what it is

AI Enablement is the step where AI use becomes real, controlled, and repeatable—not just experimentation. The focus is on selecting feasible use cases, implementing the operating basics (guardrails + ownership), and shipping a first capability that is safe and measurable.

This can be internal (operations/productivity) or customer-facing (academy tutor, insights automation)—depending on readiness and risk.

How it works (3 steps)

1. Use-case selection (feasibility first)

   We shortlist and score use cases by:

• value impact,
• data availability,
• operational complexity,
• privacy/risk level,
• time-to-deliver.

1. Guardrails + operating model

   We define:

• approved tools and data sources,
• access boundaries and what’s not allowed,
• human review rules,
• logging/audit needs,
• incident handling (what happens when AI is wrong).

1. MVP delivery + measurement

   We implement a first version with clear KPIs (adoption, time saved, quality, safety), then iterate.