Embracing AI and Automation: A Step-by-Step Guide for Small Businesses
Choose a useful first task, review the information and people it depends on, then use a focused pilot to decide what to do next.
Reviewed and updated 22 October 2026. Original publication date retained.
Choose one useful piece of work
Start with a task your team understands and would like to improve. Look for repeated enquiries, information that is hard to find or manual handovers between systems. Ask someone to show a typical case and a difficult one. This gives the discussion a practical foundation and helps identify whether AI, automation or a simpler process improvement is worth exploring.
Review the information and systems
Find out where the task's information lives, who maintains it and who should be able to use it. Check whether existing software already provides part of the answer. For a knowledge service, review source material and access. For a workflow, look at identifiers, interfaces and what happens when information is missing. These foundations help define the scope of a sensible first test.
Bring the team into the decision
People doing the work can explain exceptions and informal workarounds. Involve them in choosing examples and reviewing the proposed approach. They also need guidance on what to check and where to seek help. The government AI-upskilling research is a useful source for planning workforce support. Learning should be connected to the roles and tasks involved.
Run a focused test
Agree what the pilot should tell you, who owns it and how the results will be reviewed. Use representative cases and a small set of measures, such as task completion, answer quality, checking effort and successful handover. Include the work needed to keep the service useful. Avoid treating a demonstration or a time-saving estimate as proof of a wider result.
Make the next decision with better evidence
The next step might be a wider rollout, more preparation, a different product or an improvement to the existing process. Each can be a useful outcome when the review makes the reasons clear. Read about our AI opportunity review, data and systems readiness and people, skills and adoption work before choosing where to begin.
