AI for Product Managers: 8 Essential FAQs That Reveal the Future
You're probably asking the wrong questions about AI in product development.
Most PMs fixate on "Can AI write our code faster?" or "Should we add ChatGPT to our product?" These are surface-level concerns. The real questions dig into the structural problems that kill products: invisible technical debt, features that ship but don't work together, competitive gaps you can't see because your engineers are too busy firefighting.
Here are the 8 questions that actually matter. And the uncomfortable truth about why most product tools can't answer them.
1. "What features do we actually have?"
Sounds basic. It's not.
Your roadmap says you shipped user permissions three months ago. Your docs mention role-based access control. But can you list every permission check in your codebase? Can you explain how they interact? Can you guarantee they're consistent across web, mobile, and API?
You can't. Because feature documentation lives in Notion or Confluence, disconnected from the code that implements it. Engineers build things slightly differently than specs described. Edge cases emerge. Six months later, nobody remembers exactly what got shipped.
Traditional product tools track intentions. They don't track reality.
This gap between "what we think we built" and "what actually exists" grows exponentially with codebase size. You end up with phantom features that sort-of work, actual features nobody documented, and a support team handling bugs for functionality that supposedly doesn't exist.
Code intelligence platforms like Glue solve this by automatically discovering features from your actual codebase. Not by reading your PRD — by analyzing files, symbols, API routes, and database schema. The AI agents trace execution paths and build a map of what genuinely exists, not what Jira tickets claimed would exist.
2. "Why does this 'simple' change take three weeks?"
Your designer mocks up a new filter option. One dropdown. Seems trivial.
Engineering estimates two weeks. You're confused. It's one dropdown.
What you don't see: that filter touches seven different database queries, three API endpoints, two caching layers, and a background job that hasn't been modified in eighteen months. The engineer who wrote the background job left. The caching layer uses a library with a critical security vulnerability. The database queries need optimization before adding another filter or performance tanks.
This is where PMs and engineers talk past each other. You see UI. Engineers see technical debt, coupling, and risk.
Most product tools show you tasks and timelines. They don't show you why those timelines exist. You need code-level intelligence: churn analysis (how often code changes), complexity metrics (how gnarly it is), ownership maps (who actually knows this code), and dependency graphs (what breaks when you touch this).
Without this visibility, you're negotiating blind. With it, you can ask better questions: "Is the two-week estimate because we need to refactor the caching layer first? Should we tackle that as separate technical debt work?"
3. "Who can answer questions about X?"
Your payment processing system throws weird errors at 2 AM. Who do you wake up?
The engineer who originally built it left eight months ago. Three people have touched it since, but only for minor bugs. Your team wiki lists someone as the owner, but they moved to a different team.
Knowledge distribution is invisible until you need it urgently. Then it's catastrophic.
Code intelligence reveals knowledge risks by analyzing who commits to what code, who reviews it, and how concentrated that knowledge is. If one person owns 90% of your authentication system and they're about to go on parental leave, you need to know now, not when something breaks.
This matters for product planning too. That ambitious feature requiring changes to your core data pipeline? If only one engineer understands that pipeline, your timeline just got a hidden dependency. Maybe you prioritize differently. Maybe you schedule knowledge transfer first.
4. "What's the blast radius of this change?"
You want to modify how customer IDs work. Small change, big impact.
Customer IDs touch everything: authentication, payments, analytics, support tickets, audit logs, third-party integrations. Change the format and you break twelve things you didn't know existed.
Dependency mapping sounds like an engineering concern. It's absolutely a product concern. You need to understand what's coupled to what when making roadmap decisions.
Glue builds these maps automatically from your codebase. You can see that modifying the user model affects 47 files across 8 different features. You can trace through to database schema changes. You can identify which API endpoints would need versioning.
This changes how you prioritize. Maybe that "simple" ID change isn't worth it. Or maybe it is, but you schedule it carefully, batch other breaking changes, and plan migration scripts first. Either way, you're making informed decisions instead of discovering problems in QA.
5. "What do competitors have that we don't?"
You browse competitor websites. You sign up for trials. You build feature comparison spreadsheets.
This is shallow research. Their marketing site lists features. You list yours. Looks competitive.
But marketing descriptions are lies by omission. They don't tell you how features work, how deeply integrated they are, or what edge cases they handle. You ship "real-time collaboration" because competitors have it. Yours updates every 30 seconds. Theirs uses operational transformation with conflict resolution. Not the same.
Gap analysis needs to go deeper than marketing bullets. You need to understand implementation approaches, not just feature existence.
Code intelligence makes this possible for your own product. You can analyze code complexity, see how features interconnect, understand technical approaches. This creates a baseline for honest competitive comparison. When you evaluate competitors, you're comparing architectural approaches, not marketing copy.
6. "Where's our technical debt?"
Your engineering team says everything is technical debt. Your VP says ship features.
Both are right and wrong. Not all technical debt matters equally. Some slows velocity permanently. Some affects only deprecated code paths. Some is scary but stable.
You need debt prioritization based on impact, not feelings. Code health metrics help: complexity trends over time, churn rates, bug density, test coverage, dependency age.
A module with high complexity, frequent changes, and low test coverage? That's a velocity killer. A module with high complexity but zero changes in two years? That's stable legacy code. Don't refactor it.
Product managers should care about this because technical debt is future roadmap constraint. If your checkout flow is a tangled mess, every new payment method takes twice as long. If your notification system is brittle, that "simple" reminder feature becomes a risky project.
Mapping code health to product areas makes technical debt a roadmap conversation, not an engineering complaint.
7. "Can we actually ship this by deadline?"
Your CEO commits to a launch date at a conference. Engineering says maybe.
The problem isn't estimation accuracy (though that's bad too). The problem is invisible dependencies. That new dashboard needs data from three different services. One service needs schema changes. Schema changes need migrations. Migrations need careful sequencing. Also, the engineer who owns two of those services is on vacation for half the sprint.
Realistic timelines require seeing the whole system: code dependencies, team capacity, knowledge distribution, concurrent work, technical debt in affected areas.
Most project management tools track tasks. They don't track the hidden complexity underneath tasks. This is why estimates are always wrong and deadlines slip predictably.
Code intelligence doesn't magically fix estimation. But it surfaces the factors that make estimates meaningless: high churn in the code you're modifying, knowledge concentrated in one person, dependencies on unstable modules, concurrent work that will cause merge conflicts.
8. "Should we build this or integrate a third-party solution?"
Classic build-vs-buy decision. You run the spreadsheet: building costs X engineer-months, buying costs Y dollars per month. Math seems clear.
Except the spreadsheet lies. Building isn't just initial development. It's maintenance, bug fixes, feature expansion, keeping up with standards, handling edge cases, security updates. Third-party solutions look expensive until you calculate true cost of ownership.
Code intelligence helps by showing maintenance burden of similar features. How often does your authentication code get touched? How many engineers contribute to it? How many bugs get filed? That's the realistic maintenance cost for building your own solution.
It also reveals integration tax. Adding a third-party service means API calls, error handling, rate limits, webhook management, data synchronization, fallback logic. You can analyze existing integrations to estimate this work accurately.
The Pattern You're Missing
These eight questions have something in common: they're all answered by understanding code as a living system, not as an abstraction.
Most PM tools treat engineering as a black box. Stories go in, features come out, timeline TBD. This worked when products were simpler. It doesn't scale.
Modern products are complex systems where everything affects everything else. You can't make good product decisions without understanding the system. Not at code-level detail — you're not debugging — but at architectural insight level.
This is why code intelligence matters for PMs. Not because you need to read code. Because you need to understand what questions to ask and recognize bullshit answers.
When engineering says a feature will take three weeks, you need to know if that's because of legitimate technical complexity or because they're padding for unknown risks. When they propose a refactor, you need to evaluate actual impact on velocity, not just trust the pitch. When you plan Q3 roadmap, you need to see knowledge risks, technical debt hotspots, and dependency constraints.
The future of product management isn't less technical. It's more technical but differently technical. You don't need to code. You need to see through the code to the system underneath. That's what separates PMs who ship from PMs who slide deadlines and blame engineering.
The answers to these eight questions exist in your codebase right now. Most product teams just don't know how to extract them.