A few posts back, we mentioned something a little uncomfortable: when Product Owners sort their own AI use cases into categories, one bucket consistently stays nearly empty — AI-Powered Predictions and Insights. We promised to come back to it. This is that post.
Why This One Gets Skipped
It’s not hard to see why. “Draft this user story” has an obvious shape. “Predict how this roadmap decision plays out over the next two quarters” does not. Prediction feels like it requires more trust in the tool, more data than you’re confident you have, and more risk if the AI gets it wrong in a way that’s hard to catch. So people reach for it last, if at all.
But this is also exactly the category where AI can add the most distinctive value — not because it can see the future, but because it can help you reason through more scenarios, faster, than you’d manage by hand.
What This Actually Looks Like
Think of it less as “ask AI to predict the future” and more as “ask AI to help you stress-test a roadmap decision from more angles than you’d naturally consider alone.” A few concrete entry points:
Scenario exploration for roadmap decisions. Before committing to a roadmap sequence, ask AI to generate two or three plausible scenarios for how a given prioritization choice plays out — what happens if adoption is slower than expected, what happens if a dependency slips, what happens if a competitor ships something similar first. You’re not asking for a prediction; you’re asking for a wider set of scenarios than you’d generate solo, so your actual decision accounts for more of what could happen.
Pattern-spotting across historical data. If you have past sprint data, past feature adoption numbers, or past customer feedback trends, AI can help surface patterns you might not notice by eye — which types of features tend to get delayed, which customer segments respond fastest to a given kind of change. The value isn’t the AI telling you what will happen; it’s making the patterns already sitting in your own data more visible.
Early-warning signals. Ask AI to help you define what a leading indicator would look like for a risk you’re already worried about — not to monitor it automatically, but to sharpen your own thinking about what “this is starting to go wrong” would actually look like before it’s obvious.
The Same Caution That Applied to the Hardest Problems
If this sounds similar to the advice from a few posts back about cost, trust, and IP — it should. Prediction and insight work is squarely in “AI as thinking partner” territory, not “AI as autopilot.” A scenario AI generates is a starting point for your own judgment, not a replacement for it. The value is in surfacing possibilities and patterns worth your attention, not in outsourcing the actual call.
Try This This Week
Pick one upcoming roadmap or prioritization decision you’re already facing. Ask an AI tool to generate three different scenarios for how it plays out over the next two quarters — one optimistic, one pessimistic, one that assumes something unexpected happens. Don’t act on any of them directly. Just notice whether any of the three surfaces a risk or opportunity you hadn’t already considered.
Where This Fits
This is the category most people reach for last — which is exactly why it’s worth reaching for on purpose, at least once. If you try this and find a use for it, that’s a genuinely useful thing to bring up at the next Alumni Meetup, since it’s the category fewest people have real experience with yet — and one good example from the room tends to unlock ideas for everyone else too.
Keep going:
- Ready to go deeper? Check out our upcoming AI courses for hands-on classes with real practice, not just reading.
- Want more like this? Subscribe to our YouTube channel for walkthroughs and demos that pair well with these posts.
- Have questions or want to talk it through? Reach us directly at hello@rocketninesolutions.com.
- Know a colleague wrestling with the same challenge? Send them this post — half the value of this series comes from the people who’ve already tried it sharing what worked.
A Note on How This Was Written: This post was developed collaboratively between Rocket Nine’s training team and Claude, Anthropic’s AI assistant — drawing on real material from our AI for Product Owners class and anonymized insights from the Product Owners who’ve taken it. We think that’s fitting: the same thoughtful human-AI collaboration we teach in class is exactly how we approached writing about it.
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