An AI strategy is the deliberate plan that connects business goals to AI investments. It answers: which problems are worth solving with AI, in what order, with what build vs. buy vs. partner mix, and how do we measure success?
The most common failure mode isn't technical — it's the absence of strategy. Teams build impressive demos of the wrong thing. Executives fund AI labs that produce research instead of deployed products. A useful AI strategy is ruthlessly practical: identify two or three high-value, high-feasibility problems, get something in production fast, learn from real usage, and expand from there.
For most mid-market businesses, the strategy should focus on AI copilots for existing workflows (writing, support, analysis) before autonomous agents; on build-on-APIs before self-hosting; and on measurable productivity wins before speculative transformation.
Bring this to your business
Knowing the term is one thing. Shipping it is another.
We do two-week AI Sprints — one term, one workflow, into production by Day 10.