Generative AI has swept across corporate landscapes, but the returns have been underwhelming. Nearly eight in ten companies have integrated some form of generative AI, yet the same share report no measurable improvement in earnings. This is the “gen AI paradox”: broad deployment with little economic impact. The problem lies in how AI is used—bolted onto existing systems rather than embedded into the fabric of how work happens.

Most organizations have focused on “horizontal” applications like chatbots and copilots. These tools improve efficiency but in diffuse ways, producing benefits too shallow to influence the P&L. By contrast, “vertical” use cases—those tied to core business functions such as procurement, customer service, or credit analysis—have higher potential but remain trapped in pilot mode. Technical limitations, fragmented efforts, poor data governance, and organizational resistance have all slowed progress.

Agentic AI promises to break this impasse. Unlike static copilots, AI agents are autonomous systems that can plan, act, and adapt. They can handle multi-step workflows, integrate with enterprise systems, and collaborate with people to execute goals in real time. Instead of waiting for prompts, they proactively monitor processes, trigger actions, and optimize outcomes. This shift marks the evolution of AI from a reactive assistant to an active collaborator.

The operational impact is profound. Agents can accelerate workflows by executing tasks in parallel, adapt processes dynamically, personalize outputs to each customer, and scale capacity instantly. They can also make systems more resilient by detecting anomalies early and rerouting operations before disruptions spread. Beyond efficiency, agents unlock new revenue potential—enabling tailored offers in e-commerce, adaptive financial products, or autonomous maintenance in industrial systems that enable pay-per-use business models.

Several case studies illustrate the change. A major bank cut modernization time in half by creating hybrid digital factories where human supervisors managed squads of coding agents. A research firm reduced errors and uncovered hidden insights by using multi-agent systems to detect anomalies in market data. Another bank reinvented the credit memo process, turning relationship managers from writers into reviewers and cutting turnaround time by 30 percent. In each case, the benefit came not from adding AI to existing workflows, but from redesigning the workflow around AI agents.