Most organisations do not have an AI problem. They have a prioritisation problem. The technology is capable of far more than any single business needs, and the hard work is deciding where it earns its place. The useful edge of AI is the narrow band where a capability is genuinely better than the status quo, cheap enough to run, and safe enough to trust.
Start with the decision, not the model
The wrong question is "where can we use AI?" The right one is "which decisions are slow, repetitive or inconsistent today?" Document review, demand forecasting, customer triage, anomaly detection in operations — these are decisions, made many times a day, where a small improvement compounds. Anchor to the decision and the right technique usually becomes obvious.
The three tests before you build
A use case is ready when it passes three tests. First, value: does improving this decision move a number the business already cares about? Second, data: do you have enough clean, accessible history to learn from? Third, tolerance: what is the cost of being wrong, and can a human stay in the loop where that cost is high? Anything that fails these tests belongs on a watch-list, not a roadmap.
Buy the commodity, build the differentiator
Language understanding, transcription and image recognition are commodities now — buy them. Reserve your build effort for the models and workflows that reflect something only your business knows. That discipline keeps cost down and focus high.
Govern from day one
Applied AI without governance is a liability waiting to surface. Decide early how you will handle data privacy, model monitoring, human oversight and auditability — the same rigour you already apply to security and data. Governance is not the brake on AI adoption; it is what lets you accelerate safely.
The organisations that win with AI are not the ones with the most models. They are the ones that found the useful edge early, proved it, and expanded from evidence rather than hype.