Supercharging Growth: The Role of AI in ICE Scoring
We all want the holy grail of growth—to know exactly which experiments in your backlog will deliver the biggest impact. While that level of certainty is still out of reach, we can get much closer with the right tools. The ICE scoring framework helps teams prioritize quickly and make smarter decisions. And with artificial intelligence now reshaping ideation and experimentation, ICE scoring isn’t becoming irrelevant—it’s becoming even more valuable.
The Value of ICE Scoring
I developed ICE scoring because growth teams often don’t have the luxury of time, resources, or sample size to test every idea. ICE helps by asking three simple questions:
Impact: How much will this move the needle on key metrics?
Confidence: How sure are we it will succeed, based on data and insights?
Ease: How easy is it to implement?
Typically, higher ICE scores mean higher priority because they offer more impact relative to the effort required. It’s flexible, too—for example, you can prioritize ease if you need to hit weekly experiment targets. But even with ICE, estimating "Confidence" and "Impact" can be tricky, and that’s where AI steps in.
How AI Supercharges ICE Scoring
AI helps reduce guesswork and improves the scoring process in several ways:
Data-Driven Confidence, Enhanced by Qualitative Insights: AI can analyze historical data, customer behavior, and even qualitative insights like user feedback to predict an experiment’s likelihood of success. This creates a more reliable "Confidence" score.
More Accurate Impact Predictions: AI can model potential outcomes by analyzing similar experiments and customer data, offering a more precise forecast of an experiment’s potential impact. By simulating various scenarios and outcomes, AI helps teams better understand how likely an experiment is to drive significant results. This deeper insight into possible outcomes allows for more informed, data-driven prioritization decisions, helping teams focus on the experiments most likely to move the needle.
Automated Scoring: AI can automatically assign ICE scores to your backlog based on real-time data, making the prioritization process faster and more dynamic.
Making Ease More Actionable: AI can evaluate technical feasibility, helping teams balance low-effort, high-impact experiments with more complex ones.
AI Brings More Ideas—And More Need for Prioritization
Here’s the kicker: AI is going to supercharge your ideation process. It can generate more test ideas than ever by analyzing customer behavior, trends, and even competitor tactics to suggest new experiments. You’re going to have a massive backlog of potential tests, which is exciting—but it’s also overwhelming.
That’s why you need a reliable system like ICE even more. As your backlog grows, you need a way to filter through it and find the experiments that are actually worth running. AI doesn’t replace ICE—it makes it indispensable. Without ICE, you could easily get lost in the flood of new ideas, wasting time on low-impact tests while the high-value experiments get buried.
Conclusion
AI doesn’t make ICE scoring irrelevant—it makes it better. By removing subjectivity and speeding up decision-making, AI enhances the process, but teams still need a framework like ICE to structure their prioritization. AI is a tool to complement ICE, not replace it. As AI generates more ideas and enables faster experimentation, ICE becomes even more critical to ensure focus on the right experiments.
AI is driving experimentation at an unprecedented scale, but without structure, it’s easy to get lost in the flood of possibilities. That’s where ICE scoring comes in—combining AI with a framework like ICE gives growth teams a serious competitive advantage. I’d love to share specific examples of companies using AI with ICE, but so far, I haven’t seen any public case studies. My guess? Many teams are already doing it but are keeping it under wraps to maintain their edge.
If you’ve started using AI for better ICE scoring, reach out. I’d love to hear how it’s working for you.