Every executive team we meet is being told two contradictory things: AI will reshape their industry within 18 months, and most AI initiatives never reach production. Both are true. The reason is rarely the model — it is the operating model around the model.
Hype cycles produce pilots. Pilots produce slide decks. Slide decks rarely produce P&L impact. To move from experimentation to outcomes, AI has to sit inside a delivery operating model with the same rigour you would apply to a CRM rollout or a regulatory programme.
We work with leadership teams to define three things before any vendor conversation: the decision the AI is meant to improve, the human-in-the-loop checkpoints around it, and the operational data flows it depends on. If those three are vague, the pilot will be vague.
From there, governance becomes the unlock. Not heavy committees — light, weekly forums where the people closest to the work can flag drift, bias, or cost runaway early. The teams that move fastest with AI are the ones who built feedback loops first.
Our recommendation: pick a single revenue or cost lever, instrument it end-to-end, and only then introduce the model. Transformation that compounds is boring on the surface and powerful underneath.