A Product Leader's Perspective: Building an AI-Proof Product Career

The room filled up early. Laptops closed, sleeves rolled, the kind of audience that prefers a whiteboard and a working prototype over a slide deck. Recently, a few Toast product teams met with other product industry leaders in Bangalore for a conversation on “Building an AI‑Proof Product Career”, and what followed wasn’t a panel discussion so much as a working session in public. Across the product world, we tried to put language to what many PMs are feeling: the job is changing faster than the rituals around it, and the gap between the two is where careers are made, or left behind.

“AI‑proof” is a provocative phrase. It suggests a shield you can wear. I don’t think that shield exists. What does exist is a path to becoming more valuable because of AI. That path is uncomfortable. It asks PMs to stop treating AI like a garnish on their feature roadmaps and start treating it as the fabric of how we build, test, and learn. It asks managers to automate their own management tasks first. It asks leaders to trade the comfort of status updates for the messiness of shipping unfinished things to real customers. If that sounds like a different job than the one you signed up for, that’s because it is.

Smaller Teams, Bigger Judgment

Over the last year, technical teams across the industry have watched organisational structures flatten and role boundaries blur. A tight unit comprising one PM with two or three engineers can now test more hypotheses in a week than a larger team could in a month. The reason isn’t just better tools, it’s that AI removes a lot of the friction that used to exist between an idea and a testable artefact. Coordination, once a full‑time job, is increasingly not required because of shared context in smaller teams.

That shift has a hard edge. If your role is defined mostly by orchestration, scheduling alignment, writing status docs, driving consensus through meetings, then AI will compete with you directly. In a three‑to‑five‑year window, we won’t need as many execution or coordination‑only PM roles. We will still need PMs, but fewer of them, and they’ll be the ones whose judgment travels: people who can frame problems clearly, make fast tradeoffs, and put working software in customers’ hands, so reality can argue back.

From Writing Culture to Building Culture

We are continuously striving for AI proficiency within our Fintech product teams. Not because it’s trendy, but because it changes the speed and quality of the work. Specifically, we are pushing our Fintech PMs and engineers to prototype first. Show the thing. Let a real user touch it. Then write down what you learned and what you’ll try next. With AI, when anyone can generate a beautifully formatted and structured PRD, format stops being a proxy for clarity. Deep thinking and judgement - what you decided and why, has to stand on its own.

This is where the term “Vibe Coding” earns its keep at Toast. Vibe coding is the scrappy, AI‑assisted way of pulling together a functional prototype from a handful of prompts, APIs, and glue code. It’s not production engineering for sure. It’s product discovery at the speed of curiosity. When PMs can vibe‑code, the calendar changes. Debates that would have consumed a month turn into a two‑day experiment with a metric attached.

AI Levels the Tools. Your Edge Must Be Human.

A common worry is that AI will make PMs interchangeable. It will certainly level the playing field on tasks like first‑draft research, insight generation, or meeting notes. But the more AI standardises the inputs, the more advantage accrues to PMs who are differentiated on the outputs: thinking, taste, and trust. Deep Thinking is the capacity to cut to the essence of a problem. Taste is your instinct for what “good” looks like in your domain. Trust is the credibility that makes teams follow you into the unknown.

In my day-to-day interactions with Toasters, I personally like to describe product management as a combination of  Art + Science. AI is absorbing a lot of the science part, i.e. execution, coordination. What rises in value is the ‘Art’ part: judgment under uncertainty, the ability to tell a story that aligns a team around outcomes. Use AI as a copilot, not a crutch. Since everyone has access to roughly the same models, your durable edge is your deep thinking, better judgment, the quality of the questions you ask and the consequences you’re willing to own.

Technical Depth Isn’t Optional Anymore

You don’t need to become an ML researcher to lead in an AI‑native organization, but you do need to be technically fluent. This is rapidly becoming table stakes across the industry. That means understanding how model choices affect latency, cost, privacy, and reliability; recognizing when retrieval beats fine‑tuning; knowing what safety and policy guardrails are necessary; and, crucially, being able to explain those tradeoffs in business terms.

Why does this matter? Because “AI” is not a moat. Your competitors can access many of the same models you can. The moat is your data and orchestration, how you collect, structure, and responsibly use proprietary signals; how you compose tools and policies into agent behaviours; how you design for failure and recovery. If you can’t evaluate those choices, you can’t responsibly own the product decision.

AI‑Native > AI‑Bolted‑On

Early in my journey, I made the same mistake a lot of teams make: I tried to AI‑fy existing features. It barely worked. The inflection came when we flipped the question from “Where can we sprinkle AI?” to “How would we build this if AI was available on Day 1?” That reframing and first principles thinking change everything.

When you start with capabilities, you also stop thinking in features and start thinking in outcomes. A payment dispute isn’t a feature request; it’s a messy, multi‑step, policy‑constrained job to be done. An agent that can see the data, reason about the rules, and guide a human through the exception path can be far more humane than another modal dialog with eight fields.

The Time AI Gives Back Belongs to Your Customers

If AI automates half your status work and most of your boilerplate docs, what will you do with the hours it returns? Here’s an anecdote that changed how we think about that question at Toast within the Fintech teams. A PM on our team used AI to generate weekly notes, pre‑groom the backlog, and first‑draft discovery briefs. They got back a meaningful slice of the week and chose to spend it talking to more customers, analyse care ticket data further, and building a few more hypotheses. The insights that came out shifted our priorities from “make it faster” to “make it certain.” We are now working on clearer agent guidance for edge cases, safer defaults. The impact on operator confidence beat any naive speed improvement we could have shipped.

That’s the pattern I personally want to normalise for all PMs across the industry, and especially within the Fintech teams at Toast:  let AI empty your calendar of low‑leverage work, then spend that time on deeper customer research. The questions that matter most rarely live in your backlog; they live in the hour you haven’t watched yet.

Automate Yourself Before AI Does

If you’re standing at that career threshold, here’s the uncomfortable truth: output and leverage both matter, and AI multiplies both for the people who embrace it. Outwork others in the short term, but do it by aiming to automate most of your workload and reinvesting the time in higher‑order work; customer engagement, systems thinking, talent development. Don’t make your brand “great at alignment” or “writes perfect docs.” Instead, make it “ships working bets that change the numbers,” “raises the technical and ethical bar,” and “builds teams that move faster with more safety.”

Five Years Out: Capabilities, Agents, and Data Moats

Look five years down the road and the contours are already visible. We’ll think less in features and more in capabilities; portable, policy‑aware building blocks that can be orchestrated by agents. Many customers, especially in SaaS, will assemble lightweight tools themselves with AI. Your advantage won’t be the feature list; it will be the quality of your data, the strength of your safety layer, the grace of your orchestration, and the judgment to combine them well. PMs in that world look less like traffic cops and more like conductors; setting tempo, choosing the score, knowing when to let the soloist (the agent) lead and when to bring the section (the UI) back in.

If this sounds like AI will replace PMs, I’d offer a sharper framing: AI won’t replace PMs. but PMs who embrace AI will replace those who don’t. Not by being louder in meetings, but by learning faster than the problem is changing.

Closing the Loop

The Bangalore community showed up with urgency and generosity, and I’m grateful for it. Thank you to The Product Folks for co‑creating the space, and to my fellow panelists for pushing the conversation past frameworks into mindset, leadership, and trust. We are adding a few photos alongside this post, but the real artefact is cultural: a shift from talking about the future of the product to building it in public.

If you’re reading this as a PM wondering where to start, start where the friction is highest. Replace a doc‑first ritual with a prototype‑first conversation. Let an agent take your status meeting. Use the time to sit with a customer during the hour when everything usually goes wrong. Make one decision this week that only a human with good judgment could make, then make sure you have the data to learn from it. That’s how you “AI‑proof” a career, by becoming the person who is more valuable because of AI.

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