Ravi Mehta on shifting from craft to judgment in the age of AI
When Ravi Mehta talks about AI, he doesn’t start with tools. He starts with strategy. A longtime product executive at TripAdvisor, Facebook, and Tinder, and now a product advisor and co-creator of Reforge’s AI Strategy Program, Ravi helps companies compete and win in a rapidly changing environment.
“A lot of people are asking: how do we stand out?” he says. “How do we make sure our use cases don’t get subsumed into these really broad, horizontal products like ChatGPT or Claude?”
For Ravi, that question sits at the heart of the AI transition. It’s not just about how teams use AI, but about how they rethink their product strategy, their pace, and their craft in an era where execution is easier and differentiation is harder.
The goal isn’t to react to every new model. It’s to understand where you’re vulnerable and build an advantage where AI can’t easily follow.”- Ravi Mehta, Product Advisor
When Ravi and Brian Balfour created the Reforge AI Strategy Program, their goal was to help product leaders move beyond adoption checklists and into competitive thinking. “There were plenty of courses showing people how to use AI in their workflows,” he says. “But not many helping them understand what it means for their business model.”
The program begins with a simple question: How vulnerable are you to AI-based disruption?
Some companies, like Stack Overflow or Chegg, have already seen their traffic drop because their core use cases now live inside large AI models. Others, like Airbnb, are taking a slower, more deliberate approach. The difference, Ravi says, is strategy.
“Every company needs to assess its risk,” he says. “If you’re close to a use case that AI can easily replace, you have to move faster. If you’re building around something deeply human with community, trust, and creativity, you can afford to go slower and design more intentionally.”
In practice, Ravi advises teams to start by mapping their products across three dimensions:
- Automation risk: How much of your core experience can AI already replicate? (For example, Q&A, summarization, or tutoring, as we’ve seen with Stack Overflow and Chegg.)
- Differentiation: What makes your product uniquely valuable? If it’s built on data, relationships, or network effects, you have a stronger foundation than products competing on features alone.
- Customer dependency: How embedded are you in your users’ workflows? Products that are peripheral to daily work are easier for AI to replace; those tied to identity or habit are harder to dislodge.
Teams that score high on automation risk but low on differentiation should pivot quickly, either by integrating AI themselves or by moving up the value chain. Companies built on human connection can afford a more deliberate approach, focusing on trust, design, and experience as long-term differentiators.
“The goal isn’t to react to every new model,” Ravi says. “It’s to understand where you’re vulnerable and build an advantage where AI can’t easily follow.”
2. How leaders raise AI fluency
When Ravi advises product teams on building AI fluency, his first recommendation is practical: Remove friction.
“Step one is access,” he says. “Give everyone access to your preferred LLM: ChatGPT, Claude, whatever. And encourage people to actually use it. Writing specs, summarizing research, prototyping ideas. Learning happens by doing.”
The second step is expectation. Leaders should make AI use part of the workflow, not a side project. Ask in meetings: “How could we do this faster with AI?” That simple question sets the tone that this is normal, not optional.
The third step is to challenge. Push teams to think creatively about acceleration. “Not everything should move 10x faster,” he adds. “But some things can: prototyping, documentation, research synthesis. When teams feel empowered to experiment, they find those AI leverage points.”Within organizations, he sees three groups emerge: the evangelists (already deep in), the interested majority (curious but unsure where to start), and the skeptics. His advice for leaders: use the first group to model behavior, support the second with hands-on education, and engage the third with empathy.
Not everything should move 10x faster. But some things can: prototyping, documentation, research synthesis. When teams feel empowered to experiment, they find those AI leverage points.”- Ravi Mehta, Product Advisor
3. Product teams are becoming jazz bands
One of the biggest shifts Ravi sees is how AI blurs boundaries between roles. A PM can now prototype. A designer can code. An engineer can run research. “AI is turning product teams into jazz bands where everyone can play more instruments,”he says. “That’s exciting, but it’s also noisy without a rhythm.”
That overlap isn’t chaos; it’s evolution. Product teams used to move like an assembly line: discovery, spec, design, build, test. AI compresses those steps. A prototype that once took weeks now takes hours. That means you can learn faster, but only if you collaborate differently.
Still, he cautions against mistaking speed for strategy. AI-generated prototypes tend toward the average. “AI can do pretty good work, but it’s not going to create something truly new or evocative,” he says. “It’s average intelligence.” That’s where humans come in. “The goal isn’t to move faster toward the average. It’s to move faster toward something exceptional.”
4. What moves at AI speed, and what stays human
Ravi believes the next great skill in product management is knowing the difference between what moves at AI speed and what moves at human speed.
Writing specs, analyzing data, creating prototypes. Those can all move faster now. But understanding why something matters, and what to build next, will always move at the pace of human judgment.
“For PMs who want to set themselves apart, focus on the areas that still move at human speed,” Ravi says. “Customer discovery, product sense, and strategy: Those are only getting more important.”
He sees the emphasis in product work shifting dramatically. “It used to be fifty-fifty,” he says. “Half the challenge was building well, half was deciding what to build. Now it’s more like ninety-ten. The building gets easier. The real differentiator is judgment.”
For PMs who want to set themselves apart, focus on the areas that still move at human speed. Customer discovery, product sense, and strategy: Those are only getting more important.”- Ravi Mehta, Product Advisor
5. Taste replaces craft as the differentiator
Ravi talks often about the rising value of “taste,” a word that has suddenly become part of every product conversation. “It used to be that to create something, you needed two things: taste and craft,” he says. “Taste is knowing what you want to make. Craft is the skill to make it.”
In creative fields, AI is rapidly democratizing craft. Anyone can produce something that looks polished. What’s scarce now is knowing what to create and why it matters.
“If craft becomes accessible to everyone, the advantage shifts to those with better judgment,” Ravi says. “In product, that means taste. You need to understand what’s missing, what feels right, and what your users will actually love.”
This shift mirrors what’s happening across creative work. As tools lower the barrier to execution, the real differentiator moves upstream to vision, curation, and intuition.
6. What doesn’t change
Despite all the disruption, Ravi is quick to remind teams that the fundamentals of great product management remain intact. “Customer insight, product strategy, and influencing people doesn’t go away,” he says.
Execution evolves: AI can now draft specs, QA tests, even functional code. But insight and strategy only deepen in importance. Customer discovery gets faster, but interpreting the data, connecting it to emotion, and deciding what to do next is all still human work.
Great PMs, he argues, will be defined less by how fast they build, and more by the clarity of their thinking. You’ll always need someone who can see around corners and knows when the right move is to slow down.
AI can generate strategy documents, but it can’t feel the market shift under your feet. It can’t see the pattern that isn’t in the training data yet.”- Ravi Mehta, Product Advisor
7. A faster loop for product discovery
In his advisory work, Ravi has seen firsthand how AI accelerates one of the hardest parts of product building: the search for product-market fit.
“Product discovery used to move at the speed of engineering,” he says. “Even the fastest teams could only get a new concept in front of customers every few weeks.”
Now, he’s working with companies that prototype new ideas every day. Product teams use “vibe-coding” tools to build small tests, put them in front of real users, gather feedback, and iterate.
“They go to a coworking space, show a prototype, get reactions, and come back the next day with improvements,” he says. The loop compresses from weeks to days, and the learning compounds.
The goal isn’t just to move fast; it’s to find conviction faster. “If you’re not sure whether you have product-market fit,” he says, “you probably don’t. AI helps you get to that answer sooner, one way or another.”
8. The enduring craft of product leadership
As Ravi sees it, AI isn’t replacing the craft of product management, it’s reshaping it.
“AI will handle more of the doing,” he says. “But strategy, empathy, and taste are going to matter even more.”
The challenge for product leaders is to elevate the conversation inside their teams. Less “What can AI do for us?” and more “What can we uniquely do because of AI?”
In that question lies the future of product work: not faster assembly lines, but smarter orchestras.
Keep learning
Ravi Mehta’s framework for Access + Expectation + Challenge appears alongside those of Kene Anoliefo, Laura Burkhauser, Aakash Gupta, and Elena Verna in Atlassian’s new ebook, The New Language of Work: Raising AI Fluency. Read the ebook →
And you can listen to his full interview with Atlassian here.