Automated Standups: Pulls updates from tasks and generates standup summaries. Sounds great until you realize it's just reformatting what people already typed. If your engineers aren't updating tasks (and let's be honest, most aren't), you get garbage summaries.
Q&A Across Workspaces: You can ask "What's the status of the mobile redesign?" and it searches across all your spaces. This is genuinely helpful for PMs juggling multiple projects. It's essentially search with natural language.
Progress Summaries: Automatically generates sprint summaries, project updates, status reports. Again, this is just aggregating data that's already in ClickUp. If that data is stale or wrong, the AI can't fix it.
Here's what Brain doesn't do: It can't tell you if a task is actually feasible. It doesn't know that "Add real-time collaboration" is a 6-month project, not a 2-week sprint. It has no idea what code already exists or what technical debt you're dealing with.
Monday's AI Is Even More Surface-Level
Monday calls their AI features "WorkOS AI." It's included in their Enterprise plan, which means you're probably paying $16-24 per seat per month minimum.
Formula Generation: You can describe what you want in plain English and it generates Monday formulas. "Calculate days until deadline" becomes a formula. This is nice for non-technical PMs but hardly groundbreaking.
Automated Text Generation: Write project briefs, meeting notes, email updates. Same as every other AI writing tool.
Smart Item Creation: Type "Create 5 tasks for user research sprint" and it generates them. Here's the problem: the tasks are generic as hell. You get "Conduct user interviews," "Analyze findings," "Create report." Wow. Groundbreaking.
AI-Powered Search: Search your boards with natural language. You'd think this would be standard search functionality in 2024, but apparently it requires AI now.
The most telling thing about Monday's AI? Their own case studies focus on automating status reports and generating update emails. Nothing about helping teams make better technical decisions.
Asana's AI Studio: The Most Ambitious, Still Missing the Point
Asana Intelligence is part of their Business and Enterprise plans ($24.99+ per user/month). They've gone further than the others in some ways:
Smart Goals: Suggests relevant goals based on your project. If you're building a mobile app, it might suggest goals around app store ratings or performance metrics. This is actually useful context.
AI Teammate Creation: You can create "AI teammates" that monitor specific projects and send you updates. Think of it as a bot that pings you when certain conditions are met. More sophisticated than a simple notification rule, but still just reacting to data you've manually entered.
Workflow Recommendations: Analyzes how your team uses Asana and suggests process improvements. "Your team often misses deadlines in Sprint Planning—try adding a buffer day." This is the closest any of these tools get to actual intelligence.
Smart Summaries: Like the others, generates project summaries and status updates. Asana's are slightly better because they can pull from multiple projects and show dependencies.
But here's what Asana's AI can't tell you: Which of those dependencies are actually blockers? Is "waiting for API integration" a real problem or is the API already built and nobody updated the task? Should you actually ship this feature or is it built on top of code that's about to be deprecated?
The Fundamental Problem: These Tools Don't Understand Code
All three platforms operate in their own bubble. They know about tasks, comments, due dates, and who's assigned to what. They're incredible at organizing that information and even generating useful summaries of it.
But they're completely blind to the actual work.
When your backend engineer updates a task to "blocked by database migration," Monday's AI can't tell you that the migration script is already written and tested—it just needs a 5-minute deploy window. Or that the migration is going to take 3 days because nobody's looked at the schema dependencies.
When a task says "Refactor user authentication," ClickUp Brain can't tell you that authentication code is spread across 47 files, has been modified by 12 different engineers, and touches every major feature. It doesn't know this is actually a 6-week project with massive risk.
Asana's AI might notice that "Fix mobile performance" has been in progress for 3 sprints and suggest you review the timeline. But it can't tell you that the actual performance bottleneck was fixed 2 weeks ago and everyone just forgot to update the task.
What Code Intelligence Actually Looks Like
This is where something like Glue becomes relevant. I'm not saying ditch your PM tool—keep using whatever your team likes. But these tools need to know about your codebase to be genuinely useful for engineering decisions.
Real code intelligence means understanding:
What actually exists: When someone says "we need to add OAuth," knowing if you already have OAuth infrastructure for other features. Not just searching for the word "OAuth" in comments, but understanding what code patterns exist and what they're capable of.
Who knows what: Not just who's assigned to a task, but who actually wrote the authentication code, who's modified it recently, who would be the best person to review a change. This isn't in your PM tool because it's not in git commits either—you need to analyze actual code patterns.
What's actually complex: Task says "update pricing logic." Your PM tool sees one task. Code intelligence sees that pricing logic is spread across 3 services, 12 database tables, and has conditional logic that nobody fully understands. This is the difference between a 2-day task and a 2-week project.
Technical debt that matters: Every codebase has technical debt. But which debt actually blocks new features? Your PM tool can't tell you that the "simple" feature you're planning requires refactoring code that hasn't been touched in 2 years and has no tests.
This is where Glue fits in. It indexes your entire codebase—files, functions, API routes, database schema—and builds a map of what actually exists. When you're planning a sprint in ClickUp or Asana, you can actually check if the technical foundation for these features exists or if you're about to commit to impossible timelines.
The Cost Reality Check
Let's talk about what you're actually paying:
ClickUp: $5-19/user/month + $5/month for AI = $10-24/user/month
Monday: $12-24/user/month (AI included in higher tiers)
Asana: $13.49-24.99/user/month (AI included)
For a 20-person product + engineering team, you're spending $3,600-6,000 annually for AI features that mostly just reformat information you already entered.
Compare this to the cost of building features based on wrong assumptions. Your PM commits to a 2-week timeline for something that actually takes 6 weeks. You miss the deadline, leadership loses confidence, or you ship technical debt that costs months to fix later.
The AI in these PM tools can't prevent that. It's optimizing the wrong layer.
Which One Should You Use?
If you're asking which PM tool has the best AI, you're asking the wrong question.
Pick your PM tool based on how your team actually works:
Use ClickUp if you want everything in one place—docs, wikis, tasks, time tracking. Their AI is fine but not the reason to choose it. The real value is consolidation.
Use Monday if you need visual dashboards and your stakeholders love colorful charts. The AI features are whatever, but the visualization layer is solid.
Use Asana if you want the cleanest interface and best mobile experience. Their AI is slightly more sophisticated than the others, but still limited to the task layer.
But regardless of which you choose, you're missing the connection to actual code. Your PM tool shows tasks moving across a board. Glue shows you what code exists, who understands it, where the complexity lives, and what's actually feasible to build.
The best setup? Use whichever PM tool your team already likes, but add code intelligence that actually understands your engineering reality. Let ClickUp/Monday/Asana handle task management. Let something that actually indexes your codebase handle technical decisions.
The Real Value of AI for Product Teams
Here's what actually matters: Can you answer these questions when planning your next sprint?
Do we already have code that does something similar?
Who actually understands this part of the system?
What's the real complexity here, not just what the task says?
Are we building on solid foundation or technical debt?
What features do our competitors have that we're missing?