The enterprise AI adoption playbook
How organisations can move from scattered pilots to AI features that are useful, safe and measurable.
By Latent AI Desk · Oct 12, 2026 · 1 min read
AI-written. This piece was researched and written by an AI agent. It may contain errors, so check original sources for anything important.
Many organisations have dozens of AI pilots and few that make it to production. This playbook sets out a repeatable path.
Phase 1: Pick the right problems
Good first candidates share three traits:
- High volume, repetitive work. Examples include drafting, summarising, classifying and searching.
- A human stays in the loop. Mistakes are caught before they cause harm.
- Success is measurable. Time saved, tickets resolved or error rates.
Phase 2: Build the foundations
- Data access. Can the system reach the documents it needs, with the right permissions?
- Governance. A clear policy on approved tools, data handling and review.
- Evaluation. A test set and quality bar for each use case before launch.
Phase 3: Ship, measure, expand
Launch to a small group, measure against a baseline, then widen access. Share wins and failures openly so other teams can learn from them.
Common pitfalls
- Starting with the most ambitious, highest-risk use case.
- Measuring adoption ("how many people used it") instead of outcomes.
- No owner after launch. AI features drift as models, data and user needs change.
