The Role of AI in Product Management

🤑 The AI Hype and Reality Check

In recent years, the buzz around Artificial Intelligence (AI) has reached a fever pitch. Companies are scrambling to integrate AI into their products, often for the sake of being perceived as innovative rather than solving real user problems.

AI is being thrown into existing products, simply because it can. There might be real value hidden there, but because of the rush, the implementations often seem baffling.

As a Product Manager, even if you're working at the bleeding edge of innovation, you have to ask yourself if users are left in a better place than they were without your AI feature

This trend underscores a crucial point for product managers: the focus should always be on solving real user problems, with AI serving as a means to an end, not the end itself.

AI has tremendous opportunities to help people be more productive, and transform the way we use computers and products, but just like companies had to find out what made good websites and applications in the .com boom/bubble, we have to do the same with AI.

AI really is a game changer

I'm not a luddite. AI enables us to build products that we would never be able to build without it, and some of those products are almost magic in nature. But as part of the gold rush, we're just also seeing an abundance of really bad examples, that if it weren't for the AI buzzword, would likely have been killed off before they even ended up being seen by users.

🤖 The Product Manager's AI Toolkit

As a product manager navigating the AI landscape, it's crucial to have a toolkit that helps you separate hype from reality. Here are some essential tools and approaches:

• User-Centric Design: Always start with user needs. Don't start with the solution. AI should enhance, not complicate, the user experience.
• Data Strategy: Understand what data you have, how you can use AI to enhance that data to give a better experience. What you more data you need, and how to ethically collect and use it.
• Iterative Testing: Implement AI features gradually, testing and learning at each step.
• Kill your darlings: At least half of all ideas doesn't work out. If you went ahead with something, be quick to remove what clearly doesn't work.

AI Integration: The Good, The Bad, and The Ugly

Let's look at some real-world examples of AI integration in products:

😍 The Good:

Netflix's Recommendations

Netflix uses AI to personalize content recommendations, significantly enhancing user experience and engagement. The key here is that the AI works in the background, seamlessly improving the core product offering.

Spotify's Discover Weekly

Spotify leverages AI to create personalized playlists, introducing users to new music based on their listening habits. This feature has become a beloved part of the Spotify experience, demonstrating how AI can add significant value when focused on user needs.

Grammarly's Assistant

Grammarly uses AI to provide real-time writing suggestions, improving users' grammar, style, and tone. The AI seamlessly integrates into various writing platforms, enhancing the user's existing workflow rather than disrupting it.

😔 The Bad (or problematic):

AI-Generated Customer Service Responses

Many companies have implemented AI chatbots for customer service, but when poorly executed, these can frustrate users with irrelevant or nonsensical responses. The lesson? AI should complement, not replace, human interaction in sensitive areas.

Overaggressive Content Moderation AI

Some social media platforms use AI for content moderation, but these systems often flag or remove benign content while missing actual violations. This demonstrates the importance of maintaining human oversight in complex decision-making processes.

We also see this being gamed, where bot networks are used to report content, resulting in take downs based on quantity alone.

AI-Powered Resume Screening

Many companies use AI to screen job applications, but these systems can inadvertently perpetuate biases and miss qualified candidates. This shows the need for careful testing and monitoring of AI systems, especially in high-stakes applications.

(It's also easy to game, but that's a different problem.)

🤬 The Ugly:

Clippy 2.0?

Remember Microsoft's Clippy? Imagine a modern AI version popping up in your work documents, offering unsolicited advice. This not-so-hypothetical example is starting to pop up in a lot of products, and reminds us that AI features should be unobtrusive and genuinely helpful, not annoying distractions.

AI-Generated Social Media Posts Gone Wrong

Some brands have experimented with using AI to generate social media content, leading to tone-deaf or nonsensical posts that damage brand reputation. This illustrates the importance of maintaining human oversight and understanding the limitations of AI in creative tasks.

LinkedIn's AI-Generated Post Questions

LinkedIn introduced AI-generated questions underneath posts, ostensibly to encourage engagement. However, these questions often miss the point of the post, add no value, and can be distracting or even annoying to users. This demonstrates how AI features, when not carefully implemented, can detract from rather than enhance user experience.

Strategies for Effective AI Integration

As a product manager, how can you ensure your AI integrations add real value? Here are some strategies:

1. Start with the Problem, Not the Solution: Identify user pain points first, then consider if AI can provide a meaningful solution. Don't get me wrong, we're seeing amazing products where AI is at the very center. But they're still solving a real problem, and is not made in a tacked on fashion.
2. Embrace Incremental Implementation: Don't try to boil the ocean. Start with small, impactful AI features and iterate based on user feedback. (Of course you might need a big AI feature to solve your problem, but then see 1. and know that you're probably taking a big bet, and think about how you can risk manage that bet).
3. No AI here: When AI works, you don't call it AI. Netflix isn't calling their personalized recommendations "POWERED BY AI". Why? Because no one cares.
4. People like being in control: Use AI to enhance human capabilities, not to replace human judgment entirely.
5. Plan for Continuous Learning: We're seeing new AI models at an incredible pace. What didn't work out yesterday might actually work tomorrow.

📊 Measuring AI Success: Metrics and KPIs

Understanding how to measure the success of AI features is crucial for product managers. Here are some key metrics I would use:

• User Engagement: Are users interacting with AI features? How often?
• Impact on Core Metrics: How are AI features affecting your product's key performance indicators?
• Time Saved: Are AI features making users more efficient?
• User Satisfaction: Has the introduction of AI features improved overall user satisfaction?

Remember to use A/B testing strategies to accurately measure the impact of your AI features.

The Future of AI in Product Management

As AI continues to evolve, so too will the role of the product manager. Here are some trends to watch:

• AI-Assisted Decision Making: AI tools may help PMs analyze user data and market trends more effectively.
• Personalization at Scale: AI will enable hyper-personalized product experiences.
• Dashboards on demand: One thing AI is really good at, is making sense of lots of data. We're already starting to see Claude.ai being able to generate on-demand dashboards. How soon before this will be a standard in most SaaS tools?
• Predictive Product Development: AI could help forecast user needs and market shifts, informing product roadmaps.
• Ethical AI Management: PMs will need to become well-versed in AI ethics and governance.
• AI Security: Product teams will at the very least need to know about AI security, and how to guard against prompt injections.

Conclusion: Navigating the AI Revolution

The AI revolution in product management is not about blindly integrating AI into every feature. It's about thoughtfully leveraging AI to solve real user problems and create meaningful experiences. As product managers, our role is to be the bridge between technological possibilities and user needs, ensuring that AI serves as a powerful tool for innovation, not a gimmick.

Remember, at the end of the day, we're not building AI products – we're building products that use AI to deliver exceptional value to our users.