AI Overload → Strategic Use-Cases, Not Enterprise Fantasies

What’s happening

Across strategy and transformation teams, leaders are quietly shifting from “enterprise-wide AI ambition decks” to a shortlist of high-confidence, high-value use-cases. Why? Because the last 12 months created AI overload: too many pilots, unclear ownership, and a gap between excitement and execution. Now the pressure is on to prove value fast, not run another 30-page “AI potential” workshop.

Why it matters

The organisations actually getting ROI from AI are the ones ruthlessly focusing on:

• Clear workflows (not abstract capabilities)

• Measurable outcomes (not theoretical upside)

• Change adoption (not more models)
  

In recent surveys, fewer than 15% of AI pilots make it into production, and the common failure point isn’t tech. It’s missing business ownership, poor data foundations, and no defined success metric.

What to do this week (3 moves)

1. Name your “Big Three” AI use-cases

Pick three use-cases where:

• the data already exists,

• the workflow is understood,

• the outcome can be measured in 30–60 days. No more than three. More = fantasy.
  

2. Assign business ownership, not technical ownership

For each use-case, name a single business owner who’s accountable for value creation. AI only sticks when it belongs to the business, not to “innovation”.

3. Define one adoption metric

Not accuracy. Not model performance.Adoption. E.g., “% of decisions supported by the model”, “% of frontline using the assistant daily”, “time saved per workflow”. If adoption is <20% after 60 days, pause and fix the workflow, not the model.