Despite all their pitches promising something new, AI startups share many of the same questions as startups in years past: How do they know when they’ve achieved the holy grail of product-market fit?
Product-market fit has been studied extensively over the years. Entire books have been written on how to master the art. But as with so many things, AI is disrupting established practices.
“Honestly, it just couldn’t be more different from all the playbooks we’ve all been taught technology in the past.” Anne Bordetskypartner in New Enterprise Associateshe told a standing-room-only crowd at TechCrunch Disrupt in San Francisco. “It’s a whole different ball game.”
At the top of the list is the rate of change in the world of artificial intelligence. “Technology itself is not static,” he said.
Even now, there are ways founders and operators can assess product-market fit.
One of the best things to watch, Murali Joshipartner in Iconiqhe told the audience, it’s “cost-effectiveness.” AI is still early on the adoption curve for many companies, so much of their spending is focused on experimentation rather than integration.
“Increasingly, we’re seeing people really move away from experimental AI budgets to core office CXO budgets,” Joshi said. “Research into this is extremely critical to ensure that this is a tool, a solution, a platform that’s here to stay, versus something they’re just trying and testing.”
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Joshi also suggested that startups consider the classic metrics: daily, weekly and monthly active users. “How often do your customers engage with the tool and product they’re paying for?”
Bordetsky agreed, adding that qualitative data can help nuance some of the quantitative metrics that may suggest, but not confirm, whether customers are likely to stick with a product.
“If you talk to customers or users, even in qualitative interviews, which we tend to do very early on, that’s very clear,” he said.
Interviewing people in the executive suite can also be helpful, Joshi said. “Where does this sit in the technology stack?” suggests asking them. He said startups should think about how they can become “stickier as a product in terms of core workflows.”
Finally, it’s important for AI startups to think about product-to-market fit as a continuum, Bordetsky said. Product-market fit is not a point-in-time kind of thing,” he said. “You learn to think about how you can start with a little bit of customization in the product market in your space, but then really build it up over time.”
