ADOBE 2026 AI AND DIGITAL TRENDS REPORT

The past three years have brought significant market shifts — from the adjustment to post-pandemic norms to the rapid mainstreaming of emerging technologies like AI. Organizations report measurable improvements during this period in key CX performance metrics, such as personalization (70% say this metric has somewhat or significantly improved), lead generation (64%), and customer retention (59%). Yet these gains coexist with modest self-perceptions of digital CX maturity, with more than half (57%) saying their organization is on par with or behind peers and only about a third (36%) considering themselves ahead of the curve.

Despite doubts about their digital capabilities, organizations are making some progress in deploying generative AI. Across most customer experience workflows we examined — from marketing content creation to customer support, personalization, and back-office operations — experimentation with generative AI is widespread, with roughly one-quarter to one-third of organizations running limited pilots in these areas. The vast majority of organizations report improvements driven by generative AI in areas including content ideation and production, employee productivity and efficiency, and even marketing-driven revenue growth. (Fig. 1)

Organizations are eager to scale generative and agentic AI, yet many still fall short on the foundational tools, data structures, and measurement practices required to support organization-wide deployment. More than half (53%) say their content supply chain remains largely linear and resource-intensive, and only 47% are using generative or agentic AI for journey design or omnichannel activation — capabilities essential for delivering personalization at scale.

One major challenge for organizations is that agentic AI lacks the necessary supporting infrastructure. Only 51% have cloud-based technology for agentic AI, compared to 89% that have the technology to support generative AI. Investments in responsible use guidelines, integration tools, customer data platforms, data management processes, and employee training are all significantly lower for agentic compared to generative AI.

The ability to measure and communicate the true impact of AI remains a critical stumbling block. When asked which metrics matter most for evaluating AI success, organizations overwhelmingly point to customer satisfaction and loyalty metrics such as Net Promoter Score (NPS), retention, and churn. However, 52% say their organization struggles to demonstrate measurable returns on AI investments using CX-related metrics, and over half (56%) report that leadership at their organization prioritizes purely financial outcomes when assessing the success of AI initiatives.

Tools for tracking ROI remain underdeveloped for AI across the board. Only 44% have implemented a measurement framework for generative AI, and even fewer (31%) for agentic AI. Nearly half (47%) have neither framework in place nor are unsure whether one exists.

Internal challenges with AI implementation limit organizations’ ability to meet rising customer expectations. Customers reward relevance, clarity, and convenience, and they react quickly when brands fall short. Half of customers say they will disengage with a brand if promotions feel irrelevant or mistimed, and 45% will disengage if they receive too many promotions, regardless of relevance. The sweet spot for engagement is short — half of customers say promotional content has two to five seconds to capture their attention, and about one-fifth judge in less than two seconds. The key drivers of engagement are immediate personal relevance, unique content, visuals and sound, and special offers.

Delivering consistently relevant experiences requires AI trained on unified, high-quality data and the ability to act on that data in real time. Yet less than half (44%) of organizations say their data quality and accessibility is currently adequate for AI in general, and just 39% have a shared customer data platform capable of supporting agentic AI.

This readiness gap persists even as organizations acknowledge the problem. When asked about their priorities for AI investments, only 32% named data quality, unification, and governance as a top focus, and just 20% prioritized increasing the value and understanding of data. This is despite the fact that 52% admit that their current data unification and structure limits advancement of AI initiatives, and a full 75% cite data integration and quality as the top challenge for implementing agentic AI solutions. Data integration ranks ahead of other challenges, such as lack of relevant talent and limited upskilling resources (71%) and unclear return on investment or business case (68%).

These constraints extend beyond data into content and experience operations more broadly. Just 54% of organizations are preparing to optimize content for AI-powered discovery tools, even as customers increasingly rely on such tools to navigate digital shopping experiences. Further, only 39% of organizations have a unified customer data foundation that enables them to extract insights from all the data created by their AI agents and conversational interfaces.

Differences between executives and day-to-day practitioners threaten progress in AI deployment. Both groups agree on broad priorities for AI investments — personalization, customer satisfaction, and workflow automation — but their views on readiness, impact, and performance diverge in consistent ways.

Nearly one-third of respondents say executives and day-to-day practitioners at their organization are misaligned on AI strategy, and 47% say alignment is only partial at best. The drivers of alignment include clear communication of AI goals (72%), collaborative planning (69%), and strong leadership support (59%). The top challenge causing misalignment is executive misunderstanding of AI (61%) — outranking other factors like resistance to change or technology adoption (52%), insufficient communication about AI’s role (52%), and unclear measurement of AI’s value and ROI (39%).

These differences show up in how each group defines goals for AI investments. Practitioners are more likely than executives to focus on the operational realities that let them deliver better experiences, like content creation and operational activation. Executives, meanwhile, are more likely to emphasize goals like revenue growth and customer satisfaction (Figure 5). Financial outcomes are a priority for most respondents, but 62% of executives consider metrics like operational efficiency and cost savings a top or large priority, compared to 54% of practitioners.