AI and Digital Trends 2026: Customer engagement spotlight

2026 AI AND DIGITAL TRENDS IN CUSTOMER ENGAGEMENT

Progress and Pressures in AI-Powered Customer Engagement

Agentic AI is reshaping customer interactions while exposing a gap between ambition and reality. Organizations are setting bold expectations for real-time personalization, but success hinges on closing gaps across data readiness, analytics infrastructure, and alignment from leadership to execution.

This year's report surfaces insights on how brands are shifting from recognizing agentic AI’s potential to realizing its returns. It explores data such as:

Introduction

Brands are building toward a future where agentic AI manages the bulk of customer interactions and operational workflows, leading to increased employee productivity and creative outputs. Yet our global survey of 3,000 executives and practitioners for the Adobe 2026 AI and Digital Trends report finds that ambitions for AI-powered, real-time personalization at scale are running well ahead of organizational readiness.

Few have the data quality, harmonized profiles, or analytics frameworks required to scale their AI investments. While AI is already leading to improvements across key marketing workflows, teams are struggling to translate those benefits into quantifiable returns to leadership. Persistent misalignment on AI strategy between executives and day-to-day practitioners only compounds these challenges. To effectively scale AI-driven engagement, organizations must focus on closing three gaps simultaneously: data readiness, measurement infrastructure, and executive-practitioner alignment.

Brands Are Striving for Real-Time Personalization Amid Digital Maturity Shortcomings

Organizations are betting heavily on real-time personalization as the future of customer experience (CX), while simultaneously acknowledging that their current digital capabilities may not be equipped to deliver it at scale.

More than half of organizations cite providing more personalized customer experiences as a top AI investment goal over the next 18 months, outpacing other priorities like automating repetitive tasks and workflows, improving data quality and governance, and accelerating content creation (Figure 1). When asked what will define breakthrough CX over the next two to three years, 80% point to highly personalized experiences that anticipate customer needs in real time.

Figure 1

However, many organizations would benefit from improving the technology infrastructure, tools, and integrated capabilities they currently have in place to better deliver on customer experience goals. When asked to compare, 57% of brands consider their own digital CX maturity merely on par with or behind industry peers. Only 36% view themselves as ahead of the curve.

Agentic AI Ambitions Outpace Deployment Readiness

Brands are setting aggressive timelines for agentic AI, but today’s deployment progress points to a more uneven reality. Less than one-third of organizations have moved beyond the pilot stage into organization-wide or cross-functional deployment of agentic AI for customer support (30%) — an area that many expect to automate in the near future. Even fewer have deployed agentic AI to the same degree in marketing content creation and activation (22%) and personalization and recommendation (18%).

Despite their limited progress in scaling agentic AI, a majority of brands anticipate that the technology will directly handle a substantial share of all interactions across customer touchpoints. Within the next 18 months, more than three-quarters believe agentic AI will manage at least half of all customer support interactions. They hold similar expectations for agentic AI’s role in post-purchase support, customer sales and transactions, and conversational engagement (Figure 2).

Figure 2

Plans for agentic AI are equally bold when it comes to optimizing internal processes. Within the same timeframe, organizations expect agentic AI to be assisting employees with research and knowledge retrieval (69%), automating routine customer service tasks (63%), assisting marketing with campaign orchestration (52%), and creating content for marketing campaigns (44%).

Harmonized Data Is a Prerequisite to AI-Driven Engagement

AI can help organizations achieve their vision of real-time personalization at scale, but fragmented data is a major barrier to implementation. Most have not established the data foundations (i.e., quality, harmonization, and accessibility) required to make AI applications effective.

The basic prerequisites remain unmet for a substantial share of organizations. Only 44% say their data quality and accessibility are currently adequate for AI, and roughly half say their ability to advance AI initiatives is limited by their current level of data unification and structure.

Fewer are prepared for agentic AI. Just 39% have a shared customer data platform capable of supporting the widespread adoption of agentic AI, and only 44% have clear data management rules and processes for the technology. When asked to identify their top implementation challenges, 75% cite data integration and quality, ahead of talent gaps (71%) and unclear ROI (68%). Without a strong data foundation, organizations will struggle to deploy agentic AI to effectively manage tasks like generating audience segments, creating complete customer profile overviews, and providing real-time engagement updates.

AI Is Delivering Benefits, but Quantifying Value Remains a Challenge

Teams report experiencing significant gains from their AI applications — especially generative AI — but few have developed the measurement frameworks needed to formally demonstrate the technology’s ROI to leadership.

Nearly two-thirds of organizations have identified practical, high-value AI use cases, and almost half are already using agentic or generative AI for journey design and omnichannel activation to achieve personalization at scale. Generative AI, in particular, is already driving momentum across a range of workflows and operations. A majority of organizations say it has improved the volume and speed of content ideation and production, enabled non-creative teams to generate content, increased employee productivity, and enhanced data-driven decision-making (Figure 3).

Figure 3

Despite the widespread perception of these benefits, demonstrating AI’s value is a persistent challenge. While organizations rank customer satisfaction and loyalty (e.g., Net Promoter Score, retention, churn) as leadership’s most important indicator of AI success — ahead of revenue growth and cost savings — more than half (56%) report that leadership ultimately evaluates AI outcomes purely through a financial lens. This disconnect is compounded by the fact that 52% of organizations struggle to demonstrate measurable returns on AI investments using any CX-related metrics, leaving the case difficult to make on either front.

Closing the Alignment Gap Between Executives and Practitioners

The organizations best positioned to deliver on AI-driven engagement are those where executives and practitioners share a clear strategic vision. Yet only 21% of organizations report that their executives and day-to-day practitioners are very aligned on AI strategy. Nearly half (47%) describe their alignment as partial, and nearly one-third report outright misalignment.

According to those experiencing it, misalignment is driven by factors such as resistance to change or technology adoption (52%), insufficient communication of AI goals and strategies (52%), limited practitioner involvement in strategic planning (39%), and unclear measurement of AI’s value and ROI (39%). Where alignment does exist, it is built on clear communication of AI goals and objectives (72%), collaborative planning and decision-making (69%), and strong leadership support (59%).

Key Insights and Next Steps

Brands are convinced of agentic AI’s potential to reshape how they engage customers, from cross-channel campaign orchestration and real-time journey decision-making to continuous optimization driven by AI-generated analytics. These transformations can drive critical business results: reducing churn, increasing retention, delivering always-on personalization, and fueling growth.

But many lack the structural foundations and operational readiness — including strong measurement frameworks, unified data infrastructure, and executive-practitioner alignment — to keep pace with these ambitions.

Organizations can take key steps to close these gaps and scale AI-driven engagement: