Banking, Retail and Tech Leaders Align on AI Agents’ High-Impact Use Cases

But findings in the January 2026 edition of The CAIO Report from PYMNTS Intelligence reveal that, across industries, companies are converging on the same handful of high-impact uses for agentic artificial intelligence (AI).

Rather than fragmenting into niche or industry-specific uses, the report found agentic AI adoption is clustering around a common set of high-leverage functions: customer insight, product lifecycle management and strategic analytics. Executive interest in these areas among those surveyed typically exceeds 80% across industries, especially tech where it approaches percentages in the low 90s.

What ties these use cases together is not the function itself, but the nature of the work. These are areas where insight depends on synthesizing diverse inputs and coordinating across boundaries. Traditional software can struggle here. Humans can struggle too.

Autonomous agents, in theory and increasingly in practice, are well suited to fill the gap.

Agentic AI Sheds Task Bot Label and Eyes Corporate Infrastructure

For much of the past decade, enterprise AI followed a predictable script. Companies tested narrow use cases, launched pilots with limited scope, and spoke carefully about “assistance” rather than autonomy. Even as generative AI exploded into the mainstream, most organizations treated it as a productivity enhancer, not core enterprise software.

Agentic AI is causing businesses to rethink that assumption. The report found that corporate leaders increasingly see artificial intelligence agents as a horizontal layer, or a system that reasons across departments, coordinates workflows, and takes action without being boxed into a single function. The impact is less “digital intern,” more “always-on operating system.”

Customer insight is a prime example. Most companies collect far more feedback than they can realistically analyze. Support tickets, reviews, surveys and usage data pile up faster than teams can synthesize them. Autonomous agents promise to change that dynamic by continuously scanning inputs, identifying patterns and surfacing emerging issues in near real time. The appeal is less about replacing analysts and more about closing the gap between signal and response.

This architectural shift mirrors earlier transitions in enterprise technology. Cloud computing replaced bespoke infrastructure. Platforms replaced standalone applications. Now, intelligence itself is being centralized and abstracted. That move makes it easier to scale, govern and improve agentic systems over time.

Read the report: Agentic AI Breaks Out of the Sandbox

Why Everyone Is Focusing on the Same Things

The convergence around customer insight, product lifecycle management and strategic analytics is not driven by fashion. It reflects a growing consensus about where agentic AI can move the needle.

Product lifecycle management sits at the center of that consensus. Modern products generate data at every stage, from early research through launch and iteration. Yet that data is often fragmented across tools and teams. Agents that can track performance, flag risks and coordinate across engineering, design and marketing promise faster iteration and fewer blind spots. For leaders under pressure to shorten development cycles, that capability is hard to ignore.

Strategic analytics pushes autonomy further up the value chain. Here, agents are expected to do more than summarize dashboards. They frame questions, run scenarios and suggest actions across occasions where the sheer complexity of modern decision-making has made some level of machine assistance unavoidable.

It’s still early innings for agentic AI applications. But, as the report found, the outlines of the agentic enterprise are increasingly coming into view. In this model, autonomous agents form a connective layer across the organization, continuously translating data into insight and insight into action. Humans remain central, but their focus shifts toward judgment, creativity and values.

Whether this vision fully materializes will depend on technology, regulation and culture. What seems clear is that autonomy is no longer a fringe idea. The convergence around a shared playbook suggests that many businesses see agentic artificial intelligence as a foundational capability, not a passing trend.