From AI opportunity
to operating value
Building a repeatable way to decide where AI belongs, what deserves investment, and how proven solutions reach real operations.
Find what limits the outcome before choosing the technology.
In complex organisations, the largest visible inefficiency is not necessarily the most valuable place to intervene. I start with the business system: the outcome that matters, what currently constrains it, and which decision or process is creating that constraint.
“Where can AI be used?” is usually the wrong first question.
The stronger question is: what limits the outcome — and what intervention actually changes it? Sometimes that intervention is AI. Sometimes it is software, process redesign, better data, or no intervention at all.
Increase commitment as uncertainty falls.
A prototype can prove that something can work. It does not prove that the organisation should invest in it. Early learning should be cheap. Larger engineering, data and organisational commitments should follow stronger evidence.
From “automate maintenance” to a second pair of eyes for engineers.
The opportunity initially looked obvious: maintenance was costly, expert knowledge was unevenly distributed, and troubleshooting could be improved with AI. But the goal was not to automate the most inefficient task.
The product vision became a copilot — not a replacement.
The system was designed to surface relevant history, likely causes and troubleshooting information while keeping the maintenance engineer responsible for the decision. The work also made data readiness part of the product problem.
Not every valuable problem deserves bespoke AI.
I used different investment paths depending on differentiation, maturity and the economics of ownership.
The goal is not to maximise the amount of AI in the organisation.
Understand the outcome before selecting the intervention.
The largest visible inefficiency may not be the system constraint.
A working prototype is not yet a business case.
Learn cheaply before making expensive organisational commitments.
Deployment alone is not operating value.
Not building can be the right AI investment decision.
The real job is to make better decisions about where AI creates durable operating value.