Enterprises are deploying AI agents at scale in 2026, and few can show whether those agents are improving outcomes. After several years of proofs of concept, innovation labs and early AI programs, boards expect a return on investment, operations leaders expect higher throughput, and IT expects fewer tickets. Agents are being deployed in response to that pressure.
In my experience, many of these deployments are based on assumptions about how work is done and not on data about it.
Deployment Decisions Based on Assumptions
Most AI agent deployments start by asking where AI can be used. That question starts from the technology and not from the work. A more useful question is what work AI should perform.
Answering it requires visibility into three things that most enterprises do not have:
- What work is actually performed, as opposed to what the process map says, including what people do inside enterprise applications every day.
- Which tasks should be done by humans, which by agents, and which by both, based on observed execution patterns, complexity and judgment requirements.
- Whether AI improves outcomes after deployment, measured by speed, accuracy, cost and user experience.
Without this visibility, deployment decisions are guesses, and guesses made at scale are expensive.
Limits of Traditional Approaches
Process mining shows how transactions flow through systems. That is useful, but it does not show what a person does when working inside Pega, Salesforce or a legacy mainframe terminal.
Task mining captures screen interactions, but most implementations are narrow. They are tied to specific tools, limited in scope, and disconnected from the broader process context.
What is missing is a layer that connects process-level understanding with task-level execution data. We call this layer Execution Intelligence.
What Execution Intelligence Provides
Execution Intelligence captures how work happens inside enterprise applications: screen navigation patterns, application interactions, context switches, decision points and workflow variations across thousands of users.
With this data, decisions about AI agents can be made at each stage of a deployment:
- Before deployment: Identify which tasks are candidates for full automation (agents replace humans), augmentation (agents assist humans), or should remain fully human.
- During deployment: Monitor how humans and agents work together, and whether agents are speeding work up or adding friction.
- After deployment: Measure Agentic ROI through its effect on handling times, accuracy rates and operational cost.
Combining Execution Intelligence With Process Intelligence
Our partnership with Celonis combines Execution Intelligence with Process Intelligence. The result is a digital twin of the enterprise that allows an organization to:
- Understand how work flows across systems (process layer)
- See how humans execute tasks within those systems (execution layer)
- Deploy AI agents grounded in observed data
- Measure and continuously optimize outcomes
This combination supports operationalizing AI and not only deploying it.
Practices of Organizations Seeing Returns
The enterprises that are seeing a return from AI agents share several practices.
They baseline before they deploy. They measure how work is done today, by real users across real applications, before deciding where agents fit.
They match the deployment to the task. Some tasks need full automation, others need collaboration between humans and agents, and some should remain fully human. The data indicates which applies.
They measure continuously. Agentic ROI is a continuing cycle of deploying, measuring and optimizing.
They keep optimizing. The best-performing AI implementations change as work patterns change, as agents improve, and as new opportunities emerge from the data.
Managing a Blended Workforce
Enterprises will increasingly manage fleets of AI agents working alongside employees. Organizations that can see, measure and optimize this blended workforce will outperform those that cannot. The foundation for doing so is observed data on how work is performed, which is what Execution Intelligence provides, and not a better model or agent framework.
Prabhjot Singh is the CEO of Pyze, the Execution Intelligence company. Pyze helps Fortune 1000 enterprises understand how work actually happens, identify what AI agents should do, and measure Agentic ROI.