Despite marketing themselves as promising something new, AI startups still have many of the same questions as startups of the past. The question is, how do you know if you’ve achieved the holy grail of product-market fit?
Product-market fit has been extensively studied for many years. Entire books have been written about how to master this technique. But like many other things, AI is upending established conventions.
“Honestly, this couldn’t be more different from every playbook we’ve all been taught in technology in the past,” Ann Bordetsky, a partner at New Enterprise Associates, told a standing-room-only audience at TechCrunch Disrupt in San Francisco. “It’s a whole different ball game.”
Top of the list is the pace of change in the world of AI. “Technology itself is not static,” she said.
Still, there are ways founders and executives can assess the fit between their product and market.
Murali Joshi, a partner at Iconiq, told the audience that one of the most noteworthy aspects is “sustainability of spending.” AI is still in the early stages of adoption for many companies, and much of their spending is focused on experimentation rather than integration.
“More and more, we’re seeing people really shift from just experimental AI budgets to core areas of CXO budgets,” says Joshi. “Digging into this is really important to make sure this is a tool, solution, platform that’s here to stay and is just something that we’re testing and trying out.”
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Joshi also suggested startups consider the classic metrics of daily, weekly, and monthly active users. “How often do your customers use the tools and products they are paying for?”
Bordetsky agreed, adding that qualitative data can help nuance some of the quantitative metrics that may suggest, but not confirm, whether a customer is likely to stick with a product.
“When we talk to customers and users, even qualitative interviews, which we tend to do early and often, come across very clearly,” she said.
Interviewing people in the C-suite may also be helpful, Joshi said. “Where does this fit in the technology stack?” he suggests asking them. He said startups need to think about how they can become more “sticky as a product in terms of core workflows.”
Finally, Bordetsky said it’s important for AI startups to think of product-market fit as a continuum. “Product-market fit isn’t something that happens at one point in time. I’m learning to think about how to make a little bit more product-market fit in my field and really strengthen it over time,” she said.
