Beyond the Button: Why Conversational AI Rewrites Your Product Management Job

As a VP of Product Management, I spend a lot of time thinking about artificial intelligence. I see it fundamentally changing how users interact with software, and, consequently, how we do our jobs as product managers.

I want to share my perspective on this big shift: the move away from clicking through screens and toward users simply asking for what they want.

How I Used to Work: Focusing on Clicks and Screens (GUI)

Think about the software we've built over the years, often with a Graphical User Interface (GUI).

In the past, much of my focus as a PM involved watching users interact with these GUIs. I studied how they clicked through screens, filled out forms, and navigated menus. I looked closely at how long specific steps took. My goal, like many of yours, was often to make each step in that visual process easier and faster.

🔍 Example 1: If I were managing an online shopping site, I would meticulously analyze how users found products, used filters, added items to their cart, and navigated the checkout screens. I'd be testing button placement and screen layouts.

The New Way I See: Users Talking to Software (Conversational AI)

Now, AI is introducing a different way for users and software to communicate.

Users increasingly use chatbots or voice assistants, which feels natural because it's how humans communicate. Instead of clicking through many steps, users can now state their goal directly. 

Think: "Book me a flight to New York," or "What meetings do I have tomorrow?" The AI system then needs to understand the user's request (their "intent") and work to achieve the desired outcome, hiding the complex steps.

I also see AI handling different types of information much better. This includes not just structured data, but also unstructured text, voice commands, images, and even video.

How This Changes Our PM Role: Less Focus on How, More on What

From my viewpoint, this shift significantly changes the classic PM job description:

👉 Less need for detailed click-tracking: If users state their goal directly, I find studying their exact click path becomes less critical for many tasks. Obsessing over the perfect button placement isn't relevant if the interaction is primarily conversational.

👉 Focus shifts from process to outcome: I believe success is now measured more by whether the AI correctly understood the user and delivered the right result, rather than how efficiently the user navigated a set of screens.

🔍 Example 2:  If I were managing an AI travel assistant, I would prioritize ensuring the AI finds the correct flights based on the user's verbal request. This differs from designing the visual layout of flight options on a screen.

My New Focus: Defining Value and Driving Commercial Success

So, where does our focus need to be now?

1. Identify valuable outcomes. I believe our primary job is shifting to a deep understanding of what outcomes users genuinely need and value. What specific results do they want the AI to deliver for them?

2. Ensure monetization. A crucial part is figuring out how these valuable outcomes can translate into a thriving, sustainable business. PMs must become much more focused on the commercial aspects of our products.

3. Spend more time on go-to-market strategies. Consequently, I anticipate investing significantly more time in how our product is marketed and sold. This includes thinking through:

   • Pricing strategies (How should we charge? Per successful outcome? Via subscription tiers?)

   • Sales channels (Direct online sales? Working with partners? Enterprise sales?)

   • Taking ownership of the product's commercial success.

Questions I now ask myself about monetization:

1. How should we price access to basic versus advanced AI capabilities?

2. Is a pay-per-successful-outcome model viable?

3. How can we best bundle AI-driven features into subscription tiers?

4. Which specific outcomes are so valuable that users would be willing to pay a premium?

Understanding Users is Still Crucial (But in a New Way)

Let me be clear: this doesn't mean I think we stop trying to understand our users! Far from it. But what I focus on understanding is changing:

• Why do users need this outcome? What's the underlying goal or context?

• What happens when the AI fails or misunderstands? Where are the user frustration points?

• Do users trust the AI with their information? What are their security and privacy concerns?

• How do users actually phrase their requests? People use varied language for the same intent.

• When and why might users prefer chat/voice over a traditional screen, or vice-versa?

My user research might now involve analyzing dialogue patterns or understanding user expectations for error recovery in a conversation.

Final Thoughts: How PM Skillsets Will Evolve

The move to AI and conversational interfaces requires us to shift our focus:

👉 From: Primarily focusing on the process (clicks, screens, micro-interactions).

👉 To: Sharply focusing on the result (delivering valuable outcomes) and ensuring commercial success.

What This Means for Product Managers

We need a solid high-level understanding of AI and Natural Language Processing (NLP) capabilities.

AI-powered PMs must define success based on outcomes delivered, not just feature usage. This will involve developing commercially viable strategies centered around these outcomes. Essentially, we become more like the general manager or business owner for our product area.

I strongly believe that the product managers who adapt to this outcome-driven, commercially savvy, conversational paradigm will be the ones who thrive in the years ahead.

Do you agree?

About the Author

Filip Szymanski has over 20 years of experience as a product leader and is passionate about leveraging AI to power a new generation of product managers. Follow my journey at productpath.ai or add me on LinkedIn.

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