AI and Product Management: Smarter, Not Replaced

As AI continues to revolutionize the way we work, product management is no exception. The role of the Product Manager (PM) is evolving, and it’s clear that shortly, every PM will move towards embracing AI and becoming an AI PM. But what does that mean in practical terms? Here’s what I’ve learned from Lenny's podcast I recently listened to, including thoughts shared by AI expert Marily Nika.

AI is a Tool, Not a Replacement

The fear is, of course, that AI will replace human roles, including that of the PM. But that's far from reality. AI is here to make the jobs of product managers easier, to assist in execution and automate mundane tasks, freeing up our time for high-level strategy. It means that AI will not replace PMs but will make us more productive by performing all the time-consuming tasks, freeing our time for ideation, problem-solving, and strategic thinking.

The Human Element: Ideation & Prompts Still Matter

The key thing I want to drive across, which Marily Nika mentioned is that even though AI programs like ChatGPT are great in building on that foundation like editing and fine-tuning content, be it mission statements or project documentation, the genuine creativity comes from us. This ideation, vision, and these basic prompts of creativity belong to the PM. We, as PMs, must own the vision and define the problems that AI can help solve.

Focus on Adding Smart Features and Don’t Overuse AI in Early Stages (Especially MVPs)

AI can be truly transformative, but it has to be applied only where needed by the AI PM. The philosophy must always be to enhance the user experience with intelligent features that truly add value. Be it smart recommendations, automation, or predictive insights, the use of AI is to sprinkle those smart touches throughout the product. It’s tempting to incorporate AI into every feature, but doing so can lead to over-engineering and bloated products. Instead, AI should be applied strategically to make meaningful improvements.

Many are tempted to use AI from the outset, but when building an MVP (Minimum Viable Product), AI can often add unnecessary complexity. MVPs are meant to test core features quickly and efficiently. Incorporating AI too early in the process can increase costs (computing resources, development time) and hinder agility.

Becoming a Strong AI PM:

• An AI PM needs a foundational understanding of how AI works—its capabilities, limitations, and ethical considerations. This includes knowing what data is needed for machine learning models, the impact of algorithmic biases, and the infrastructure required to support AI functionalities.

• Successful AI PMs prioritize problem-solving over product-building. AI is a tool, and as a PM, you need to understand what problem you are solving and how AI can be leveraged to solve it better. The goal isn’t to create an AI product, instead, it’s to solve user pain points, and AI can be a powerful enabler in that journey.

• AI opens the door to countless possibilities. Be it the automation of processes, offering experiences tailored to users, or predicting user behavior, AI empowers PMs to explore solutions that were earlier beyond their reach. The mindset of an AI PM should be open and creative, seeking an explanation continuously for how AI may drive innovation and solve complex problems.