AI strategy for growing businesses
How to build an AI business case the board can use
Turn an attractive technology idea into a commercial decision with evidence, ownership and a credible route to value.
Define the business job before the technology
Name the user, workflow, constraint and commercial consequence in language the operating team recognises. This matters because AI business case decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. If the problem cannot be observed or measured today, the case needs diagnosis before investment. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Establish a truthful baseline
Measure current time, cost, delay, quality, error, capacity and opportunity loss without pretending every benefit will become cash. This matters because AI business case decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Separate recoverable cost, released capacity and strategic value so the board can judge each claim properly. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Model the whole cost of change
Include data work, integration, assurance, supplier input, human review, training, adoption and continuing maintenance. This matters because AI business case decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Compare the proposed route with improving the process through ordinary automation or clearer operating practice. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Make uncertainty investable
Use ranges, assumptions and evidence gates instead of one precise return figure built on fragile inputs. This matters because AI business case decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Agree what the first phase must prove before a larger commitment is released. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Put ownership beside the economics
Name the business owner, technical owner, affected users and executive decision-maker before the work begins. This matters because AI business case decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Do not approve a use case that everyone supports but nobody is prepared to own. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
End with a decision, not a presentation
State the recommended move, alternatives considered, immediate actions, review date and explicit stop conditions. This matters because AI business case decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. A useful board paper reduces the number of unresolved choices rather than displaying the volume of analysis. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
What to carry into the work
- If the problem cannot be observed or measured today, the case needs diagnosis before investment.
- Separate recoverable cost, released capacity and strategic value so the board can judge each claim properly.
- Compare the proposed route with improving the process through ordinary automation or clearer operating practice.
- Agree what the first phase must prove before a larger commitment is released.