating roles to impact sales outcomes with holistic enablement of content, training and coaching will have these key benefits:

Drivers ■ AI for content, lesson creation and scoring, and digital human experiences increase this market’s ability to create innovative and adaptive learning and coaching experiences for sellers.

■ Digital sales rooms (DSRs) are a feature within REPs used to increase collaboration between buyers, revenue-generating roles or partners throughout the customer life cycle. Capabilities include online meetings, collaboration tools, buyer engagement analytics, conversational analytics, negotiation features, and links to configure, price and quote (CPQ) tools and digital commerce. This enables selling organizations to develop multiple stakeholder penetration and alignment.

■ Buyers prefer digital methods of engaging with sellers, and DSRs provide this while allowing sellers to engage both asynchronously and synchronously to decrease buyer conflict and increase sales velocity.

■ Adoption of REPs improves revenue-generating roles’ (i.e., sales, presales, marketing and customer success) performance, skills and engagement with buyers, improving sales outcomes and building trust, especially at moments of uncertainty.

■ REPs offer buyer engagement analytics that improve seller insights and actionability to influence buyers. Sales outcomes, including inferred revenue, can be linked to enablement initiatives.

■ REPs offer analytics that help measure the impact of enablement efforts.

Obstacles ■ Sales force automation (SFA) platforms do not offer strong and complete enablement capabilities, but are increasing capabilities that overlap with those of REPs.

■ Generative AI (GenAI) for content creation is still emerging and should be grounded with customer-specific data and monitored for hallucinations.

■ REPs lack full support for:

■ In-channel enablement with collaboration tools like Microsoft Teams or Slack

■ Augmented reality and virtual reality mediums like webinars and livestreams

■ Adaptive learning

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■ Organizations need to examine their willingness to “enable enablement teams.” There is a cultural shift to using technology and content such as video, coaching and conversation intelligence.

User Recommendations ■ Evaluate REPs for revenue-generating roles by investigating specific needs by role, even if sellers are your primary focus, and even if the needs overlap or the REP conflicts with some features of existing technology investments. REPs enable rapid improvements to the effectiveness of sales execution. ■ Evaluate the depth and breadth of ML for skills scoring and prescriptive recommendations including the use of GenAI by investigating deeper into how the platform’s GenAI and ML operate. ■ Examine closely each vendor’s AI capabilities for content creation, AI assistants and ML in the product. ■ Investigate solutions that include DSRs to broaden the audience and include customers to support virtual and hybrid buying and selling. ■ Examine the administrative UI of solutions carefully by exploring common use cases and the effort required to determine support and maintenance of the platform. ■ Learn about the REPs’ use of GenAI for content, search, learning and role-plays.

Sample Vendors Allego; Bigtincan; GTM Buddy; Highspot; Mediafly; Mindtickle; Pitcher; SalesHood; Seismic; Showpad

Gartner Recommended Reading Market Guide for Revenue Enablement Platforms

Toolkit: RFP for Revenue Enablement Platforms

Sales Enablement Benchmark Report 2024

Podcast: Measuring the Impact of Sales Enablement

Customer Data Platform Analysis By: Rachel Smith

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Benefit Rating: High

Market Penetration: 20% to 50% of target audience

Maturity: Early mainstream

Definition: A customer data platform (CDP) is a software application that supports marketing and customer experience (CX) use cases by unifying a company’s customer data from marketing and other channels. CDPs optimize the timing and targeting of messages, offers and customer engagement activities, and enable the analysis of individual-level customer behavior over time.

Why This Is Important CDPs set the standard for how all customer-facing functions manage customer data through capabilities such as data ingestion, profile unification and activation. CDPs originated as customer data management solutions for marketers; but in 2025, CDP technology is an enterprise data strategy decision. CDPs support an expanding set of use cases across CX functions such as marketing, sales, service and commerce.

Business Impact CDPs are increasingly crucial for marketers and others managing customer experience (CX). They are a hub for managing common data objects — from customer profiles and audiences, to offers. This enables downstream use cases like segmentation, orchestration and predictive modeling. CDP functionality is nearly ubiquitous across enterprise technologies that create customer-facing experiences given their need to process customer data (e.g., multichannel marketing hubs, marketing automation platforms).

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Drivers

■ With CDPs setting the standard for how enterprises manage customer data, more c-suite stakeholders see the importance of a CDP to functionally-specific (marketing, sales, etc.) and enterprisewide (analytics, enterprise architecture) needs. Buying groups are expanding, with an average of five groups providing funding for CDP purchase and two to three groups contributing to requirements and objectives.

■ The value proposition of composable architectures is taking root as innovations in data sharing enable CDPs to leverage customer data centralized in a cloud data warehouse, as opposed to ingesting or copying all data to a CDP. While this reduces data management complexity and increases CDP appeal and relevancy to data and analytics, and IT functions, it also requires a new operating model.

■ AI enablement is becoming a key differentiator for CDPs, scaling data and business process automation. AI models in CDPs support predictive analytics and personalization use cases, while metadata and data lineage enable AI and agents to make better decisions. CDPs are increasingly the trusted foundation for AI finetuning, grounding and model execution, particularly for large enterprise application providers’ (EAPs) CRM portfolios.

■ Organizations are embarking on complex customer journey orchestration initiatives that benefit from the automated workflows and unified customer data provided by a CDP. As the number of channels and touchpoints that brands reach their customers on continues to proliferate, automation is required to scale CX processes.

■ EAPs are tying the value proposition of their suites to the CDP’s expansive utility to all customer-facing functions. This is compelling for the plethora of existing customers of EAPs. The top five vendors in the broader CRM marketing software market hold 44.4% share (see Market Share Analysis: CRM Marketing and Cross- CRM Software, Worldwide, 2023). This puts pressure on stand-alone CDPs that don’t have a broader suite offering.

Obstacles ■ Cost-to-value: Marketing budgets are being squeezed, while CDP and forthcoming agentic AI pricing models remain opaque and expensive. Marketers find it challenging to forecast the relationship between use cases, compute and storage costs, and face risk of profile overages.

■ CDP ubiquity: While CDPs are stand-alone technology solutions, vendors in adjacent categories (e.g., MMH, MAP) have incorporated CDP-like functionality into their offerings, raising cost and redundancy concerns.

■ Long-term decision, fast-moving market: CDP buyers seek long-term partnerships with vendors given the complex implementation process. However, the stand-alone CDP market is in a period of significant M&A activity that forces buyers to question the future stability of providers.

■ Technical skills: Successful CDP use is correlated with having the technical skills to operate and integrate the technology. IT is often involved, and even with generative AI (GenAI) performing common tasks, upskilling downstream users is paramount.

User Recommendations ■ Familiarize yourself and your team with consumption-based pricing models, such as pay-as-you-go, pay-as-you-use, transaction-based and credit-based pricing. Request a measurement tool from CDP vendors to forecast and track consumption and cost.

■ Expand CDP’s role to support AI to enhance business value (e.g., journey orchestration).

■ Remain vigilant in reviewing your CDP vendor’s roadmap to ensure it keeps pace with competitors on key innovations around data warehousing and GenAI, among others.

■ Collaborate with stakeholders to build use cases and assess business value for unified customer data across marketing, IT, sales, service and commerce.

■ Evaluate vendors in the context of your CRM stack. Large EAPs differentiate by providing cross-platform integrations, activating data across functions.

■ Audit your technology landscape to identify potential overlaps (e.g., personalization engines, MMHs and MAPs).

■ Use proofs of concept to validate value creation on promised capabilities.

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Sample Vendors

ActionIQ; Adobe; Salesforce; Tealium; Treasure Data; Twilio

Gartner Recommended Reading

Magic Quadrant for Customer Data Platforms

Critical Capabilities for Customer Data Platforms

Toolkit: Use Case Library — Customer Data Platform, Personalization Engine and Multichannel Marketing Hub

Quick Answer: What Is a Composable Customer Data Platform?

Customer Journey Maps

Analysis By: Simon Yates, Saul Brand

Benefit Rating: High

Market Penetration: 20% to 50% of target audience

Maturity: Mature mainstream

A customer journey map is a visual representation of the steps a customer goes through to reach a goal and the organization’s touchpoints that are involved along the way. It follows the flow of events and presents the story of the customer’s journey, from their perspective, including highlights and challenges.

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Business Impact A focus on customer satisfaction, and growth through the design and development of new products and services, remain high priorities for CIOs and business executives (see 2025 CIO and Technology Executive Agenda: Grow The Digital Vanguard). Customer journey maps play a key role in delivering both customer satisfaction and growth by providing a simple but powerful way to show how customer needs and the value proposition are met — something few other models can do.

Drivers ■ Business product and service design remained a popular topic over the last year, and, as a result, journey mapping, with the number of inquiries to Gartner on the topic remaining the same.

■ Senior business executives expect a short downturn and are looking to drive growth through the design and delivery of new products and services, and a high-quality customer experience. Design approaches are needed that center on the customer, such as customer journey maps.

■ Customer behavior is changing at a faster rate than in the past, and organizations need to be able to adapt at the same pace.

■ Many organizations are shifting from “projects” to “products.” This involves using iterative, experimental approaches to developing services and experiences, and a focus on the “customer” at the center of the product and service design process. Customer journey maps — and more detailed customer experience maps — are a key model in this approach.

■ As responsibility for digital innovation shifts out of the IT organization and into the business (what Gartner terms “democratization”), business teams use methodologies more familiar to them. Many business team members are familiar with customer journey mapping and see it as a “business” methodology.

■ Customer journey maps don’t just help improve customer experience. They also enable more collaborative partnerships and co-created innovation by ecosystem entities.

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Obstacles

■ While many people are familiar with customer journey mapping, few have a deep understanding of how to apply it (see Quick Answer: How EA Leaders Can Make Business Architecture More Consumable).

■ Customer journey maps often lack quality, resulting in misleading insights and misinformed decisions.

■ Many organizations have yet to make the move to more human-centric design methodologies, often stuck in a project/requirements methodology. Customer journey mapping is not just a simple add-on to projects and requirements. It requires a change in the overall product and service design approach, which is often difficult for many organizations.

■ The customer journey map must be produced by both the business side and IT side; the customer’s “story” must be a common guide to everyone involved. This requires closer collaboration between different team members from business and IT, and a focus on shaping a story everyone understands. This can be challenging where business and IT, at a fundamental level, don’t understand each other.

User Recommendations

■ Provide product and service design teams with training in design thinking and human-centric design to help them understand the theory and practice involved.

■ Practice developing customer journey maps and telling the “story” from the customer’s perspective. Where possible, share the journey map with customers and get their feedback, and find out what you missed and what you got right.

■ Define a high-level product and service design process for your organization that includes customer journey maps.

■ Start out by putting the simplest, shortest journey for which you have the best data into a customer journey map. Prioritize this over applying design principles to simple problems right at the start.

Sample Vendors Ardoq; InMoment; JourneyTrack; Lucid; Microsoft; Milkymap; Orbus Software; Quadient; Smaply; TheyDo

Gartner Recommended Reading 5 Steps to Deliver EA as an Internal Management Consultancy

Ignition Guide to Conducting Customer Journey Mapping Workshops

How to Run a Customer Journey Mapping Workshop

Quick Answer: My Customer Journey Map Is Done — What Do I Do With It Next?

Video: How Do I Build a Customer Journey Map?

Customer Service and Support Suites Analysis By: Pri Rathnayake

Benefit Rating: High

Market Penetration: 5% to 20% of target audience

Maturity: Early mainstream

Definition: Customer service and support suites are systems that align all capabilities of customer service technology to create an integrated application platform on a unified data architecture. Many of these systems are built around the customer engagement center (CEC) or contact center as a service (CCaaS) at the core. They also offer extensions of capabilities from CEC into CCaaS and vice versa, as well as into knowledge, insights and resource management.

Why This Is Important A unified customer service and support suite can improve customer and employee experience by streamlining the orchestration of internal processes to serve external touchpoints. It can also simplify procurement and vendor management, while setting up the technology foundation necessary for future autonomous customer service.

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Business Impact As organizations increasingly focus on creating seamlessly personalized customer experiences, application leaders have many options for automating various aspects of the customer service function. These include AI assistants and AI agents, employee enablement, knowledge and insights orchestration, context preservation throughout the customer journey and channel-agnostic multiexperience. Such innovative capabilities are increasingly being offered as native features or add-ons to core customer service and support solutions.

Drivers ■ Customer service and support organizations need real-time interaction data across voice and digital channels to exploit advances in AI-enabled automation capabilities like AI agents, AI assistants, conversation mining and process mining.

■ The cost and complexity of customer service and support technology escalates when organizations deploy multiple adjacent vendor products with overlapping capabilities rather than a single unified enterprise solution for customer service.

■ Innovations such as AI-powered customer service use cases, LLM-based AI agents, and agentic AI make it feasible for vendors to incorporate previously disparate capabilities into their core solution.

■ Some of the leading technology providers are expanding their customer service and support portfolios through the acquisition of smaller, innovative companies that have proven their best-of-breed niche solutions in the marketplace.

■ The orchestration of more efficient and effective customer journeys is lowering service costs and encouraging the use of lower-cost engagement channels.

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Obstacles ■ Customer service and support suites are built around either a core of a CCaaS application or around a CEC application. When customer service organizations require multiple vendors’ solutions to meet their customer service ambitions, choosing from among overlapping capabilities is a challenge.

■ The functionality in CCaaS and CRM CEC increasingly overlaps. However, no vendor yet offers a complete, unified solution spanning all the process orchestration, contact routing and interactions, knowledge and insights, and resource management capabilities necessary to comprehensively meet enterprise customer service and support requirements.

■ The monolithic nature of some of the emerging customer service and support suites makes avoiding duplication of capabilities difficult when enterprises must deploy complementary solutions, which limits the choices available to enterprises. They can either carry the additional cost of duplicated capabilities, or consider transformational projects that entail significant change management burdens.

■ Current-state customer service and support suites that have evolved from monolithic solutions continue to offer limited integrations to third-party best-of-breed customer service and support applications and solutions that service other business functions, like ERP and human capital management.

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User Recommendations

■ Create a vendor-agnostic technology reference model for customer service that represents a customer technology platform for your organization.

■ Issue an RFI or RFP to representative customer service and support suite vendors, detailing both mandatory and optional business requirements to be met without any third-party integrations.

■ Prioritize bidder responses that offer modular capabilities interoperable with existing solutions, for a scenario where the objective is to rationalize the current customer service and support technology estate for operational reasons or to standardize around an incumbent vendor solution.

■ Prioritize bidder responses where a vendor’s unified customer service and support suite can meet all mandatory business requirements. Ensure it has modular extension options for a scenario where the objective is to enable business transformation by rapidly deploying novel functionality while consolidating the customer service and support technology stack.

■ Opt for customer service and support suites that favor composable architectures modeled on a unified data layer foundation and a use-case-specific modular application layer built with microservices in both the above scenarios.

Sample Vendors

Amazon Web Services; eGain; Genesys; Microsoft; NiCE; Salesforce; ServiceNow; Zendesk

Gartner Recommended Reading

Innovation Insight: Unified CRM CEC and CCaaS Lays Foundation for Future of Customer Service

Quick Answer: What Does a Technology Reference Model for Customer Service and Support Look Like?

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Magic Quadrant for Contact Center as a Service

Gartner

Climbing the Slope

Personalization Engines

Analysis By: Penny Gillespie

Benefit Rating: High

Market Penetration: 20% to 50% of target audience

Definition:

Personalization engines are a technology that enables marketing leaders to identify, set up, conduct and measure the optimum experience for an individual based on knowledge about them, their intent and context. By improving customer experience (CX), personalization engines can boost conversion rates, increase customer satisfaction and grow revenue in support of three use cases: marketing, digital commerce and customer service.

Getting personalization right is a challenge, especially as its scope continues to increase, encompassing all aspects of a customer’s journey, associated touchpoints and business use cases. The 2025 Gartner CMO Spend Survey shows an expected 33% increase in yearover-year budget allocated to personalization efforts over 2024 (see Insights From the 2025 CMO Spend Survey).

Personalization engines improve outcomes for marketing, digital commerce, merchandising and customer service experience efforts. They enable data collection, segmentation and experience testing, and make real-time product and content recommendations across channels and use cases. They drive revenue through increased conversion, improved customer satisfaction and higher average order values while reducing abandonment rates.

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■ Sophisticated testing: Some vendors have expanded their testing capabilities from A/B and multivariate to include multiarmed bandit. Some have also tightened the reins on testing to stop inefficient testing quicker and to better manage statistical soundness.

■ Sophisticated AI options: Many providers offer out-of-the-box, built-in, customer- level predictions that can be used for triggering, behavioral segmentation and identifying customer attributes or ideation (promotions and engagement). Some offer customer journey optimization algorithms and also support “bring your own algorithms.”

■ Use of GenAI: Many vendors offer sophisticated testing, but some incorporate GenAI to enable easier test setup and improved statistical soundness. Others use GenAI for automated segmentation and triggering, creating emotionally relevant messages or enabling sophisticated virtual agents. The 2025 CMO Spend Survey reveals that 19% of CMOs believe their GenAI investments are delivering the most ROI/impact in the area of personalization.

■ Recognizing intent of anonymous customers: Vendors continue to expand their capabilities to understand anonymous customers’ intent and deliver better out-of- the-box experiences, which in turn promotes customer data sharing.

Obstacles ■ Personalization technology and organizational complexity: Personalization engine features and capabilities can require significant expertise. Organizations can be equally challenged in aligning staffing to support personalization endeavors.

■ Confusing product landscape: Personalization engines compete against many solution types — multichannel marketing hubs (MMHs), digital experience platforms (DXPs) and customer data platforms (CDPs) — all with overlapping capabilities and inconsistent channel support. This complicates vendor selection.

■ Complicated vendor offerings: Personalization vendors go to market in three ways: pure-play personalization engines, personalization engines with complementary solutions (e.g., WCM, CDP and digital commerce) or personalization engines in conjunction with an enterprisewide product portfolio. This adds further complexity to purchasing because multiple products may be required based on the scope of personalization (i.e., customer journey touchpoints).

User Recommendations

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■ Determine your requirements for personalization based on where it will occur in the customer journey, which channels will be supported and the desired outcome(s). Create an appropriate CX steering committee to support it. ■ Assess personalization capabilities in your existing martech stack (e.g., CDP, MMH, DXP). Develop a map/workflow of how these solutions work together to deliver personalization. Identify gaps in existing functionality (e.g., analytics, segmentation, testing, real-time triggering, AI/GenAI, data storage) to determine specific personalization engine requirements. ■ Identify and map sources of customer data (e.g., behavioral, contextual, transactional) with appropriate product data (e.g., content, images, inventory levels) to understand data integration and governance needs. ■ Invest in training to increase personalization engine adoption and use. Evaluate vendor training resources and customer success teams to speed up instruction.

Sample Vendors Adobe; Bloomreach; CleverTap; Dynamic Yield by Mastercard; Insider; Monetate; Optimizely; Salesforce; SAP; Sitecore Gartner Recommended Reading Magic Quadrant for Personalization Engines Critical Capabilities for Personalization Engines Case Study: Drive Revenue Through High-Impact Personalization Revitalize Your Personalization Strategy to Drive Future Growth Navigating Personalization Challenges in Buying Groups Quick Answer: How Is GenAI Most Commonly Used in Digital Commerce? Customer Journey Analytics and Orchestration Analysis By: Christopher Sladdin Benefit Rating: High Market Penetration: 20% to 50% of target audience Gartner, Inc. | G00827302 Page 109 of 125 This research note is restricted to the personal use of kylie@polyai.com.

Maturity: Early mainstream

Definition: Customer journey analytics and orchestration (CJA/O) solutions track and analyze how customers and prospects interact with an organization across assisted, digital and physical channels over time. Organizations can subsequently prioritize and orchestrate real-time interventions in customer journeys to drive desired outcomes (e.g., improved CX, conversion or loyalty). Interventions may take the form of outbound messaging, personalized digital experiences or seller/agent actions.

Why This Is Important Channel proliferation has resulted in increasingly complex multichannel customer journeys, which degrade the customer experience (CX) and are expensive to serve. CJA/O tools help organizations understand customer activity across channels and, in particular, the interface between digital and assisted channels. Leaders increasingly look to these tools to nudge customers toward better-fit channels and outcomes and to deliver personalized, customer-centric engagement and revenue-generating campaigns.

Business Impact CJA/O has broad applicability across industries and customer-facing functions. Its most common use cases are in marketing and customer service, although it has potential throughout the customer life cycle.

CJA/O supports customer journey management disciplines, where leaders adopt a continuous improvement posture, designing and optimizing journeys to drive specific business and CX outcomes, including increased revenue or conversion, improved efficiency, cost reduction and enhanced customer loyalty.

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Drivers ■ CJA/O is a strategic priority for multiple roles, especially as marketing, sales, service and support, CX and product leaders strive to gain a better understanding of customers’ complete journeys across channels and functions.

■ CJA/O can enable greater personalization of customer journeys, including delivering next best actions in real time by leveraging machine learning and AI. These orchestrations can be designed for broader groups of customers (e.g., personas/segments) or for individual customers based on an understanding of the individual’s context and intent.

■ Access to CJA/O is accelerating as function-specific suite providers, such as multichannel marketing hubs, contact center as a service or voice-of-the-customer solutions, have acquired or built CJA/O capabilities into their broader solutions. A healthy market of specialist CJA/O vendors remains.

Obstacles ■ Business leaders struggle to access, analyze and activate their customer data across channels. The greater the number of siloed customer channels or data sources, the more challenging delivery of comprehensive CJA/O becomes.

■ Vendor literature and CX practitioners often conflate journey management terminology. In particular, customer journey analytics is often confused with journey mapping. Although the two processes are complementary, their supporting technology solutions remain largely siloed.

■ The optimal buyers of CJA/O solutions are data and analytics leaders who can drive consistency and efficiency in how the organization analyzes customer journeys via a single technology stack. But coordinating across all the functions that oversee or manage underlying channels is challenging given the often-conflicting priorities.

■ The move toward CJA/O as a capability within broader function-specific solutions challenges organizations looking for a cross-functional understanding and approach to CX.

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User Recommendations ■ Acknowledge that valuable insights come from understanding the combination of channels customers use, not from understanding customer (or segment) behavior within a single channel. ■ Understand the differences between customer journey analytics and mapping, and assess your need for broader technology solutions beyond CJA/O to support customer journey management disciplines. ■ Evaluate your existing technology stack to see whether you’re already paying for an application with journey analysis capabilities — because some journey analysis functionality is often embedded into other systems. ■ Avoid measuring outcomes with channel-specific KPIs, such as single-channel conversion rates or cost per acquisition. Channel-specific KPIs are most useful as diagnostic indicators for prioritizing optimizations. ■ Start by focusing on two to three high-impact and high-visibility channels, where the journey benefits both the customer and organization, and the data is both available and valuable.

Sample Vendors Adobe; Alterian; CSG; Genesys; Joulica; Medallia Gartner Recommended Reading Market Guide for Customer Journey Analytics & Orchestration Innovation Insight: Customer Journey Analytics & Orchestration Distinguishing Customer Journey Management, Mapping, Analytics and Orchestration 2024 Strategic Roadmap for Customer Service Journey Management Customer Engagement Center Analysis By: Pri Rathnayake Benefit Rating: High Market Penetration: More than 50% of target audience

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Maturity: Mature mainstream

Definition: The CRM customer engagement center (CRM-CEC) is a cohesive set of software built around core case management tools. It is used to provide customer service and support by engaging with customers while intelligently orchestrating the processes, data, systems and resources of an organization. CRM-CEC applications offer workflow management capabilities and may be used as a system of record for customer interactions.

Why This Is Important CRM customer engagement centers are customer service technology suites offering externally focused interaction orchestration and internally focused process orchestration. CRM-CECs are often seen as a key component of a broader customer technology platform framework, enabling consistent and intelligent experiences through all customer-facing channels. Core functions of the CRM-CEC application are case management, digital engagement, workflow management and knowledge management. CRM-CECs can serve as the official system of record for customer data.

Business Impact Superior customer service is expected to be a low-effort, personalized and seamless experience. The ability to orchestrate the processing of customer engagements for the best outcomes is key to improving customer satisfaction, resolution times and operational efficiency. Intelligent workflows and automations enable customer service organizations to reduce burdens of customer service agents (CSAs) and enable a robust self-serve experience for customers. CEC technologies are applicable for organizations of all sizes and industries.

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Drivers ■ Over recent years, Gartner has observed important changes in how organizations handle customer service. Whereas formerly a single department would respond to customers’ needs, increasingly customer service is becoming a cross-departmental function that requires coordination and visibility.

■ To support these changes, most customer engagement center vendors have increased research and investment in AI and generative AI (GenAI), conversational virtual customer assistants, dynamic knowledge management programs, and agent assist tools.

■ In many cases, functional innovation has moved toward deploying first to cloud- based CEC offerings and then retrofitting toward other deployment models.

Obstacles ■ The customer service technology landscape includes myriad vendors and offerings in areas such as the CRM-CEC, contact center as a service, knowledge management and analytics, workforce engagement management (WEM), and field service management. Although a unified customer service suite spanning all these domains does not yet exist, CRM-CEC is leading the increasing market shift in that direction.

■ Customer service organizations are largely set up to wait for customers to engage. This reactive approach prevents organizations from identifying key requirements to help them make sense of the overlapping application landscape that continues to grow in complexity.

■ This reactive approach also results in experiences involving a high degree of effort from customers and limited self-service effectiveness, especially as customers often switch channels or use multiple channels concurrently.

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User Recommendations ■ Expand CEC focus beyond customer service and support use cases by prioritizing vendors offering low-code development capabilities for application user experience development, additional workflows and composability support across their SaaS and other APIs. ■ Shift away from reactive channel-focused capabilities toward supporting proactive customer journeys with an appropriate mix of digital touchpoints and interaction modalities (or multiexperience) by prioritizing vendors with digital engagement, automation of engagement and knowledge management capabilities. ■ Invest in AI technologies that have virtual agent and/or agent assist functionality and usability. Prioritize vendors with advanced automation of engagement paired with real-time continuous intelligence capabilities.

Sample Vendors Creatio; eGain; Freshworks; Microsoft; Oracle; Pegasystems; Salesforce; SAP; ServiceNow; Zendesk

Gartner Recommended Reading CRM Application Functionality Taxonomy Propeller

Use-Case Prism: Artificial Intelligence for Customer Service

2023 Strategic Roadmap for Customer Service and Support Technology

Prescriptive Analytics Analysis By: Peter Krensky

Benefit Rating: High

Market Penetration: 5% to 20% of target audience

Maturity: Adolescent

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Definition: Prescriptive analytics is a set of capabilities that specifies a preferred course of action and, at times, takes automated actions to meet an objective. The most common types are optimization methods (a combination of predictive analytics and rules), heuristics and decision analysis methods. Unlike descriptive, diagnostic, and predictive analytics, prescriptive analytics explicitly guide decision making toward optimal business outcomes by evaluating future scenarios and leveraging AI and ML.

Why This Is Important Prescriptive analytics capabilities either automate or augment decision making to improve business responsiveness and outcomes. From a “purist” perspective, the term “prescriptive analytics” is a broad category with little hype, encompassing components with varying positions across the Hype Cycle and various levels of maturity. Such components include optimization, rules combined with predictive techniques, what-if scenarios and decision intelligence. The increasing focus on composite AI is further propelling the importance of prescriptive analytics.

Business Impact Prescriptive techniques support:

■ Strategic, tactical and operational decisions to reduce risk, maximize profits, minimize costs or more efficiently allocate scarce or competing resources

■ Recommendations for a course of action that best manages the trade-offs among conflicting constraints and goals

■ Exploration of multiple scenarios and comparison of recommended courses of action

■ Strategic and tactical time horizons as well as real-time or near-real-time decision making

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Drivers ■ Prescriptive analytics benefits from maturing and expanding data science initiatives, better algorithms, more cost-effective cloud-based computing power and a substantial increase in available data.

■ With improvements in analytics solutions, data quality and user skills, prescriptive analytics will continue to advance.

■ The increasing popularity of graph techniques provides a great substrate for prescriptive analytics. Graph techniques highlight early signals, causality links and paths forward, facilitating the implementation of decisions and actions.

■ Demand is shifting from traditional reactive reporting to actionable, proactive insight, placing greater focus on optimization, advanced techniques, composite AI and prescriptive analytics.

■ AI platforms and decision management tools increasingly include prescriptive techniques, driving user acceptance and potential value to the organization.

■ Prescriptive analytics continues to evolve, ranging from relatively straightforward rule processing to complex simulation and optimization systems. To respond to an ever-greater complexity in business, organizations need more advanced prescriptive analytics and composite AI (for example, combining rules/decision management with machine learning or optimization techniques).

■ Organizations continue to improve, optimize and automate their decision making by applying decision intelligence and decision modeling. Prescriptive analytics is a key enabler of this approach.

■ Prescriptive analytics is moving quickly through the Trough of Disillusionment, driven by excitement and value around decision intelligence, robotic process automation and agents.

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Obstacles ■ Expertise on how and where to apply prescriptive techniques is lacking.

■ The industry lacks formal operationalization methods and best practices.

■ Historically, organizations have required separate advanced analytics software specializing in prescriptive techniques. Such point solutions offer little cohesion across the analytics capability continuum, from descriptive to diagnostic to predictive to prescriptive. ■ Even established use cases can fall victim to common data science challenges, such as data quality issues, bias and talent shortages. ■ Although it is a necessary competency, prescriptive analytics does not automatically result in better decision making.

User Recommendations ■ Start with a business problem or decision involving complicated trade-offs, multiple considerations and multiple objectives. ■ Explore the breadth of prescriptive analytics approaches and decision models available. Identify the ones that best cater to your specific business problems and skills. ■ Analyze packaged applications to determine which applications provide specific vertical or functional solutions and which service providers have the necessary skills. ■ Ensure the enterprise is willing to rely on analytics recommendations by gaining buy- in from stakeholders — ranging from senior executives to frontline workers carrying out the recommended actions. ■ Ensure your organizational structure and governance program will enable the enterprise to implement and maintain functional as well as cross-functional prescriptive analytics recommendations.

Sample Vendors AIMMS; Amazon Web Services; FICO; Frontline Systems; Google; Gurobi Optimization; IBM; Microsoft; River Logic; SAS

Gartner Recommended Reading Augment Predictive and Prescriptive Analytics With Generative AI

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Entering the Plateau Digital Experience Platforms Analysis By: Irina Guseva

Benefit Rating: High

Market Penetration: More than 50% of target audience

Maturity: Mature mainstream

Definition: A digital experience platform (DXP) is a cohesive set of integrated technologies designed to compose, manage, deliver and optimize personalized digital experiences across multiple channels in the customer journey.

Why This Is Important DXPs enable enterprises to compose, manage, deliver and optimize contextualized digital experiences for a wide range of audiences. They support many different channels and devices, such as websites, digital commerce sites, customer, partner and employee portals, mobile devices, kiosks, automotive displays and the Internet of Things (IoT). The composed nature of DXPs results in faster time to market, lower deployment costs and higher levels of customer and employee engagement and satisfaction.

Business Impact Poor digital experiences, often delivered in silos, lead to customer and employee frustration. DXPs help enterprises deliver compelling digital experiences to consumers, employees, citizens and partners. They provide significant efficiencies in composing, managing, delivering, contextualizing and optimizing digital experiences through multiple touchpoints.

Drivers ■ Organizations look to DXPs to address their growing need to improve customer and employee experiences, and to establish a stronger competitive position in digital business. ■ DXP brings multiple silos of engagement (for marketing, sales, service and digital commerce, for example) into a single seamless experience.

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■ Adoption of multiexperience strategy is leading to a total experience model — something that DXPs provide strong support for and that improves the experience at the intersection of customers and employees. ■ Organizations want the ability to scale and pivot as business needs and environments change — something that DXPs help them do. ■ DXPs enable business agility, elasticity, flexibility, extensibility and faster time to market. ■ DXPs embrace a composable user experience that supports a composable business model. ■ The need for an integration layer, supporting APIs, integration platform as a service (iPaaS) and other models, is driving adoption of DXPs. ■ Many organizations want content experts and other business experts, like marketers, to manage content and digital experiences with minimal IT support. DXPs provide powerful, user-friendly interfaces to enable the management of digital experiences.

Obstacles ■ There is often a lack of digital maturity, skills, disciplines and people in the right positions to realize the benefits of DXPs. ■ Monolithic DXPs, which have a lot of native functionality, are expensive and cannot be licensed appropriately for enterprises’ precise usage or needs. ■ Some industries are conservative, and others may have use cases associated with low digital experience aspirations. ■ With large functional footprints, monolithic DXPs can have limited agility and may be complex to deploy. ■ There may be organizational inertia or resistance to change.

User Recommendations ■ Ensure a business-aligned and streamlined DXP strategy by focusing on business outcomes, along with governance, including key business and IT stakeholders. ■ Plan an architecture for a DXP that suits your enterprise’s vision by examining its current position, identifying gaps in current functionality, and assessing opportunities to employ the innovations required to fulfill that vision.

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■ Create an internal roadmap based on your enterprise’s desired outcomes, technological maturity, potential disruptors and risks for the next three to five years, keeping composable DXPs and the ideal user experience in mind. Sample Vendors Adobe; Contentful; Contentstack; Optimizely; Uniform Gartner Recommended Reading Magic Quadrant for Digital Experience Platforms Critical Capabilities for Digital Experience Platforms Defining the Digital Experience Platform Adopt a Composable DXP Strategy to Future-Proof Your Tech Stack

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Appendixes

Hype Cycle Phases, Benefit Ratings and Maturity Levels

Table 2: Hype Cycle Phases

(Enlarged table in Appendix)

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Table 3: Benefit Ratings

Source: Gartner (July 2025)

Table 4: Maturity Levels

(Enlarged table in Appendix)

Recommended by the Authors

Some documents may not be available as part of your current Gartner subscription. Understanding Gartner’s Hype Cycles Tool: Create Your Own Hype Cycle With Gartner’s 2024 Hype Cycle Builder The Elusive CRM Magic Quadrant Market Share: Customer Experience and Relationship Management, Worldwide, 2024 How to Select the Best-Fit CRM Vendor for Your Business Objectives Quick Answer: Why Isn’t There a Universal Magic Quadrant for CRM?

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Gartner

© 2025 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. This publication may not be reproduced or distributed in any form without Gartner's prior written permission. It consists of the opinions of Gartner's research organization, which should not be construed as statements of fact. While the information contained in this publication has been obtained from sources believed to be reliable, Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartner research may address legal and financial issues, Gartner does not provide legal or investment advice and its research should not be construed or used as such. Your access and use of this publication are governed by Gartner's Usage Policy. Gartner prides itself on its reputation for independence and objectivity. Its research is produced independently by its research organization without input or influence from any third party. For further information, see "Guiding Principles on Independence and Objectivity." Gartner research may not be used as input into or for the training or development of generative artificial intelligence, machine learning, algorithms, software, or related technologies.

Gartner

Table 1: Priority Matrix for CRM Technologies, 2025

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Source: Gartner (July 2025)

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Table 2: Hype Cycle Phases

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Source: Gartner (July 2025)

Table 3: Benefit Ratings

Source: Gartner (July 2025)

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Table 4: Maturity Levels

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