Blog 105: AI systems for product management - How AI PMs are building their edge?

Every few years, a new “must-have” system for product management comes up. JIRA dashboards, Notion templates, OKR boards, growth funnels, etc. Currently it's AI systems.

Everyone’s talking about them.

Few are actually building them well.

And even fewer are truly using them to create product advantage rather than operational comfort.

Let’s break down what AI systems for product management really are, and how the best teams are using them.

What are AI systems in product management, really?

“AI in PM” used to mean automating routine work – summarising user feedback, drafting PRDs, or suggesting backlog priorities.

Now, AI systems are living frameworks that evolve with your product. They:

• Learn from product telemetry, customer feedback, and user journeys

• Predict bottlenecks before they happen

• Help PMs make high-value decisions, not just faster ones

Think of it as your product brain: always awake and learning. (In addition, refer to blog 101 on how GenAI is being leveraged in product management).

Three layers of the AI-Powered PM systems

Let’s visualise this as the AI-PM systems pyramid:

Each layer adds a different kind of intelligence:

1. Analytical AI systems

   Aggregate and synthesise product data.

   Example: Amplitude’s AI insights engine (Amplitude Blog, 2025) identifies which behaviours correlate with retention — something human PMs might miss.

   
   

2. Operational AI systems

   Optimise work at the team level.

   Example: Linear’s AI prioritisation assistant uses signals from comments, and deadlines to assist product teams (Linear Updates, 2025).

   
   

3. Strategic AI systems

   Align org-wide decisions with customer and market shifts.

   Example: Notion’s AI for work scans customer research and aligns upcoming roadmap items with user trends and company OKRs.

   
   

From “assistants” to “systems”

The smartest product leaders aren’t adopting AI as chatbots.

They’re designing AI systems with intent – where automation supports judgment, not replaces it.

Consider, 

1. Figma: They’ve built an internal AI that observes feature usage and user drop-offs, then surfaces potential UX pain points for review before releases. It’s not just a tool; it’s a feedback nervous system.

2. Airbnb’s AI Demand Model: Dynamically recommends listing categories to product managers when consumer travel patterns change (Airbnb Engineering Blog, 2025).

The insight? These companies aren’t “using AI” – they’re building systems that are trained continuously.

Word of caution – be aware of the pitfalls of AI. The best PMs treat AI systems as decision partners, not deciders.

Building your own AI system as a PM

Here’s a practical approach to designing your own AI-driven PM workflow:

1. Define one key problem area (e.g., prioritisation, churn prediction, feedback synthesis).

2. Integrate a feedback loop – connect user behaviour data with your hypothesis engine (Amplitude, Mixpanel, etc.).

3. Automate the repetitive layer – e.g., backlog triage, PRD summaries, or qualitative tagging.

4. Keep the judgment layer human. Every insight should still go through contextual review.

5. Document the system – treat your AI processes like internal products.

   
   

In general, the most powerful AI systems are designed, not purchased.

To conclude, instead of asking “What tool can make me faster?”, the question you must ask yourself is “What system can make my team smarter?”

AI systems aren’t the future of product management – they can be the infrastructure for it.

Jump to blog 100 to refer to the overall product management mind map.

I wish you the best for your journey. 😊