Spot what matters. Test what works.

New AI tools often repackage familiar capabilities in a better interface. That can still be valuable: a well-placed feature may change behaviour more than a stronger underlying model. Evaluate what becomes easier, safer or newly possible, rather than whether the launch calls itself revolutionary.

1. Watch capabilities, not launch volume

Enterprise search and grounded answers connect business documents to conversational retrieval. Tools such as Glean and Microsoft 365 Copilot illustrate this category. The critical questions are permission inheritance, index freshness and citation quality. An answer should point to evidence the current user is allowed to access; a citation alone does not prove the claim matches the source.

Coding assistants, including GitHub Copilot and Cursor, can help navigate repositories, draft changes and suggest tests. Assess accepted changes, defect rates and review effort rather than generated lines of code. Require normal security checks and code ownership. A large patch created quickly can be slower to understand than a small patch written manually.

Meeting and voice tools turn conversations into transcripts, summaries and proposed actions. Evaluate speaker attribution, proper nouns and whether the summary distinguishes an idea from a commitment. Recording laws, employee consultation and participant notice vary by location. Set appropriate permissions and retention before a bot joins sensitive calls.

Browser and computer-use agents interact with interfaces where stable APIs may be absent. They can bridge a legacy process, but layouts change, sessions expire and pages contain untrusted instructions. Keep early trials in isolated accounts with synthetic data. Purchases, messages and destructive actions need explicit controls.

2. Treat interoperability as plumbing

The Model Context Protocol, or MCP, standardises aspects of how AI applications connect to tools and contextual resources. It can reduce custom integration work and make capabilities easier to expose. It does not make a connected server trustworthy, guarantee compatibility across every feature, or decide which records a user should see.

Inspect each server's publisher, requested permissions, network access and update process. Authorisation still belongs at the receiving service. Keep credentials scoped, review tool descriptions and log actions. A standard connector can accelerate both a good integration and a badly permissioned one.

3. Run a two-week pilot with a baseline

Choose one role, one task and a small group of volunteers. During the first few days, measure the existing process: elapsed time, active work time, error rate and downstream rework. Pick representative tasks rather than only examples that flatter the tool. Avoid introducing confidential data until security and contract reviews are complete.

In the second phase, compare tool-assisted work with similar unassisted tasks. Rotate users or task sets where practical to reduce the effect of individual skill. Record setup, prompting and review time. Ask reviewers to score outputs without knowing which method produced them when that is feasible.

These thresholds are examples, not universal standards. A high-risk financial workflow needs stricter validation than internal brainstorming. Define stop conditions in advance: a serious data exposure, an unapproved transaction or repeated fabricated evidence should pause the pilot, not become an anecdote in the final presentation.

4. Decide: adopt, adapt or stop

Calculate value from observed use. If 12 employees each save 30 minutes weekly, that is approximately 26 hours a month. At an assumed loaded cost of £35 per hour, the gross capacity value is about £910. Subtract licences, administration and additional review. Released capacity is not automatically a cash saving unless the business can use it productively.

Adopt when quality, economics and control all meet the agreed thresholds. Adapt when the capability is promising but the workflow needs a narrower scope. Stop when the benefit depends on heroic prompting, constant correction or access you cannot justify. A documented rejection is a useful outcome because it prevents repeated, unstructured experiments.

Explore MCP documentation, Glean, GitHub Copilot and Cursor for current product details.