A New Era for Startup Strategy
For years, the concept of product-market fit has been the cornerstone of startup success. Countless founders have built their strategies around identifying and refining it, with entire books dedicated to the topic. Yet, as artificial intelligence continues to reshape industries, the traditional frameworks for achieving product-market fit are being rewritten.
At TechCrunch Disrupt in San Francisco, Ann Bordetsky, Partner at New Enterprise Associates, captured this shift perfectly:
“Honestly, it just could not be more different from all the playbooks that we’ve all been taught in tech in the past. It’s a completely different ball game.”
Unlike traditional technology startups, AI companies operate in a space where the pace of innovation is relentless. As Bordetsky noted, “The technology itself isn’t static.” This constant evolution makes identifying a stable product-market fit particularly challenging — and also uniquely exciting.
The Challenge: Moving Beyond Experimentation
According to Murali Joshi, Partner at Iconiq, one of the clearest signals of product-market fit lies in what he calls the “durability of spend.” In the early stages of AI adoption, many organizations allocate funds primarily for testing or pilot programs rather than long-term use.
Joshi explained:
“Increasingly, we’re seeing people really shift away from just experimental AI budgets to core office of the CXO budgets. Digging into that is super critical to ensure that this is a tool, a solution, a platform that’s here to stay, versus something that they’re just testing and trying out.”
In other words, when AI tools move from the innovation lab to the executive budget — when they become essential rather than optional — that’s when true product-market fit begins to emerge.
Tracking Real Engagement
Metrics still matter. Joshi emphasized that traditional indicators such as daily, weekly, and monthly active users remain powerful measures of engagement.
“How frequently are your customers engaging with the tool and the product that they’re paying for?” he asked.
However, Bordetsky added that numbers alone rarely tell the whole story. Qualitative insights — especially from direct customer interactions — can reveal nuances that data might overlook.
“If you talk to customers or users, even in qualitative interviews, which we do tend to do a lot early on, that comes through very clearly,” she said.
Integrating AI Into Core Workflows
Joshi also recommended that AI founders focus on understanding where their product sits within an organization’s tech stack. By asking executive teams questions like “Where does this sit in the tech stack?” startups can better assess how integral their product truly is.
He advised founders to think about making their offerings “more sticky as a product in terms of the core workflows.” The more embedded a solution becomes within day-to-day operations, the more indispensable — and resilient — it becomes.
Product-Market Fit as a Continuous Process
Perhaps the most crucial insight came from Bordetsky’s reminder that product-market fit isn’t a one-time milestone.
“Product-market fit is not sort of one point in time,” she said. “It’s learning to think about how you maybe start with a little bit of product market fit in your space, but then really strengthen that over time.”
For AI startups, that means viewing product-market fit as an ongoing relationship between technology, users, and evolving market needs. As AI tools continue to advance, staying adaptable and maintaining alignment with user value will define long-term success.
The Takeaway
In an industry defined by constant transformation, the quest for product-market fit has never been more dynamic. AI founders who blend data-driven metrics with qualitative insights, focus on durable customer engagement, and adapt as the technology evolves are the ones most likely to thrive.
AI may be changing the rules — but for the startups willing to learn them, it’s also expanding the playing field.
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