For many business leaders, the AI conversation has become a constant background hum. You’ve likely read headlines about its transformative potential, sat through presentations from vendors promising the world, and listened to your own teams suggest ways they might “use AI to get ahead.” You’ve probably also heard the cautionary tales: projects that went nowhere, wasted budgets, and technology that promised to be revolutionary but ended up gathering dust.
This combination of hype and risk makes one decision surprisingly difficult — what should be your first AI project? Get that first step right, and you build credibility, trust, and momentum for future initiatives. Get it wrong, and you risk scepticism, stalled investment, and a perception that AI is more trouble than it’s worth.
The good news is that choosing the right first project doesn’t have to be a gamble. By applying a few well-established frameworks — and a healthy dose of pragmatism — you can identify a project that delivers tangible value without introducing unnecessary risk.
Why Your First AI Project Matters
Your first AI project sets the tone for everything that follows. A successful start builds internal confidence, demonstrates measurable value, and creates momentum for scaling AI initiatives across the business. Conversely, a poor choice can stall progress for years, making stakeholders wary of further investment.
A well-chosen initial project will balance three things: clear business value, achievable scope, and manageable risk. In this guide, we’ll walk through a process that ensures all three are in place before you begin.
Step 1: Define the Job to Be Done
What the Jobs to Be Done Framework Means for AI
When Harvard Business School professor Clayton Christensen developed the “Jobs to Be Done” (JTBD) framework, he wasn’t thinking about artificial intelligence. He was explaining why customers choose certain products or services. His famous example involved milkshakes: people weren’t buying them simply because they liked milkshakes; they were “hiring” them to solve a problem — in that case, making a boring morning commute more bearable.
The JTBD approach works beautifully for AI adoption because it forces you to identify a clear business outcome before selecting any tools or technologies.
How to Translate Business Needs into AI Use Cases
Rather than beginning with, “We should use AI for something,” ask:
- What’s the recurring job in our business that feels slow, frustrating, or prone to error?
- Who benefits if that job is done better?
- How will we know if we’ve succeeded?
In professional services, this might mean capturing accurate meeting notes so managers can focus on client relationships. In clinical research, it could be reviewing trial protocols for compliance to speed regulatory approval. In manufacturing, it might involve improving demand forecasting to avoid costly overstock or shortages.
Step 2: Prioritise with the Impact vs. Effort Matrix
Once you’ve listed potential “jobs” for AI to tackle, you need to prioritise them. That’s where the Impact vs. Effort matrix comes in — a decision-making tool used to weigh potential value against delivery complexity.
The Four Quadrants Explained
The matrix divides potential projects into four categories:
- Quick Wins – High impact, low effort.
- Major Projects – High impact, high effort.
- Fill-Ins – Low impact, low effort.
- Thankless Tasks – Low impact, high effort.
Why “Quick Wins” Are the Smart Place to Start
Your first AI project should sit firmly in the “Quick Wins” quadrant. These are initiatives you can deliver in 30–60 days, measure easily, and use to prove the value of AI in your specific context. This approach builds trust with stakeholders and avoids the risk of overreaching on day one.
Step 3: Run a Risk-Confidence Check
Even with a clearly defined job and a quick win in sight, you need to make sure your project is safe to launch.
Five Questions to Test Your AI Project Readiness
- Is the value clear and measurable?
- Do you have the data, and can you use it lawfully?
- Have you addressed privacy, bias, and accountability?
- Can you run a pilot in 60 days or less?
- Will the AI be embedded in existing workflows for adoption?
Balancing Innovation with Governance
This is about building confidence, not adding bureaucracy. By aligning with recognised frameworks like the NIST AI Risk Management Framework and OECD AI Principles, you ensure your first AI project is not just innovative, but also responsible and sustainable.
Industry-Proven First AI Project Examples
AI in Professional Services: Faster Proposals and Meeting Summaries
Somerset Council in the UK reduced meeting minute preparation time from 72 to just 17 minutes using Microsoft Copilot. Similarly, firms are using AI to draft first versions of client proposals, saving hours each week while maintaining quality.
AI in Clinical Research: Protocol Reviews and Risk Monitoring
The FDA has piloted “Elsa,” a generative AI tool for protocol review and scientific evaluation. Other CROs use AI for centralised monitoring, spotting compliance risks earlier and reducing costly trial delays.
AI in Manufacturing and Distribution: Forecasting and Quality Control
McKinsey reports AI forecasting can cut errors by up to 50% and reduce inventory costs by 10–40%. Manufacturers are also using computer vision systems to detect defects earlier, reducing waste and improving product quality.
Common Pitfalls to Avoid When Starting with AI
Even with a robust process, leaders often fall into avoidable traps:
- Tool-first thinking – Chasing a technology rather than solving a defined business problem.
- Overengineering – Choosing a complex, high-stakes project before proving basic delivery.
- Ignoring governance – Skipping data and privacy checks.
- Neglecting adoption – Not integrating AI into the tools people already use.
Avoiding these mistakes keeps your first AI project from becoming a cautionary tale.
From First Win to Ongoing AI Success
Choosing your first AI project is like learning to swim — you start in the shallow end, master the basics, and build confidence before diving into deeper waters.
By defining the job to be done, prioritising with the Impact vs. Effort matrix, and applying a risk-confidence check, you set yourself up for an early win that paves the way for more ambitious AI initiatives.
AI Starter Workshops
If you’d like expert guidance, we run AI Starter Workshops for leadership teams to help you identify top AI opportunities, prioritise them effectively, and de-risk implementation. In just 90 minutes, you’ll have a clear, confident first step toward AI success.