AI will become part of how companies compete. The difference will not be who has access to the tools. The difference will be who is able to apply them with structure, judgement and commercial relevance.
TREC Advisory uses AI as a working method across finance, treasury, risk, energy and commodities. It helps accelerate analysis, structure complex information and challenge assumptions. The value comes from combining technology with specialist knowledge, critical thinking and the ability to turn insight into actions that work in practice.
Not a layer added on top — connected to the work itself.
My approach is pragmatic. I do not see AI as a separate layer that should be added on top of the organisation. It has to be connected to the work already being done: decision processes, reporting, governance, risk assessment, liquidity planning, commercial priorities and execution.
That is also where many AI initiatives fail. They start with the tool instead of the business problem. They produce output, but not necessarily better decisions. They create activity, but not always value.
In finance, treasury, risk, energy and commodities, the important decisions are rarely clean. Data can be incomplete. Markets can move faster than the process. Liquidity, capital, risk and commercial priorities do not always point in the same direction. In those situations, AI can improve the decision basis, but it cannot replace judgement.
The output still needs to be challenged. Assumptions need to be tested. Risk needs to be understood. Governance needs to be clear. And the final recommendation has to be translated into something management, boards and organisations can act on.
That is how I use AI.
The new electricity.
The point is useful because AI is becoming a general-purpose capability, not a narrow technology topic.
For companies, the question is therefore not whether AI should be used. The question is where it improves the work, strengthens decisions and creates measurable business value.
That requires more than tool access. It requires clear use cases, governance, AI literacy and people who understand both the technology and the business context.
