# The Vellmerk Matrix: Why Impact Matters More Than Effort > In AI projects, effort estimation is notoriously unreliable. The Vellmerk Matrix shows why you should still start boldly, if you start on the right line. *Published: 2026-04-05* *Source: https://vellmerk.ai/en/blog/vellmerk-matrix* Anyone who has ever planned an AI project knows the problem: effort estimation is a gamble. Vellmerk.ai works with state-of-the-art models whose capabilities change every few months. APIs that work today may be deprecated tomorrow. And then there's the data foundation that looked perfect in theory, until you actually look at it. The requirements? Often unclear, because the client doesn't yet know what's possible with AI. The effort estimation? At best, an educated guess. **But: The impact, that Vellmerk.ai can usually assess very well.** ## The Vellmerk Matrix The Vellmerk Matrix is a simple 2x2 framework that Vellmerk.ai uses in consulting, refined across multiple client projects, to prioritize AI projects. The two axes: **Y-axis: Impact**, How significant is the business value? This can usually be assessed well: time savings, error reduction, customer satisfaction, revenue potential. **X-axis: Effort (uncertain!)**, How much work will it take? And here's the crux: in AI projects, this estimate is notoriously unreliable. ## The Four Quadrants **Quick Wins (high impact, low effort): **The sweet spot. This is where we love to start. Quickly visible results that motivate the team and build trust in the use of AI. **Strategic Bets (high impact, high effort): **Complex projects with great potential. Even if the effort increases: the high impact justifies the investment. If you misjudge the effort here, you've still built something valuable. **Fill-Ins (low impact, low effort): **Nice optimizations that you can pick up along the way, but never as a starting point. Because if the effort unexpectedly increases (and it does), you're stuck with a costly project that creates little value. **Time Wasters (low impact, high effort): **Stay away. In the context of AI projects, the most dangerous category, and exactly where you end up if you start with Fill-Ins and the effort estimation misses the mark. ## The Golden Rule: Stay on the Top Line The central insight of the Vellmerk Matrix: Always start on the top line, with Quick Wins or Strategic Bets. Both have high impact. Why? Because effort estimation in AI projects is uncertain by definition. If you start at the top and the effort turns out higher than expected, your project moves from Quick Win to Strategic Bet. That's okay: the impact is still high, it's still worth continuing. But if you start at the bottom, with Fill-Ins, and the effort explodes, you land in the worst quadrant: Time Wasters. A lot of work, little value. And that is exactly the effort trap that can be avoided. > "Impact is the compass, effort is just the weather. And weather changes.", Thorsten Vellmerk ## Conclusion The Vellmerk Matrix is not an academic model, it is a pragmatic tool that Vellmerk.ai uses in every consulting project. It helps prioritize the right projects and avoid the typical pitfalls in AI implementations. Want to apply the Vellmerk Matrix to your projects? **Get in touch**, Vellmerk.ai helps you find the Quick Wins and avoid the Time Wasters. ## About Vellmerk.ai Vellmerk.ai is an AI consultancy (Danish ApS) founded by Thorsten Vellmerk. Drawing on 20+ years of process and IT experience and several years of hands-on AI consulting, Vellmerk.ai helps SMEs and public administration adopt AI in a practical, sovereign way, from strategy to local, on-premise-ready implementation. Proven across multiple client projects. [Book an initial consultation](/en/contact). ## Frequently asked questions ### What is the Vellmerk Matrix? The Vellmerk Matrix is a simple 2x2 grid that sorts AI initiatives by impact and effort. From this come four fields: quick wins (high impact, low effort), strategic bets (high impact, high effort), gap fillers (low impact, low effort) and the effort trap (low impact, high effort). It helps you derive the right sequence from a long list of ideas. ### How do I prioritise AI projects correctly? By first sorting them by impact and effort, not by technical fascination. Start with quick wins that deliver visible value fast and build trust. Plan strategic bets deliberately and with a budget. Gap fillers get done on the side, and the effort trap is consistently left alone. That creates momentum instead of stalling on first steps that are too big. ### What is the difference between quick wins and strategic bets? Quick wins deliver impact fast with manageable effort, ideal for getting started and for building internal acceptance. Strategic bets have great potential but need more time, budget and appetite for risk. Both have their place but should not be confused: anyone planning a strategic bet as a quick win will fail on expectations. ### Why not just start with the technically most exciting project? Because the technically most exciting project often sits in the effort trap: lots of complexity, little measurable business value. First AI projects should show impact that the surrounding organisation understands and appreciates. Fascination is a poor guide for prioritisation; impact per unit of effort is the better one.