A lot of people in a lot of places have told you that data is gold in AI over the last few years, and I’ve been among them.
We’ve all been wrong.
Well, not wrong exactly. You do need clean data to get good output from AI, and having a lot of it usually beats having a little. It’s just that calling data AI gold distracts attention from what makes AI monetizable for frontier labs, software makers, and MSPs alike. Businesses have plenty of data and don’t want more, observes Mike Sanders (pictured), a former C-suite executive at Kaseya who’s now CEO of Upshop. “What they want is for that data to provide something that can be executed at whatever level it needs to be executed to offset a bad outcome.”
Or produce a good one. Upshop’s AI-powered software for food retailers does both, according to Sanders, by turning data into intelligence and intelligence into action. Say you’re a grocer and the deli counter’s bread delivery came in lighter than expected.
“If I’m only going to make a certain amount of sandwiches, which sandwiches should I make?” Sanders asks. “What’s the most likely thing to sell? What gives us the best profitability? Or what’s going to allow us to get the most coverage so we disappoint the fewest amount of guests?”
Or say instead that a bunch of milk in the dairy aisle will reach its expiration date in two days. Data tells you how many cartons are involved and what they’re worth to you. Intelligence tells you how much milk you would normally sell in two days and exactly how much you should lower your price by to sell everything at risk of expiring instead. An intelligent system of action, meanwhile, arms managers to put that price into effect automatically.
“You’re not just cutting the price in half or giving [milk] away for free,” Sanders says. “You’re making the best possible decision that you can to sell as much of it as you can while maintaining as much margin as you possibly can.”
Outcomes like that are more exciting to grocers than data, or AI for that matter. “They’re able to drive quite a bit more growth because they have the right products at the right time on the shelf,” Sanders says.
Software vendors and MSPs can learn a few things from Upshop. The company offers an industry-specific SaaS solution, for example, at a time when vertical specificity is one of the best forms of SaaSpocalypse insurance available, and it has more than just large volumes of data. It has large volumes of targeted, contextual data with a lot of retail-specific scaffolding around it.
“We’re allowing that larger data set to drive smarter decisions and smarter outcomes as a result,” Sanders says.
Which points to the biggest moral of Upshop’s story, especially for managed service providers remaking themselves as managed outcome providers: AI client conversations should more or less always center on increased sales, decreased waste, stronger margins, or something else tied to making more or spending less rather than AI itself.
Data makes AI work. Outcomes are what businesses buy.