AI Opportunity Discovery

By Juan Beltrán, Industrial B2B AI Transformation Executive.

AI Opportunity Map starts with a process, its outcome and an observed constraint. It compares interventions and records evidence, deployment conditions and economics before choosing the next measurement or pilot.

Start with a process and a performance gap. Investigate the cause before choosing technology. AI Opportunity Map makes alternatives, assumptions and the next experiment explicit.

Use processes to discover, interventions to compare and process outcomes to judge success. An individual task can be the implementation point when its improvement affects the outcome.

Evaluate my AI transformation leadership

For companies assessing an AI transformation leadership appointment: review the public operating case, the proposed mandate and the responsibilities your enterprise needs.

Key takeaways

Friction is a starting observation, not proof of AI suitability.
Compare process repair, integration, rules, predictive models and generative AI.
Keep evidence confidence and opportunity potential separate.

Decision brief

Decision criteria

A defined process, outcome, baseline and owner.
A causal explanation for the expected improvement, compared with alternatives.
Deployment conditions considered separately from potential value.

Evidence to check

Claim provenance: source, date, sample or scope and owner.
Net benefits after review and rework, adoption and total costs.
Representative evaluation, error controls, dependencies and next experiment.

What I would do Monday

Choose one process with a known performance problem. Define its boundary and owner, inspect a sample of cases and compare at least one AI intervention with a simpler alternative.