A quiet observation from the last 12 months of AI consulting work:
Most teams don't fail because their AI ideas are bad. They fail because they try to ship 10 AI ideas at once.
A simple opportunity-mapping move that helps:
1. List every recurring decision your team makes weekly (not tasks - decisions).
2. For each one, score: frequency x cost-if-wrong x data-availability.
3. Pick the top 2. Ignore the rest for now.
4. For each, ask: what would have to be true for an AI assist to be net-positive here?
5. The first thing you build is the smallest possible version of that.
Most "AI strategies" we see are really feature wish-lists. The teams getting real ROI are the ones treating it as decision-loop engineering: one tight loop, shipped, then the next.
Curious - what's the recurring decision in your business right now that costs the most when you get it wrong? That's almost always where the first useful AI lives.