When an organisation says “we need an AI strategy”, it often means “we need to decide what to do with AI”. The best answer rarely starts with the technology.
Begin with where work is hard
Ask where people lose time, where errors are costly and where decisions wait on information that exists but is hard to reach. Those are places AI can help: answering questions from documents, preparing drafts, matching records, spotting anomalies, forecasting demand. The question to ask is not “where can we use a language model?” but “which of our processes would be noticeably better if this step were faster, cheaper or more accurate?”
McKinsey’s State of AI research has repeatedly found that redesigning workflows is among the factors most associated with seeing financial impact from generative AI. Bolting a tool onto an unchanged process tends to disappoint.
Prioritise honestly
For each candidate use case, look at four things.
- Value: what outcome improves, for whom, and how would you measure it?
- Feasibility: is the technique mature enough for this task at the quality you need?
- Data readiness: does the data exist, is it accessible and is it good enough?
- Risk: what happens when the AI is wrong, and who is accountable?
A use case that scores well on value but badly on data readiness is not a bad idea. It tells you what to fix first.
Plan beyond the prototype
Before building, outline how the solution would connect to your systems, who will own it, how it will be secured and how its quality will be monitored. The AWS Cloud Adoption Framework is a useful checklist: it looks at an AI programme from business, people, governance, platform, security and operations perspectives.
How we approach it
Our AI Strategy & Discovery work follows this sequence: listen, define, assess, prioritise and plan. You can take the roadmap forward yourselves, or have us prototype and build with you.