Glossary
By Priya Shankar
AI feature prioritization uses machine learning to rank potential features based on business impact, engineering effort, strategic alignment, and market signals—then weights them against capacity constraints and roadmap dependencies. It's not replacing human judgment; it's automating tedious data compilation that should inform human judgment.
Traditional prioritization is anecdotal: "The CEO wants this." AI prioritization starts with data: actual feature usage, customer feedback sentiment, competitive positioning, technical debt impact. You decide what matters most; AI ensures you're deciding based on complete information.
An AI system might ingest: feature request backlog, customer support tickets, usage analytics, revenue impact per customer, engineering estimates, and codebase health metrics. Then it scores features against criteria: revenue impact, customer satisfaction, strategic alignment, risk reduction.
The output isn't "do this feature." It's "Here are top 15 candidates ranked by impact/effort ratio. Feature A impacts 400 customers and is a 5-pointer. Feature B impacts 20 customers but is a 3-pointer. Feature C is a 13-pointer but would reduce technical debt by 2 months."
Now you make the strategic call. But you're informed.
"AI will tell us exactly which features to build." No algorithm captures all strategic nuance. AI surfaces data; you interpret based on vision and judgment.
"We should do whatever scores highest." That leads to feature bloat. High-scoring features are important, but sometimes you need to tackle technical debt or build competitive features even if they score lower.
"Prioritization AI is too complex." Start simple: ingest feature requests and score against effort and customer impact. You can build meaningful prioritization in a week.
Teams without structured prioritization ship randomly and miss strategic opportunities. Teams with AI-informed prioritization ship strategically and build things customers actually want.
The second-order benefit: predictability. When prioritization is systematic and transparent, engineering and customers understand why features are queued.
Feature adoption: Do prioritized features get higher customer adoption? Should see 30%+ improvement in average feature adoption rates.
Customer satisfaction: Do prioritized features resolve stated problems? Track CSAT for features built with AI prioritization vs. those built intuitively.
Capacity utilization: Are you shipping more value per sprint? Compare quarterly output before and after.
Strategic alignment: Do shipped features ladder up to business goals? This should improve dramatically.
Q: Doesn't this slow down decisions? Not if you automate scoring. AI scoring takes seconds. The slowdown comes from analyzing results, which is strategic thinking that should be slow.
Q: What if the algorithm prioritizes wrong? Review the reasoning. Good systems show weights and inputs. Disagree? Adjust criteria and re-run.
Q: Can we prioritize technical debt alongside features? Absolutely. Rank features and debt together. You might discover a high-impact feature is blocked by debt reduction. Now it's visible.
Q: How do we handle strategic bets that don't score high? Explicitly reserve "strategic slots" for bets that don't optimize for short-term ROI but align with long-term vision.
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