Glossary
By Priya Shankar
An AI product roadmap is a forward-looking plan generated with machine learning assistance to forecast feature dependencies, capacity constraints, and strategic priorities across quarters. It's not a deterministic prediction; it's a strategic scenario that evolves as market conditions change and you learn more.
Traditional roadmaps are static: "Q2 we ship X, Q3 we ship Y." They ignore dependencies, assume constant velocity, and rarely account for realistic delays. AI roadmaps surface: "Feature X unlocks Features B and C; if X slips, B and C slip too. We have 2-week buffer before Q2 ends. Here's the risk profile."
An AI roadmap system ingests: historical velocity, feature complexity estimates, resource availability, dependency graphs, and strategic priorities. Then it generates scenarios: "If we start with Feature A (21 points), it blocks Features B, C, D. Best case: all three ship in Q2. Realistic case: A and B ship; C slips to Q3. Worst case: only A ships."
You can stress-test roadmaps: "What if we get a critical bug? What if we lose an engineer? What if this feature takes 50% longer?" AI roadmaps show you the ripple effects immediately.
"AI roadmaps are definitive." No. Roadmaps are hypotheses. Market moves, customers demand changes, engineers discover risks. A good roadmap is flexible and evolves.
"AI roadmaps remove uncertainty." They quantify uncertainty and show where risk concentrates. You're not removing risk; you're making it visible and manageable.
"AI roadmaps mean we commit to features." Completely wrong. A good roadmap is transparent about assumptions. "We plan to ship this if these assumptions hold true."
Teams without structured roadmaps ship randomly and disappoint stakeholders constantly. Teams with AI-informed roadmaps communicate clearly about what's coming, why it's sequenced that way, and what could change it.
Second-order benefit: resource planning. If a feature blocks three downstream features, and you have only one engineer who understands it, that's a critical dependency visible upfront.
Roadmap adherence: What percentage of planned features ship when you said they would? 70-80% is realistic. If consistently below 60%, your roadmaps are too optimistic or dependencies poorly managed.
Stakeholder satisfaction: Do customers and internal teams trust your roadmap? Track NPS/satisfaction for roadmap communication.
Dependency awareness: How many times are you surprised by a dependency at the last minute? Should trend toward zero as you surface dependencies in planning.
Strategic flexibility: Can you pivot quickly when market conditions change? A good roadmap is flexible enough to accommodate new signals while maintaining strategic coherence.
Q: If we AI-generate roadmaps, do we lose strategic vision? Not if you use AI to inform, not decide. Set strategic priorities and constraints; let AI optimize sequencing and resource allocation within those bounds.
Q: How do we handle roadmap surprises? They'll still happen. What changes is transparency. A good roadmap shows you assumptions and risk. When surprises occur, you re-run the analysis with new information.
Q: Can we use AI roadmaps to forecast revenue? Yes, if you have data connecting features to revenue. Some features have obvious ROI (pricing plans); others are indirect (infrastructure enabling future features).
Q: How often should we regenerate roadmaps? Every quarter minimum. Quarterly is the strategic unit; monthly reforecasting is reactive noise.
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