We’ve seen this repeatedly: companies get excited about sophisticated AI models, but what drives real adoption is solving real problems. For example, when users say “make my writing better,” they don’t care if you’re using a large language model or clever rules – they care about getting clearer, more effective text.

Here’s what makes AI product management different:

You need to manage uncertainty in new ways. Traditional software is deterministic – if you input A, you get B. But with AI, you might get B, or B-ish, or sometimes Q. We’ve learned that setting clear user expectations is crucial. When Spotify recommends music, they don’t promise perfect recommendations – they create an experience that makes discovery fun and low-risk.

You have to think differently about testing. Traditional A/B testing still matters, but you’re also watching for model drift, bias, and edge cases that could hurt users. When GitHub launched Copilot, they didn’t just test if it wrote code – they had to ensure it wrote secure, ethical code that respected licensing requirements.

And let’s have some real talk about ethical considerations! You’re not just asking “can we build this?” but “should we build this, and how can we build this safely?”

We’ve seen AI products stumble and cause real harm because they didn’t consider their societal or safety impacts early enough. Your role includes being an ethical steward, thinking through potential misuse and unintended consequences.

Coming up in the next few years, you’ll need to tackle:

• Increasing demands for AI transparency
• Evolution of AI regulations and compliance requirements
• Growing user sophistication about AI capabilities and limitations
• The need to differentiate in an increasingly crowded AI market

Want to be better prepared? Start by deeply understanding your users’ problems before jumping to AI solutions. Build strong relationships with your data science team – you’ll need to speak their language. And always keep ethical considerations at the forefront of your decision-making.

Remember: great AI product management isn’t about having the most advanced technology. It’s about creating trustworthy solutions that make your users’ lives better. Keep that as your north star, and the technical decisions become much clearer.

What aspects of AI product management would you like to explore further? Whether it’s user research methods, measuring success, or managing stakeholder expectations, reach out so we can dive deeper into any area that interests you.