How should AI startups think about product-market fit?

For all their pitches and promises of something new, AI startups share many of the same questions that startups have asked 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 about how to master this art. But as with many things, AI is upending established practices.

“Honestly, it couldn’t be more different from all the rules of the game we’ve all learned in technology in the past.” Anne Burdetskypartner in Partners of the new enterprisehe told a standing room-only crowd Disable TechCrunch In San Francisco. “It’s a whole different ball game.”

At the top of the list is the pace of change in the world of artificial intelligence. “Technology itself is not static,” she said.

However, there are ways founders and operators can evaluate whether their product is a good fit for the market.

One of the most beautiful things worth seeing, Murali Joshipartner in ICONIC“Strong spending,” he told the audience. AI is still early in the adoption curve in many companies, and much of their spending is focused on experimentation rather than integration.

“Increasingly, we’re seeing people really shifting away from just experimental AI budgets to the core office of CXO budgets,” Joshi said. “Researching that is critical to making sure that this is a tool, solution and platform that is here to stay, versus something that they’re just testing and experimenting with.”

Joshi also suggested startups consider classic metrics: daily, weekly and monthly active users. “How engaged are your customers with the tool and product they are paying for?”

Burditsky agreed, adding that qualitative data can help provide nuance to some quantitative metrics that might 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 a lot early on, it comes through very clearly,” she said.

Interviewing people in the executive suite can also be helpful, Joshi said. “Where does this fall in the technology stack?” He suggests asking them. Startups should think about how to make themselves “more consistent as a product in terms of core workflow,” he said.

Finally, it’s important for AI startups to think about product-market fit as a continuum, Burditsky says. “Product-market fit doesn’t happen all at once. It’s learning to think about how you can start with a little bit of product-market fit in your space, but then enhance that over time,” she said.