How to choose your first AI use case: a 10-question filter

Many small and medium-sized businesses recognise the potential of AI to boost efficiency, improve customer service, and even unlock new revenue streams. The challenge, often, isn't whether to use AI, but where to start. With so many possibilities, it's easy to feel overwhelmed and perhaps even choose a project that's too ambitious, too complex, or offers too little return for your first foray.

This article provides a practical, 10-question filter designed to help you identify the most suitable first AI use case for your business. This isn't about finding the 'perfect' AI solution, but rather a 'good enough' starting point that delivers tangible value and builds confidence. Think of it as a checklist to ensure your initial AI project is achievable, impactful, and lays a solid foundation for future AI adoption.

Why a Focused Approach Matters

Jumping into AI without a clear strategy can lead to wasted resources, disillusionment, and a belief that AI 'isn't for your business'. Your first AI project should deliver demonstrable success. This builds internal buy-in, helps your team adapt to new ways of working, and provides valuable lessons for subsequent, larger-scale initiatives. A well-chosen first project minimises risk and maximises your chances of a positive outcome.

Conversely, picking a project that's too complex, requires data you don't have, or targets an issue that isn't really a problem, can be detrimental. It can drain resources without an adequate return, discourage your team, and delay genuine AI adoption.

The 10-Question Filter: Identifying Your First AI Use Case

For each potential AI use case you're considering, ask yourself the following questions. Be honest with your answers. If a question consistently yields a 'no', it's likely that this particular use case isn't the best starting point.

1. **Does it solve a genuine, re-occurring business problem?** * *Why this matters:* AI for AI's sake is a costly distraction. Focus on challenges that genuinely hinder productivity, customer satisfaction, or profitability. Is it a persistent headache that drains staff time or leads to errors? * *Example:* Manually categorising incoming support emails is time-consuming and inconsistent.

2. **Is the problem well-defined and relatively narrow in scope?** * *Why this matters:* Avoid trying to solve world hunger with your first project. A narrow scope makes the project manageable, easier to measure, and quicker to deliver. * *Example:* Instead of "improve all customer service", focus on "automatically route technical support queries to the correct team".

3. **Do you have access to the necessary data, or can you easily acquire it?** * *Why this matters:* AI feeds on data. Without relevant, sufficient, and clean data, your AI project will struggle or fail. Be realistic about data availability and quality. * *Example:* Do you have a consistent history of support emails with their correct categorisations?

4. **Is the potential impact of solving this problem significant, yet not mission-critical?** * *Why this matters:* You want a meaningful win, but not one that could bring your business to a halt if the AI makes a mistake early on. Low-stakes projects are better for learning. * *Example:* Saving 5 hours a week in email routing is good, but if the AI occasionally misroutes an email, it's not disastrous.

5. **Can success be clearly measured? What quantifiable metrics will you track?** * *Why this matters:* Without measurable outcomes, you won't know if your AI project is actually working or providing value. Define your KPIs upfront. * *Example:* "Reduction in average email routing time by 20%", or "Increase in correctly routed emails from 70% to 90%".

6. **Does this project involve a repetitive, rule-based task that humans find mundane or prone to error?** * *Why this matters:* AI excels at automating predictable, high-volume tasks. These are often the tasks employees dislike, freeing them for more engaging work. * *Example:* Data entry, initial document review, common FAQ responses.

7. **Is there internal stakeholder buy-in from the affected department(s)?** * *Why this matters:* Without the support of the people who will actually use or be affected by the AI, adoption will be difficult. Involve them early. * *Example:* The customer service team's manager and key staff agree this is a valid problem and are willing to experiment with solutions.

8. **Will this project enhance an existing workflow rather than replace an entire job role?** * *Why this matters:* Focus on augmentation, not immediate replacement. This reduces resistance from employees and allows for a smoother transition. AI tools like Copilot are designed to assist, not replace. * *Example:* The AI helps categorise emails, but a human still reviews and responds.

9. **Do you have the internal skills or access to external expertise required to implement and manage this type of AI?** * *Why this matters:* Don't bite off more than you can chew. If you lack data science or AI engineering skills internally, ensure you have a trusted partner. For many SMBs, tools like Copilot simplify this, but still require some understanding. * *Example:* Can your IT team manage a Power Automate flow, or do you need consultancy support for a more complex integration?

10. **What is the approximate cost (time and money), and what is the anticipated return on investment (ROI)?** * *Why this matters:* Every business decision needs a justification. Even if the first project is for learning, there should be a reasonable expectation of positive ROI. Be realistic about both costs and benefits. * *Example:* Project cost is £5,000, expected saving of 5 staff hours/week equates to £150/week or £7,800/year, making it worthwhile.

Pitfalls to Avoid

As you apply this filter, be wary of these common missteps:

• **The "shiny object" syndrome:** Don't chase the latest trending AI technology if it doesn't align with a genuine business need.
• **Over-reliance on "magic":** AI is a tool, not a miracle worker. Be realistic about what it can achieve.
• **Ignoring the human element:** AI projects are ultimately about changing how people work. Communication and training are paramount.
• **Scope creep:** Stick to your well-defined, narrow problem. Adding features prematurely complicates everything.

Your Next Steps

Once you've identified a promising candidate using this 10-question filter, the next step is to formulate a small-scale pilot project. Think of it as a proof of concept. This could involve a small department, a limited dataset, or a short timeframe. The goal is to test your assumptions, gather real-world feedback, and demonstrate value without committing significant resources.

Remember, the aim is to build momentum and show your team that AI isn't just hype; it's a practical tool that can help your business thrive. Start small, learn fast, and scale deliberately. If you need help applying this filter to your business or structuring your first AI pilot, get in touch – we're here to help navigate these early stages effectively.