Blog 101: GenAI in product management - Superpower or Shortcut?

In product management circles, there’s always that one buzzword.

Earlier, it was ‘growth hacking,’ ‘design thinking,’ ‘north star metrics,’ etc.

But in 2025, the primary buzzword is Generative AI or GenAI.

Now, most PMs are still either overhyping it (“GenAI will completely replace us”) or underestimating it (“It’s just an advanced search engine”). Both are wrong.

The truth is simpler: It’s a tool. A powerful one. If used right, can take you from good PM to great PM — the kind who ships products that matter.

Generative AI has shifted from buzzword to backbone. PMs aren’t just asking “Should we use AI?” - they’re asking, “Where should we stop using AI?”.

Let’s learn how GenAI is really transforming product management in 2025, with examples, cautionary tales, and a playbook you can steal for your next roadmap review.

The Attraction: AI as a Product Manager’s magic wand

GenAI is the shiny tool that promises to compress months into minutes:

• Customer insights: Summarising thousands of feedback tickets.

• Experimentation: Instantly generating multiple copy/design variations.

• Road mapping: Predicting likely adoption curves from historical data.

However, just because AI can accelerate something doesn’t mean it should replace your judgment.

Case Study 1: Klarna’s AI Assistant

When Klarna rolled out its AI-powered customer service assistant in 2024, the headlines were glowing: 2.3 million chats handled in its first month (source: PRNewswire).

It was a textbook example of AI-powered execution:

• High-frequency use case

• Measurable cost savings

• Fast global scale

But here’s the caution: Klarna itself admitted it had to hold back from over-reliance. Why? Because not every customer issue fits neatly into an LLM-powered script. Some queries require empathy, escalation, or just a human ear.

The lesson: AI is phenomenal for volume and efficiency — but risky when nuance and trust are involved.

Case Study 2: Spotify’s AI DJ

Spotify’s AI DJ started as a personalisation experiment. By 2025, it evolved into voice requests for DJ (source: Spotify Newsroom).

This wasn’t just “AI for AI’s sake.” It was tied to a clear user behaviour: people were already comfortable using Alexa and Siri. Spotify simply collapsed friction by making DJ conversational.

But here’s the danger: personalisation is a double-edged sword. Get it right, and it’s magical. Get it wrong, and you can creep out users.

The Cautionary Checklist for PMs

Before putting “AI-powered” on your next slide deck, ask:

• Does this solve a real user pain point? Or is it AI FOMO?

• Do we have quality data? Garbage in → fancier garbage out.

• Where do humans still need to stay in the loop?

• What’s the failure mode? (e.g., misinformation, bias, or just plain user frustration).

  
  

👉 Pro tip: Always ask, “What happens when the AI gets it wrong?” If you don’t have a safe fallback, you’re setting yourself up for trouble.

The Playbook for PMs in 2025

If you’re considering how GenAI fits into your roadmap, here’s a practical checklist:

1.     Anchor in pain points, not hype

Don’t add “AI-powered” unless it solves a real customer problem.

2.     Start narrow, scale wide

Klarna began with high-volume support chats - not every problem.

3.     Keep humans in the loop

Define scenarios when empathy, ethics, or judgment must override automation.

4.     Design for graceful failure

If the AI gives the wrong answer, how do you recover without losing trust?

5.     Measure not just efficiency, but trust

Speed is good. Satisfaction and loyalty are better.

A PM's GenAI Playbook in 2025

Once you have decided to go ahead with adopting GenAI, here’s what you can (and must) do to move from curiosity to action:

1. Select a high-frequency, user-pain workflow

   Example: refunds, returns, or common support queries.

2. Gather & clean data + user feedback

   Historical logs, common failure points, user complaints — map these clearly.

3. Build a hybrid prototype

   Let AI handle most (70-80%) of cases, with humans in for edge / escalation.

4. Specify and track metrics

   • Speed: resolution time

   • Quality: CSAT / NPS / repeat issues

   • Cost: human hours saved vs overhead of AI infrastructure

5. Expand with care

   Localize language, voice, and context. Add personalization. Keep reviewing and monitoring.

   
   

Final Thoughts

GenAI is here to stay — but not every product needs to be AI-first. The best PMs in 2025 are not the ones automating everything; they’re the ones knowing where to let AI run free and where to pump the brakes.

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

I wish you the best for your journey. 😊