Before you invest in AI, find the workflow that creates the most business value first.
This is the framework behind everything on this site: ten criteria for judging an AI idea, and a simple matrix that turns a pile of ideas into a sequenced roadmap.
The order you build in matters more than what you buy.
Most AI regret starts the same way: a tool got chosen before a workflow did. The fix is unglamorous — evaluate candidate workflows against consistent criteria, rank them, and let the ranking pick your first build. Thirty minutes of scoring routinely saves a quarter of wasted implementation.
Ten questions to ask of every AI idea.
Value against feasibility — four honest buckets.
Average your value criteria (impact, frequency, strategic value, speed to validate) and your feasibility criteria (clarity, data, feasibility, risk, review, integration), then place each candidate:
Start here. High-impact workflows you could ship in weeks. One of these becomes your first initiative.
Worth doing, not first. Sequence them after a quick win has built skills, trust, and cleaner data.
Easy but pointless. Fine as learning exercises; dangerous as your definition of "doing AI".
Expensive and unimportant. Revisit only if the business changes shape.
Four steps, one afternoon.
Common questions
Why prioritize before building anything with AI? ›
Because the most expensive AI mistake is building the wrong thing well. Prioritization is how you find the workflow where AI creates real business value first — before you spend money on tools or implementation.
How many AI initiatives should a business run at once? ›
One, for most SMEs. A single initiative shipped into real use builds skills, data, and trust that make every later initiative cheaper. Parallel experiments usually starve each other.
What if every candidate scores badly? ›
That is a useful result: it usually means process clarity or data availability is the real bottleneck. Fix that first — the AI Readiness Check will tell you if that is where you are.