Psychologist Abraham Maslow once warned: “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.” Agentic AI is becoming the enterprise version of that hammer.
Once people understand what agentic AI can do, the list of possible use cases grows quickly.
VP of AI transformation at Atos.
While that enthusiasm should be applauded, it can lead to strategies that are back to front “If AI is the answer, what’s the question?” By the time it comes to looking at results and whether the investment in AI has been fruitful, it’s hard to identify what’s been achieved and why achieving it was important in the first place.
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Start with the value, not the agent
In the rush that followed ChatGPT, many organizations deployed AI tools to their workforces at large with no defined value or end goal. People started working differently almost immediately, but with no baseline captured, there was no way to know what had actually improved. Who knows how long it takes to write a coherent email and who knows what time is saved by having ChatGPT rephrase it for you!
This is what McKinsey calls the “gen AI paradox”. Its 2025 survey found just 39% of organizations reported any enterprise-level financial impact from their AI investment, despite mass adoption.
Organizations need to be precise about what they want to measure. There are three layers to this and most only capture the first.
- Adoption: are people using it?
- Behavior: are people doing their jobs differently?
- Outcome: revenue per salesperson, tickets closed per engineer, cycle time on a review gate.
Adoption and outcome together show a correlation; for example, people who use AI sell more. But it’s the, often missed, behavior layer in the middle that turns that correlation into causation, i.e. they sell more because they are now able to use AI to fill in more client reports, leaving more time to hit the phones.
Know your workflow
If organizations can’t see how work is really being done, two things happen. The baseline measures the wrong process, making any claimed improvement meaningless and an agent given the wrong steps cannot complete the job.
One procurement process at Atos looked like a four-step workflow on paper, but when we looked carefully and asked those doing it, it was closer to 10. People moved between Enterprise Resource Planning (ERP), Outlook and spreadsheets, copying information, waiting for replies and re-entering data manually.
A baseline on four steps would have measured the wrong process and an agent given four steps couldn’t have completed a job that takes ten.
Not every problem needs an agent
Understanding the workflow also shows whether agentic AI is the right technology at all. If tasks follow fixed rules “if this happens, do that” the answer may be Robotic Process Automation (RPA), workflow automation or a process change.
Agentic AI is better suited to workflows where the system needs to interpret context, coordinate across tools, make recommendations or act within defined boundaries. Even then, it needs a clear measure of success and a credible business case. The economics of deploying Agentic AI are still in infancy, but this will act as a natural filter for only the most valuable cases.
Prove the value before building
Value is where the tough work of a clear business case comes in. Proving the value of Agentifying a process before any code is written.
This requires access to good data sets. Evidence on cost and business value before introducing Agentic AI is needed before budget is committed. Where the benefits are measurable and enough open data exists, it’s possible to build something real in a week. Where the data still requires work, it is always worth refining the data you have before attempting to justify a business case.
Work managed upfront will save cost and time and give you evidence of value.
Build a portfolio, not a queue
Once there is a shortlist of use cases, organizations can prioritize against financial impact, feasibility, process impact, technology requirements, human impact and risk. Scoring across multiple factors rather than ranking on ROI alone creates a portfolio instead of a queue. A lower-value use case with fast impact might prove value early and build credibility for agents that follow. A high-value use case with a two-year runway may need to wait.
Budget for a new mortgage
Even once the right case has been selected, many organizations still struggle to see impact because they focus on the build, then under-resource the work needed to make the change land.
Gartner calls this a “transition mortgage”, where AI adoption demands more support than traditional technology, often requiring 25% more training effort and up to 200% additional change management.
In our own organization, we were guilty of building independent tools in different regions and teams to solve the same business problem. We have now established clearer oversight of our use cases. The issue was not the quality of the tools, but duplicated effort, cost and resources.
Not every workflow needs a hammer
C-suite leaders should start with fewer use cases where the value and workflows are clear, the baseline is measurable, the data is available and the role of the agent is well defined. Then they should budget for communications, adoption, measurement and scale.
The value from agentic AI is not measured by the number of agents an organization deploys. It is measured by whether the work changed and whether the organization can prove it. Agentic AI is a powerful hammer, but success lies in understanding which problems are nails and which are not.
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Pete Evans
