7 Common Mistakes Businesses Make When Using AI

Everyone seems to be using AI these days. Unfortunately, not everyone seems to have learned how to use it effectively yet. AI can be extremely useful in business, but using it effectively isn't simply a matter of opening an AI tool and asking it to do your work.

Many of the problems businesses experience with AI come from how the technology is being used rather than from the technology itself.

Here are seven common mistakes to avoid.

1. Giving AI vague instructions

One of the most common mistakes is providing very little context. For example:

"Write a blog about our company."

That leaves a lot of unanswered questions. What does the company do? Who is the audience? What is the purpose of the article? What tone should it use? How long should it be?

A better, more comprehensive prompt provides the information AI needs.

Think of the difference between asking an employee to "write something about the company" and giving them a proper brief. The second approach is more likely to produce useful work.

2. Assuming AI knows your business

AI doesn't automatically understand your company's positioning, customers, internal processes or brand voice. If those things matter to the task, provide them. Give AI useful background information about your business and explain what you are trying to achieve.

This is particularly important when creating marketing content. Generic information produces generic content.

3. Publishing AI output without checking it

This is one of the biggest risks and undoubtedly one of the top crimes committed by AI users (whelp!).

AI can produce incorrect facts, outdated information or statements that sound plausible but are not supported by evidence (the pesky stuff that substantiates claims).

The fact that something is written confidently doesn't make it true. Any information that could affect a customer's decision, your reputation or a business decision should be checked before publication or use.

This is especially important for statistics, prices, regulations, technical information and claims about products or services.

4. Asking AI to replace expertise

AI is useful for helping people work but it shouldn't automatically be treated as an expert in your industry, no matter how convincing it sounds!

A qualified professional brings experience, judgement and knowledge of the circumstances surrounding a problem.

AI can help you explore an issue or prepare a first draft, but there are situations where human expertise should remain central. For example, a business should not rely solely on AI when making legal, financial, medical or other high-stakes decisions.

5. Sharing confidential information

Another mistake is treating an AI tool like an internal company system without considering what information is being entered.

Before putting sensitive business information into an AI service, understand the relevant privacy and data handling arrangements.

Avoid casually entering passwords, customer information, confidential contracts, commercially sensitive information or other material that should not be shared.

If your employees are using AI, set clear guidelines around what information they can and cannot enter. AI can help you create a practical AI policy and staff training guide, giving everyone a clear understanding of how AI should be used and helping to keep things consistent and compliant across the business.

6. Trying to automate everything

Automation can save time, but not every task should be automated.

Consider a customer complaint. An AI system might be able to produce a perfectly structured response. That doesn't necessarily mean it's the right response. Some interactions require empathy, judgement and a genuine understanding of the situation. If a customer complaint receives a response that is obviously AI-generated, it could easily make an already difficult situation even worse.

The question should not be:

"Can AI do this?"

It should be:

"Would using AI improve this process?"

Those are two very different questions.

7. Focusing on the tool instead of the problem

AI tools change quickly. New products appear, existing products add features and businesses can easily spend a lot of time trying different platforms. It's easy to lose sight of the original purpose.

Instead, start with a business problem. Perhaps your team spends several hours every week turning one piece of content into social media posts. That's the problem. AI might be part of the solution.

Starting with the problem makes it much easier to decide whether a particular AI tool is actually useful.

How to use AI more effectively

Good AI use usually involves three stages:

Give it the right information

Provide context, instructions and relevant source material.

Review the result

Check facts, tone, logic and suitability.

Add human judgement

Use your experience to improve the final result.

AI can make the first two steps faster, but your business knowledge remains important.

The goal is better work, not simply more AI

AI is another business tool. The businesses that get the most from it are unlikely to be the ones that simply use the most AI, they're more likely to be the ones that understand where it can genuinely improve their processes.

Start with a problem. Test a solution. Measure the result. Then decide whether it's worth continuing.

Our approach to AI

AI is a useful tool, but we don’t believe everything needs to be done with AI simply because it can be.

We use AI thoughtfully for relatively lightweight tasks, practical problem-solving and logical automations where it can genuinely improve the way we work.

As environmental champions, we’re also conscious that AI has an environmental cost. We avoid using it for unnecessary content or images for the sake of it. Our mantra is that images shouldn't be generated simply because they can be, rather than because they serve a useful purpose.

We dislike the 'AI made this' aesthetic of corporate social media posts (we're not artists, but we appreciate art). These things need soul!

We’ll continue to explore where AI can add real value while remaining mindful of its wider environmental impact.