From Pilots to Profit: How to Turn AI Experiments Into Real ROI

The Reality Check

According to a McKinsey report (November 2025), almost nine out of ten companies say they already “use AI” somewhere in the business. But most see no ROI.
So the problem isn’t adoption.
The problem is turning experiments into business change.

From the latest data, two things are clear:

1. Most organisations are stuck in pilots. AI is live in one or two use cases, but it’s not scaled into core workflows or the P&L.
2. A few are “AI high performers.” They think differently: setting bold goals around growth and innovation, redesigning work around AI, and having senior leaders personally sponsor the change.

Why Your AI Experiments Aren’t Moving the Needle

• AI is framed as an efficiency project, not a transformation lever.
• Workflows aren’t redesigned—AI is just bolted on.
• There’s no clear owner at the top, and no one is signing those investment cheques.
• Data is still fragmented, so every pilot hits the same integration wall.

How to Win: Two Paths Forward

If You’re Experimenting but Not Seeing ROI

1. Raise the ambition.
   Stop asking “where can we save a bit of time” and start asking “which product, service, or process could we reimagine with AI?”
   High performers target growth, new offerings, and new ways of working—not just cost cutting.
2. Redesign at least one workflow end-to-end.
   Pick a real process, not a demo: sales qualification, customer service, claims handling, field maintenance—whatever matters for your business.
   Put AI at the centre, define the human in the loop, and change roles, KPIs, and interfaces to match.
3. Treat AI as a product, not a project.
   Give it an owner, a roadmap, and a budget. High performers invest a serious share of their digital budget into AI and iterate fast instead of running disconnected proofs of concept.
4. Industrialise data and platforms as you go.
   You don’t need a perfect data platform before you start.
   But stop building one-off pilots on spreadsheets and shadow systems. Standardise on a stack, create reusable data products, and make user feedback part of how the system learns.

If You’re Still Preparing and Not Really Experimenting Yet

1. Get alignment at the top.
   Make sure leadership sees AI as a strategic advantage, not just cost efficiency. Tie AI to mission, vision, and a small set of growth objectives.
   If AI is only a side topic in strategy conversations, it will stay stuck. Meanwhile, employees will adopt AI tools informally—without governance or impact.
2. Get your data into a connectable state.
   Don’t wait for the mythical “perfect harmonisation.” Start by connecting the most important sources so you can experiment, then improve quality as you learn.
   AI can synthesise across messy data if you design for it.
3. Get help.
   You don’t have to design the first wave alone. Work with a partner that has already built AI-powered workflows and agents for organisations like yours. They bring patterns, guardrails, and hard lessons from other people’s pilots.
4. Get the right people in the room.
   Include innovators, influential contributors, and forward-looking operators—not just IT and management. These are the people who will actually use, stress-test, and champion the tools.
5. Plan for change and expectation management.
   Even with good design, you’ll see pushback, bugs, misuse, and fear.
   Set up support, communications, and training for people who are sceptical or worried about job impact. Make it safe to learn and to say when something doesn’t work.

The Bottom Line

The gap between “we have some AI pilots” and “AI shows up in our P&L and customer experience” is widening every quarter.
If you want a pragmatic path from slideware to shipped workflows, start now. The companies that move fast will define the next wave of growth.

Found this useful? Share it with your team or peers who are stuck in pilots.
Do you want to compare notes or see what real-world AI transformations look like? Reach out to me and let’s talk!