Begin with work, not tools
Adoption stalls when organisations roll out a tool and wait for value to appear. A better approach is to map recurring tasks such as drafting, summarising, analysing and researching, then test where AI assistance genuinely saves time or improves quality, and where it does not.
Understand the limitations
Generative models produce fluent output that can be wrong, outdated or biased. Users must treat outputs as drafts, verify facts against reliable sources and remain accountable for anything they send or decide.
- Verify factual claims, figures and references before use.
- Never paste confidential, personal or client data into tools that are not approved for it.
- Keep a human accountable for every decision that affects people or money.
- Record where AI materially contributed to an output when that matters to the reader.
Set clear, usable guardrails
Policies work when people can apply them in seconds. A short acceptable-use guide, a list of approved tools, data classification rules and an easy route to ask questions are more effective than a long document nobody reads.
Leadership decisions on AI
Executives do not need to understand model architecture. They need to judge use cases on value, risk and readiness, ask the right questions of vendors, fund data quality and skills, and set the organisation's risk appetite for automation.
Measure and scale deliberately
Pilot with a defined baseline, compare results honestly and scale only what works. Share prompts, patterns and lessons across teams so capability grows faster than tool licences.