AI Beyond ChatGPT: Ideas for UK Business

AI beyond ChatGPT for business: why the familiar examples only tell part of the story

Drafting a customer email or summarising a document can be useful. But those tasks are relatively isolated. Businesses also deal with manual handovers, fragmented information, overdue follow-ups and reports that take hours to prepare. These are opportunities worth exploring because the benefit can be measured.

Think of AI as one possible component in a wider improvement. Some tasks can be handled by conventional reporting, a form or an automated workflow. Others may benefit from language-based searching, classification or assisted decision support. The question is which combination produces a reliable result.

Are smaller UK businesses making full use of AI?

There is a substantial opportunity. According to the Office for National Statistics, October 2026, 29% of UK businesses with fewer than ten employees reported using at least one type of AI technology in September 2026. That means about seven in ten did not report using AI in that survey. Across all businesses, the figure was 30%, compared with 51% among those with 250 or more employees.

These figures measure reported use of any AI technology, not whether it is integrated into finance, sales or operations. Different surveys use different methods and can produce higher estimates. The key commercial question is not whether a company has tried ChatGPT, but whether it has identified a useful problem, chosen sensible technology and measured the outcome.

For Managing Directors who have not yet started, this need not be a cause for concern: there is still an opportunity to begin with an affordable, manageable project instead of an expensive transformation.

Six practical ideas to discuss with your team

Customer enquiries: classify incoming requests and route them to the appropriate team. Internal knowledge: help employees find policies and answers in documents they are entitled to access. Stock and supply: highlight unusual demand and potential replenishment issues. Reporting: explain trends in a dashboard instead of asking directors to interpret numerous spreadsheets.

Each example is a possibility, not a guarantee. Data quality, permissions, licences and delivery costs decide what is practical.

What should a Managing Director ask before approving an AI project?

The first question should be about outcomes, not technology. Ask what will be easier, faster or more accurate when the proposed improvement is working. If the answer is vague, the requirement probably needs more thought.

Then consider five practical areas:

• Ownership: Which manager is responsible for the process and who will approve changes?
• Information: Where does the data come from, is it accurate and who can access it?
• Cost: What are the one-off build, licence, support and training commitments?
• Control: Which decisions still need a person to review or approve them?
• Measurement: How will we know whether the solution is actually helping?

Some ideas will not justify the cost. Others may work best with a conventional integration or simpler reporting. That is a useful result of the assessment, because it prevents the business spending on functionality it does not need.

What makes an AI idea commercially worthwhile?

Choose a recurring task with a clear owner. Record how long it takes, how often it happens, what mistakes cost and which systems contain the information. Then describe the smallest useful result. For example, a weekly report that currently takes four hours might be worth improving even if a more ambitious AI platform is not.

Look at the whole cost, including implementation, ongoing licences, support, security review and training. A prototype may be quick to build; a dependable business application can require considerably more testing and support.

How to explore AI without creating an expensive project

A sensible first exercise is a short management discussion. Ask each department to identify one process that takes too long, creates avoidable errors or requires the same information to be entered more than once. Choose a problem that happens regularly enough for a small improvement to make a difference.

For each idea, write down the current process, the people involved and the systems that hold the information. Set a measurable target, such as reducing the time taken to prepare a report or improving the speed of responding to enquiries. This gives you a business case that is easier to assess than a general ambition to ‘use more AI’.

Before buying anything, check whether existing Microsoft 365 tools, reporting software or standard automation features could meet the need. Consider data quality, permissions, security and ongoing maintenance. If AI genuinely adds value, a specialist can help scope a limited pilot, with defined costs, human oversight and a clear point at which to review the outcome.

One useful principle is to keep the first project small enough that you can stop, adjust or extend it without disrupting the wider business. A successful pilot provides evidence for the next investment; an unsuccessful one should still teach you something about the process.

Keep people in control of business decisions

AI-generated text or recommendations can be wrong. Decisions involving employee data, contract commitments or customer outcomes require appropriate human checks. Access to HR information must be restricted to authorised users. The Information Commissioner’s Office AI guidance is a sensible reference when considering personal data.

Security should be considered before connecting systems, not added after development. Existing business cyber security controls and supplier responsibilities matter just as much as the AI functionality.

Explore an example before committing to a project

Our interactive AI ideas for business landing page demonstrates the sorts of management questions that connected information might answer. The numbers are fictional, but the principle is useful: start with what you wish your organisation could do and assess the technology afterwards.

For a closer look at implementation considerations, see AI and business process automation. A practical first step is to select one recurring problem, understand the data and obtain a proportionate assessment.