By Sahil Singh, Founder of Glue
I have watched dozens of product manager AI assistant tools launch over the past two years, each promising to transform how PMs work. And 98% of product managers are now using AI in some form, according to a 2025 survey from General Assembly. That stat sounds impressive until you look at what they are actually using it for: drafting user stories, summarizing meeting notes, editing Slack messages. The bar for "AI-assisted product management" is sitting on the floor.
When I was building engineering teams before founding Glue, I kept seeing the same failure mode. Product managers would adopt new tools with enthusiasm, use them for surface-level tasks, and then wonder why their roadmaps still slipped and their estimates were still wrong. The tool was not the problem. The tool was solving the wrong problem.
So if you are a PM shopping for an AI assistant in 2026, this guide is designed to help you avoid the traps most buyers fall into and find a tool that actually changes how you make decisions.
The Current State of AI for PMs
Product managers are now among the heaviest AI users in organizations. General Assembly found that the average PM touches AI tools 11 times per day. That frequency sounds meaningful. But frequency is not impact.
Most of that usage clusters around content generation: writing PRDs, summarizing research, rephrasing communications. These are real tasks, and automating them saves real time. But they are also tasks that sit at the edges of what PMs actually do. They are not the work. They are the artifacts of the work.
The core of product management is making decisions about what to build, when to build it, and why. Those decisions depend on understanding your system, your customers, your market, and your constraints. Ask yourself: is your AI assistant helping you with any of those?
A Lenny's Newsletter survey found that only 1.1% of PMs use AI tools for roadmap planning. That number is revealing. It means that despite constant AI adoption, almost no one trusts their AI tools with the work that actually determines product outcomes.
Where the Usage Actually Lands
Most PM AI assistants today fall into a few buckets. There are writing assistants like ChatGPT and Jasper, which help you draft documents faster. There are analytics tools that summarize dashboards. There are meeting tools that generate action items.
Each of these is useful in isolation. None of them knows what is in your codebase. None of them can tell you whether the feature you are planning will take two weeks or two months based on real architectural complexity. None of them can answer the question: "What do we already have that does something like this?"
What Most Tools Get Wrong
The biggest gap in product manager AI assistants is not intelligence. It is context.
When a PM asks ChatGPT to help draft a PRD, the output sounds reasonable but is disconnected from reality. The AI does not know that your billing system uses a legacy payment processor that is tightly coupled to three other services. It does not know that the "quick feature" your CEO suggested actually requires touching 47 files across 4 microservices. It generates plausible-sounding specs that engineers read and immediately discard.
This is the fundamental problem: most AI tools for PMs operate in an information vacuum.
The Context Gap
According to the Lenny's Newsletter survey, 92.4% of product managers who use AI report meaningful downsides, including inaccuracy, over-reliance, and outputs that sound confident but are wrong. That number should concern anyone building or buying PM AI tools.
The root cause is that these tools lack access to the one thing that grounds product decisions in reality: your actual codebase. Your system architecture, feature inventory, dependency graph, code health, and technical debt are all invisible to generic AI assistants.
I have seen this play out repeatedly. A PM uses AI to estimate a project at three sprints. Engineering comes back with an estimate of eight. The PM is frustrated. The engineers are frustrated. The AI tool generated a confident number based on... nothing. It had no access to the system it was estimating against.
The Surface-Level Trap
There is a second problem that is more subtle. Many AI tools solve problems that feel productive but do not actually move the needle.
Generating a nicely formatted user story 3x faster is a time saver. But if the user story is based on incorrect assumptions about what your system can and cannot do, the speed is meaningless. You have produced the wrong artifact faster.
This is what I call the "productivity illusion" in PM tooling. You feel busy. You produce more output. But the decisions behind that output have not improved.
What to Actually Look For
If you are evaluating a product manager AI assistant, the most important question is not "What can it generate?" It is "What does it know?"
Codebase Awareness
The single most valuable capability a PM AI assistant can have is the ability to understand your actual codebase. Not in theory. Not from documentation that was last updated six months ago. From the code itself.
This means the tool should be able to answer questions like:
- "How does our checkout flow work?"
- "What features do we already have in the billing module?"
- "Which files would need to change to add a referral program?"
- "What is the technical complexity of implementing feature X?"
If the AI cannot answer these questions grounded in your real system, it is guessing. And you already have enough guessing in your planning process.
This is the problem Glue was built to solve. Glue connects directly to your Git repository, indexes your entire codebase, and builds a semantic understanding of your system. When you ask Glue a question, the answer is grounded in actual code, not hallucinated from training data. You can explore this approach further in our AI product management guide.
Feature Discovery
A good PM AI assistant should be able to tell you what you have already built. That sounds basic, but most teams cannot produce a complete feature inventory. Knowledge about what exists lives in engineers' heads, in scattered documentation, or nowhere at all.
The tool should automatically discover and catalog features, map them to the files that implement them, and show how they connect to each other.
Competitive Grounding
If you are doing competitive analysis, your AI assistant should be able to compare competitor capabilities against what you have actually built. Not against what your marketing site says you have built, but against what exists in the code.
Evaluation Framework
When I evaluate any PM tool, I use a four-part framework. I have found it more useful than feature checklists because it focuses on impact rather than capability lists.
1. Decision Quality
Does this tool improve the quality of the decisions I make? Not the speed of document production, but the accuracy and confidence of actual product decisions.
Test this by asking: if I use this tool to plan a feature, will the engineering estimate be closer to reality than without it?
2. Context Depth
How deeply does the tool understand my specific product? Can it answer questions about my system, or does it give generic answers?
Test this by asking the tool something only someone who has read your codebase would know.
3. Team Impact
Does this tool reduce the number of interruptions between product and engineering? Engineers at most companies lose significant time answering context questions from PMs. A good PM AI assistant should reduce that burden.
4. Compounding Value
Does the tool get more valuable over time as your codebase evolves? Or does it provide the same static value regardless of how your system changes?
Glue, for example, updates its understanding incrementally as you push new commits. The longer you use it, the more complete its understanding becomes.
Putting the Framework to Work
Score any tool you are evaluating on each of these four dimensions. Weight decision quality and context depth most heavily. A tool that scores a 9 on document generation speed but a 2 on context depth is not going to change your outcomes.
The PM AI assistant market is maturing quickly. The winners will not be the tools that generate the most text. They will be the tools that understand your product deeply enough to make your decisions better.
If you want to see what codebase-grounded AI assistance looks like in practice, try Glue free and ask it a question about your own system. The difference between a grounded answer and a generic one is immediately obvious.
Frequently Asked Questions
What should a product manager AI assistant do?
A product manager AI assistant should improve the quality of product decisions, not just the speed of document creation. The most valuable capabilities include answering questions about your codebase in plain language, discovering what features already exist, estimating complexity based on real architecture, and connecting competitive intelligence to your actual technical capabilities. Look for tools that are grounded in your specific system rather than generating generic outputs.
Which AI tools are best for product managers?
The best AI tools for product managers in 2026 depend on the problem you are solving. For codebase understanding and grounded decision-making, Glue provides direct access to your system's architecture and feature inventory. For general writing assistance, tools like ChatGPT and Claude are capable. For analytics summarization, tools like Amplitude AI work well. The key is choosing tools that address your biggest bottleneck, which for most PMs is the gap between product decisions and technical reality.
How do PMs use AI for strategic work?
Most PMs currently use AI for tactical work like writing and summarization, with only 1.1% using it for roadmap planning according to a Lenny's Newsletter survey. To use AI strategically, PMs need tools that understand their system deeply enough to inform prioritization, estimation, and competitive positioning. This means connecting AI to your codebase so it can answer questions like "What is the real cost of building this?" and "What do we already have that partially solves this problem?"