AI Strategy
An AI strategy is the plan that turns AI from scattered experiments into a coordinated capability: which processes you automate, in what order, with what data, budget and governance, and when you stop.
No jargon, no math
In plain words, what is it?
An AI strategy turns AI from a pile of scattered experiments into one coordinated plan. The mechanism is boring on purpose. First you map the processes where time and money leak: quoting, invoicing, scheduling, quality checks. Then you score each one by value and effort, and you pick a single pilot: the cheapest process where a win is visible within weeks. The pilot runs against a baseline you measured before starting. If it hits the target, you scale it and pick the next process; if it misses, you fix data and skills before touching anything else. The strategy part is the order and the stopping rules, not the technology. Where it breaks: no owner, no baseline, a slide deck instead of a pilot.
- 01
Map. List the processes where hours and money quietly leak.
- 02
Score. Rank them by value and effort; pick one pilot, not five.
- 03
Pilot. Build the smallest version that proves the mechanism works.
- 04
Measure. Compare it with the baseline KPI you recorded before starting.
- 05
Scale or fix. Hit the target and scale with governance; miss it and fix data first.
Terms that live nearby.
AI Readiness
AI readiness measures how prepared an organisation's data, processes, people and governance are to run AI in production, and it is assessed before any model is deployed.
ReadAI ROI
AI ROI is the money an AI system returns compared with everything it costs to build and run it: savings and new revenue weighed against licences, integration, data work and human oversight.
ReadAI Governance
AI governance is the set of policies, roles and controls that decide how an organisation may build, buy and use AI: who approves a use case, what data a model may see, how incidents are handled, and who is accountable when something goes wrong.
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