Generative AI is delivering measurable gains across customer experience, but only a minority of organisations are prepared to scale agentic AI, exposing a widening gap between ambition and execution.
Artificial intelligence is reshaping how organisations design and deliver customer experiences, but ambition is outpacing execution. Adobe’s AI and Digital Trends 2026 report, based on a global survey of 3,000 executives and practitioners alongside 4,000 consumers, finds that early gains are visible across the market, yet structural readiness remains uneven.
Organisations report measurable improvements from generative AI, particularly in personalisation, lead generation, and customer retention — 70% cite gains in personalisation and 64% in lead generation. Yet adoption remains fragmented. Most organisations have experimented with generative AI, but only a minority have integrated it across workflows, and even fewer still have embedded it organisation-wide.
That gap reflects a broader shift in how customer experience is evolving. Expectations are rising fast: 80% of organisations anticipate real-time, highly personalised experiences, and 72% expect seamless journeys across digital and physical touchpoints. Yet the infrastructure required to deliver these outcomes consistently is still developing.
Early Gains From Generative AI Are Driving Momentum
Over the past three years, generative AI has moved from experimentation to tangible impact across multiple customer experience functions. Organisations report improvements in content production, productivity, and even revenue growth, a sign that AI is beginning to influence both operational efficiency and business performance.
Adoption patterns suggest a cautious but steady approach. Across workflows including marketing, customer support, and back-office operations, roughly a quarter to a third of organisations are running pilots, while a larger share continues to explore use cases. The technology is widely recognised as valuable, but confidence in its scalability remains limited.
Customer behaviour is accelerating the need for adoption. Nearly half of consumers say they would use AI for personalised recommendations, while 44% are open to AI-enabled customer service. One in four already relies on AI-powered platforms as their primary source for information and decision-making, underscoring a shift toward AI-mediated interactions.
Agentic AI Is Emerging, but Adoption Remains Limited
As organisations look beyond generative AI, attention is shifting toward agentic systems designed to take autonomous action across workflows, automate routine tasks, support decision-making, and handle customer interactions with minimal human intervention.
The ambition is significant. Around a third of organisations are prioritising agentic AI over generative AI, and many expect these systems to handle the majority of customer interactions within the next 18 months, particularly in customer support and post-purchase engagement.
Adoption, however, remains at an early stage. Fewer than a quarter of organisations report running pilots, and only 16% have embedded agentic AI at scale in customer support. This contrast between expectation and current capability points to the structural challenges organisations face in moving from experimentation to deployment.
Customer sentiment adds another layer of complexity. While 43% of consumers are open to interacting with AI agents, significantly fewer are comfortable with autonomous decision-making or sharing personal data. Organisations, meanwhile, tend to overestimate customer readiness, creating a misalignment between strategy and experience that brands cannot afford to ignore
Data and Infrastructure Are Defining the Readiness Gap
The report identifies data quality and integration as the primary barriers to scaling AI-driven customer experience. Fewer than half of organisations say their data is sufficiently accessible and unified for AI use, while only 39% have a shared customer data platform capable of supporting agentic AI.
These limitations extend beyond data into measurement and governance. More than half of organisations report difficulty demonstrating measurable returns from AI investments, and only a minority have established frameworks for tracking performance. Without clear metrics, scaling AI initiatives becomes increasingly difficult to justify.
Internal misalignment further compounds these challenges. Nearly a third of organisations report gaps between executive leadership and practitioners in how AI strategy is understood and executed, while 47% describe alignment as only partial. These differences shape priorities, investment decisions, and ultimately the pace of adoption.
Execution Challenges Are Slowing Progress
Beyond infrastructure, operational friction is limiting how quickly organisations can translate AI potential into outcomes. More than half of organisations say their content supply chains remain linear and resource-intensive, restricting their ability to deliver personalised experiences at scale.
Workforce readiness also remains a concern. While 61% of organisations believe AI should be considered an indispensable coworker, only 45% say they have sufficient training programmes in place, and fewer than half report that employees are comfortable using AI in their roles.
These gaps highlight a broader issue: organisations are being asked to adapt to AI-driven change faster than their systems, processes, and teams can evolve.
Key Priorities for Closing the Gap
The findings point to a clear set of priorities. Organisations must strengthen data quality, integration, and accessibility across systems, and align executive strategy more closely with operational execution. Building measurement frameworks that connect AI to business outcomes and designing AI experiences that prioritise transparency and user control will be equally critical.
Conclusion
AI is already reshaping the customer experience, but realising its full potential depends on how effectively organisations close the gap between ambition and readiness. Generative AI has demonstrated clear value; agentic AI represents the next phase of transformation. Without stronger data foundations, clearer internal alignment, and greater operational discipline, progress will remain uneven.
The competitive advantage will belong to organisations that move beyond experimentation and build systems capable of supporting AI at scale. As expectations continue to rise, the ability to deliver relevant, responsive, and trustworthy experiences will define who leads and who falls behind.
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