AI strategy for growing businesses
AI readiness assessment for growing businesses
Assess whether the organisation can turn AI ambition into safe, owned and commercially useful work.
Start with strategic readiness
Clarify which growth, margin, customer, capacity or resilience pressure AI is expected to address. This matters because AI readiness assessment 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 generic desire to adopt AI is not yet a usable strategic mandate. 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.
Test the quality of the use cases
Review whether each idea has a defined job, user, baseline, owner and route to evidence. This matters because AI readiness assessment 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. Reduce a long opportunity list to the few cases that are both valuable and testable. 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.
Examine data and workflow reality
Look at where information lives, how consistent it is and how the current process handles judgement and exceptions. This matters because AI readiness assessment 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 mistake access to a model for readiness to change a working process. 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.
Assess governance in proportion
Check approved tools, restricted data, human review, supplier assurance, escalation and incident ownership. This matters because AI readiness assessment 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. Controls should rise with consequence rather than treating every prompt as the same risk. 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.
Measure operating capacity
Identify who can lead discovery, make decisions, support users and maintain the changed workflow after launch. This matters because AI readiness assessment 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 all capacity belongs to the day job, the roadmap needs fewer initiatives or additional support. 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.
Turn assessment into a sequence
Translate the findings into now, next and later moves with dependencies, owners and evidence gates. This matters because AI readiness assessment 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. The output should make the first sensible action obvious rather than leave the business with another maturity score. 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
- A generic desire to adopt AI is not yet a usable strategic mandate.
- Reduce a long opportunity list to the few cases that are both valuable and testable.
- Do not mistake access to a model for readiness to change a working process.
- Controls should rise with consequence rather than treating every prompt as the same risk.