AI Product Management: Ideas That Will Dominate 2025
Most product managers spend their days asking engineers questions they shouldn't need to ask. "Is the checkout flow still owned by Sarah's team?" "Why did velocity drop last sprint?" "What features did we ship that compete with Stripe's new API?"
The gap between product strategy and code reality has always existed. But in 2025, that gap becomes lethal.
Here's why: AI coding assistants are accelerating feature development by 40-60%. Your competitors are shipping faster. Your engineers are context-switching more. And you're still scheduling 30-minute syncs to understand what actually shipped last week.
The product managers who win in 2025 won't be those with the best roadmap decks. They'll be the ones who close the loop between strategy and implementation without creating more meetings.
I've seen this pattern a dozen times. PM asks engineering to build a feature. Two sprints pass. Engineering marks it complete. PM tests it. Something's wrong—the feature works, but it's missing edge cases. Or it's technically complete but unusable. Or it's 80% done but the PR got merged anyway.
Traditional product management treats code as a black box. You define requirements. Engineering builds. You validate output. Repeat.
This breaks when velocity increases. More PRs. More features. More technical debt compounding faster than you can track it. The old feedback loop—plan, build, test, ship—assumes you have time to validate everything.
You don't. Not anymore.
Smart PMs in 2025 are adopting code-aware product management. They don't wait for engineering to surface problems. They see them in real-time by understanding code health metrics that actually matter.
What does this look like? Instead of asking "Is search fast enough?" you look at query complexity trends. Instead of guessing which features are breaking prod, you track churn in specific modules. Instead of wondering if your mobile team is overloaded, you see ownership concentration across your iOS codebase.
Platforms like Glue make this stupidly simple. Point it at your repo. It indexes your codebase, discovers features through AI analysis, and surfaces health metrics (complexity, churn, ownership gaps) without requiring engineers to write a single doc. You get a living map of what exists, who owns it, and where risk is accumulating.
This isn't about micromanaging engineers. It's about making informed decisions faster.
Feature Discovery Beats Feature Planning
Here's a dirty secret: half the features your team ships don't make it into your product docs. They get built as side effects of bug fixes, customer requests, or an engineer's good idea during implementation.
This creates institutional blindness. You can't improve what you can't see. You can't prioritize technical investment if you don't know what features exist in your codebase vs. what you think exists.
Most companies solve this with mandated documentation. "Every feature needs a spec doc and an engineer-written README." Great in theory. Never works in practice. Documentation rots the moment it's written because maintaining it has zero velocity impact.
The 2025 approach flips this: automated feature discovery from code.
AI models can now analyze codebases and infer features by reading function names, API endpoints, UI components, test descriptions, and commit messages. They're not perfect, but they're 80% accurate and infinitely more current than your Confluence wiki.
I tested this on a fintech product I advise. Their documented feature set: 47 capabilities. Actual features discovered via code analysis: 83. The gap included things like "partial refund support" (built 8 months ago), "webhook retry logic" (critical but never documented), and "admin impersonation mode" (a security risk nobody in product knew existed).
Once you have a real feature inventory, you can run competitive gap analysis properly. Not by guessing what competitors do, but by comparing actual implementations. You can spot redundant features built by different teams. You can identify orphaned code nobody owns.
Glue handles this automatically. Feed it your codebase and your competitors' documentation. It maps what you have, what they have, and where the gaps are. Then it links each feature back to the actual code, so you can assess effort to close gaps or kill redundancies.
Velocity Is a Lagging Indicator
Product managers love velocity metrics. Story points per sprint. PRs merged per week. Deployment frequency. These feel objective. They're not.
Velocity tells you how much code shipped. It doesn't tell you if that code was smart or stupid. High velocity with rising complexity is worse than low velocity with stable architecture. But most PMs only see the velocity number.
Here's what matters more in 2025:
Churn rate by feature. How often does code in a specific feature area change? High churn means instability. Could be bugs, refactoring, or architectural thrash. Low churn in critical features means they're solid. Low churn in new features might mean abandonment.
Complexity trends. Is your codebase getting simpler or more tangled? Cyclomatic complexity sounds academic, but it directly correlates with bug rates and onboarding friction. Watch complexity grow in your checkout flow? Expect conversion drops and slow feature development.
Ownership concentration. How many engineers touch each part of your product? Too few means bus factor risk. Too many means coordination overhead. The sweet spot is 2-4 people per module. Deviations signal problems.
These aren't metrics you calculate manually. Tools like Glue index your git history and surface them in dashboards PMs can actually parse. You see which features are stable vs. brittle. Which teams are overloaded. Where technical debt is accumulating.
This changes how you prioritize. Instead of "let's build feature X because the roadmap says so," you ask "can we afford to build X given that the team owning that area is already maintaining Y, which is churning at 3x baseline?"
The MCP Revolution: PMs Who Code (Sort Of)
Here's a trend that'll separate elite PMs from the rest: MCP (Model Context Protocol) integration.
MCP lets AI assistants (Cursor, GitHub Copilot, Claude) directly query your codebase, documentation, analytics, and product data. Instead of asking an engineer "How many users hit the rate limit last week?", you ask Claude. It queries your observability stack, your database, and your code—then answers with context.
This isn't about PMs becoming engineers. It's about closing the feedback loop between product questions and technical answers without human latency.
Example: You're deciding whether to invest in a premium tier. You ask Claude (connected via MCP to Glue and your analytics): "What percentage of our power users trigger API rate limits, and how complex would it be to add tiered limits?"
Claude responds: "23% of users in the 95th percentile hit limits monthly. Rate limiting logic is in middleware/rateLimit.ts, owned by platform team, 450 LOC, moderate complexity. Last modified 4 months ago. Estimated effort: 3-5 days for tiered implementation based on similar recent changes."
You just got an answer in 10 seconds that would normally take a Slack thread, two meetings, and a JIRA ticket.
Glue's MCP server exposes your codebase's feature map, health metrics, and ownership data to AI assistants. PMs can ask questions like "Which features haven't been touched in 6 months?" or "Show me all API endpoints related to billing" and get instant, accurate answers.
This doesn't replace engineers. It removes the friction of trivial questions so engineers can focus on hard problems.
Team Insights Trump Roadmap Decks
The best PMs I know in 2025 spend less time on roadmaps and more time understanding team dynamics through code.
Who's contributing to what? Are certain engineers becoming knowledge silos? Is one team constantly fixing another team's bugs? Are senior engineers spending time on grunt work while junior engineers tackle architecture?
You can see all of this in code. Commit patterns reveal contribution balance. Churn by author shows who's refactoring vs. patching. File ownership concentration exposes knowledge distribution.
Most PMs ignore this because it feels like engineering management territory. But product velocity is downstream of team health. If your mobile team is burning out maintaining a legacy module, your mobile roadmap is fiction.
Code intelligence platforms surface these patterns without invading engineering's space. You're not tracking individual performance. You're identifying structural issues that affect delivery: orphaned code, overloaded modules, knowledge gaps.
One PM I know used this to kill a major feature. Her team was excited about a marketplace initiative. Code analysis showed the team that would own it was already maintaining 40% more code than any other team, with churn rates 2x baseline. They were drowning. Leadership didn't know because sprint velocity looked fine.
She presented the data: "We can build the marketplace, but it'll break the team that owns payments and checkout. Here's the ownership concentration. Here's their churn rate. We need to redistribute load first or delay the marketplace."
They redistributed. Three months later, they launched the marketplace without burning anyone out. Data-driven prioritization at its finest.
What This Means for You
If you're a PM still operating in the plan-build-ship paradigm without code awareness, 2025 is going to hurt. Your competitors are moving faster because they've closed the loop between product strategy and code reality.
Start small:
Get visibility into what actually exists. Run feature discovery on your codebase. Compare it to your product docs. Find the gaps.
Track health, not just velocity. Watch churn, complexity, and ownership concentration for features you care about. Surface risks before they become fires.
Adopt MCP-powered workflows. Connect your AI assistant to your codebase. Ask questions directly. Eliminate the latency of asking engineers for context.
You don't need to become an engineer. You need to become code-literate enough to ask better questions and make faster decisions.
The future of product management isn't better roadmaps. It's real-time awareness of the gap between what you planned and what exists. The PMs who master that gap will dominate 2025.