AI can produce rubbish very quickly.
That is one of the reasons many business owners try ChatGPT, Copilot or another AI tool, get a few impressive-looking answers, then quietly stop using it. The output looks polished, but it is too vague, too American, too salesy, slightly wrong, or just not how the business would actually say or do things.
That does not mean AI is useless. It usually means the AI has been given too little to work with.
In most small businesses, poor AI output comes from the same few causes: weak instructions, no context, no examples, unclear boundaries, and no review process. If you fix those, the quality improves quickly.
The problem is not just the prompt
There is a lot of advice online about writing better AI prompts. Some of it is useful, but it can give the wrong impression.
A prompt is not a magic spell. It is a brief.
If you asked a new member of staff to write a client email, prepare a proposal, summarise a meeting, or research a supplier, you would not just say “do the email” and expect perfection. You would explain the client, the purpose, the tone, the background, what must be included, what must be avoided, and who should check it before it goes out.
AI needs the same sort of briefing.
The businesses that get useful results from AI usually stop treating it like a clever search box and start treating it like a very fast junior assistant. It can help enormously, but it needs management.
Give it the right context
The first step is context. AI tools do not automatically understand your business, your clients, your priorities or your appetite for risk.
For example, if a Cheltenham accountancy firm asks AI to “write a blog about tax planning”, it will probably produce something generic. If the same firm explains that the audience is local owner-managed businesses, the tone should be calm and practical, the post should avoid giving personal tax advice, and the aim is to encourage readers to book a review, the answer becomes more useful.
Good context might include:
- who the customer or reader is
- what the business is trying to achieve
- the tone of voice
- the commercial goal
- what information is already known
- what must not be guessed
- any legal, privacy or compliance limits
This does not need to be complicated. A one-page “how we use AI for this task” note is often enough to make outputs far more consistent.
Show it what good looks like
Examples are one of the most underrated ways to improve AI output.
If you want AI to draft emails, show it two or three good emails. If you want it to summarise calls, show it a good summary. If you want it to write LinkedIn posts, proposals, FAQs or internal notes, give it examples that sound like your business.
You can also show bad examples and explain why they are wrong. For instance: “This is too pushy”, “This makes claims we cannot prove”, “This sounds like a corporate brochure”, or “This misses the practical next step”.
That feedback helps turn AI from a random content generator into something closer to a reusable workflow.
Set clear rules and boundaries
AI output goes wrong when the rules are only in someone’s head.
A business using AI sensibly should write down basic rules, such as:
- do not invent facts, prices, dates, case studies or testimonials
- flag uncertainty rather than hiding it
- never paste confidential client data into public tools unless approved
- ask for human review before anything is sent externally
- use British English
- keep claims specific and evidence-based
- separate research notes from finished recommendations
These rules are not there to slow things down. They make AI safer and more useful because everyone knows what acceptable output looks like.
This was one of the lessons from my Cheltenham Times AI agent experiment. The AI-assisted workflow could search for local stories, draft posts, create images, categorise content and suggest next steps. But it needed rules: what counted as Cheltenham, which sources were reliable, whether a story was useful to local readers, whether an image matched the article, and whether something was too thin to publish. Without that steering, speed could easily become a problem rather than an advantage.
The same applies in a professional business. A fast assistant is only useful if it is working to the right standard.
Build in review, not blind trust
AI should not be treated as a magic truth machine.
For many SME tasks, the best setup is “AI drafts, human decides”. AI can prepare the first version, summarise options, check documents, spot missing information, or suggest actions. A person then reviews the output, checks the important details and decides what happens next.
This is especially important for client work, regulated advice, HR, finance, contracts, pricing and anything reputationally sensitive.
Review does not have to mean reading every word of every AI output forever. As a workflow matures, you can decide which tasks need close review and which can be lightly checked. But at the start, review is part of the training process. It is how you find the gaps.
Create a feedback loop
The businesses that improve fastest do not just correct AI outputs once. They capture the correction.
If an AI draft is too vague, update the instructions. If it uses the wrong tone, add a better example. If it makes a risky assumption, add a rule. If staff keep asking the same question, create a reusable template.
Over time, this becomes a small operating system for using AI in the business. Not a huge transformation programme. Just practical guidance, examples, templates and checks that make the next output better than the last.
That is where AI starts becoming commercially useful. It stops being a novelty and becomes part of how work gets done.
A simple next step
Pick one recurring task where AI output is currently disappointing. It might be client emails, meeting notes, proposals, job adverts, internal procedures, research summaries or marketing drafts.
Then write down five things:
- What the task is meant to achieve.
- What good output looks like.
- Two examples of good work.
- Three rules the AI must follow.
- Who reviews the output before it is used.
Try the task again with that information included. In many cases, the improvement is immediate.
If you would like help identifying where AI could produce useful, safe and measurable results in your business, I can help you map the right opportunities, set the rules, and build workflows that your team can actually use. Book a short AI consultancy call and we can look at what AI could sensibly do in your business.
