Stop choosing the wrong Agentic AI use cases

Date:
Wed, 21 Oct 2026 10:19:41 +0000

Description:
Why businesses must rethink agentic AI before scaling costly, ineffective use cases.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter 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. Pete Evans Social Links Navigation

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, whats the question? By the time it comes to looking at results and whether the investment in AI has been fruitful, its hard to identify whats been achieved and why achieving it was important in the first place. Latest Videos From TechRadar Watch full video here: 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. You may like The next AI phase is better agents not bigger models Beyond AI adoption: what it takes to deliver measurable business value 'The test isnt whether AI can do something. Its whether it can make the process measurably better': We hear why businesses need to be more selective about where theyre using AI

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 its 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 . Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners
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Conditions and Privacy Policy and are aged 16 or over. Know your workflow If organizations cant 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 couldnt have completed a job that takes ten. What to read next The task trap: Why AI's biggest promise has nothing to do with speed How SMBs turn AI into lasting business value The cost of being half-hearted in AI and how to avoid the Solow Paradox 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, its 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. We've featured the best AI website builder. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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