Don’t Overthink It – Start Where It Makes Sense

When we started using AI, it wasn’t a dramatic overhaul. We tested tools that helped us take minutes, pull out data insights, and prep agendas. It wasn’t glamorous, but it worked – and more importantly, it gave our team time back.

You don’t need a grand transformation plan to get going. Start small. Look for one or two areas where your team spends too much time on repetitive or manual tasks. Then ask: Could AI help here?

Some of the easiest ways in are:

• Email assistants for summarising and drafting
• Tools that tidy up reports or analyse surveys
• AI platforms that help with brainstorming or idea development
• Internal tools that answer FAQs or pull data

Plenty of these are low-cost and don’t require full integration. The trick is to choose something low-risk, high-impact, and just give it a go.

Most of these tools are now available on a pay-as-you-go basis, requiring minimal integration.

The most successful AI use cases in SMEs start small and solve a clear business pain point. Think process automation, data management and reporting, strategy development – not building the next ChatGPT.

Trial and Error Is Part of the Process

Don’t Expect Perfection – Expect Learning

There is no single AI tool that will transform your business overnight. We took a test-and-learn approach:

• Trial a tool with a small team
•  Test it on real tasks
• Be honest about what worked (and what didn’t)
• Build on the wins

And yes, there were some false starts. That’s normal.

What made the difference was building on the wins. When a tool genuinely saved time or improved how we worked, we looked at how to scale it across the business.

That said, I’m always cautious about data security. If you’re putting client or sensitive info into a tool, make sure you know how it’s being handled. When testing tools (especially free versions), check:

• What data are you inputting?
• Where is it being stored or shared?
• Does this align with your cybersecurity and privacy policies?

Many AI tools are free or low-cost to start with. But it’s important to read the fine print. Some versions use your data to train their models, which means sensitive or client information could be at risk.

Capability Before Complexity

I often say: you can have the best piano in the world, but if no one knows how to play it, it won’t sound great. AI’s the same. You’ve got to invest in learning to get any real value from it.

That’s why we aren’t just adopt tools – we’re building internal confidence too. If your team is wary, or unsure how AI fits their role, you need to create a safe space for exploration.

Start by:

• Running short training sessions or demos of simple tools
• Setting up a small “AI working group” to test solutions
• Encouraging teams to document what works (and what doesn’t)

This reduces resistance and builds momentum from within. When employees see that AI helps, not hinders, they start asking ‘what else can we do?’  That’s when the transformation begins.

Keep Ethics, Policy and People in Mind

As AI gets more powerful, we’ve had to think more carefully about how we use it – not just whether it works. SMEs need to consider:

•  Where does the data come from?
• Can I explain how this AI reached its conclusion?
• How do I make sure we don’t accidentally share confidential information with third-party tools?
• What are the intellectual property rights implications associated with this work
• Is the result perpetuating biases present in their training data, leading to unfair outcomes?

That’s why, if you’re a business leader, I’d urge you to develop a basic AI policy. Nothing complex – just enough to cover:

• What tools are okay to use
• What kind of data can be input
• Who’s responsible for reviewing outputs
• How you talk to clients about your AI use
• A reminder that AI is a support tool, not a replacement for judgement

We need to explore how AI can benefit operations; but balance this with thoughtful policy, skill development, and the irreplaceable human touch.

Use What You Already Have

One of the most useful things we did was check the tools we already had.

Microsoft Copilot, Gmail smart replies, HubSpot’s send-time optimisation, accounting software (like Xero or QuickBooks) increasingly offers AI-led analytics – these are AI features many teams already have access to but aren’t using to full effect.

Check the tools you already pay for – you may find AI capabilities waiting to be switched on.

Final Thoughts: Start Small, Stay Curious

You don’t need to become an AI expert overnight. Just spot a pain point, test a tool, and learn by doing. Here’s how I recommend you start:

5 Practical Steps to Using AI in Your Business

1.      Spot the Time-Wasters – Pick 1–2 tasks that are repetitive, slow or admin-heavy.

2.      Test a Tool (Safely) – Try something like ChatGPT, Otter.ai or Microsoft Copilot. Don’t input anything sensitive on day one.

3.      Build Confidence First – Upskill one or two team members and create space to explore. Focus on prompt-writing and workflows.

4.      Set Some Ground Rules – Create a simple AI policy so everyone’s on the same page.

5.      Start Small, Scale What Works – Roll it out slowly, track what helps, and share the success.

If you’re curious about AI but unsure where to begin, I’d be happy to share more about what’s worked for us at BIC Innovation – and help you find what could work for your business too.