Parker Conrad knows which employees are worth their AI spend and says Rippling can help you, too

Published 25 June 2026 · tech

Parker Conrad wants you to believe that a huge chunk of data analytics belongs inside human capital management systems — a claim that conveniently positions

Parker Conrad wants you to believe that a huge chunk of data analytics belongs inside human capital management systems — a claim that conveniently positions Rippling, which started out as an HR software company, to compete directly with dedicated business intelligence tools. The pitch is that the modern data stack — the galaxy of tools that companies currently jury-rig from multiple vendors — can be collapsed into one. Just moving data from your various business systems into a warehouse is itself a massive industry; that’s what companies like Fivetran and Airbyte do. Then you need somewhere to store and query it, like Snowflake, then something to transform and clean it, like dbt Labs, then a visualization layer like Tableau on top. Conrad’s argument is that Rippling knits together all of that into one system and wraps it in something the others lack: a built-in understanding of your org, its ever-evolving reporting structure, and everything impacted when any metric moves up or down. That’s what Rippling Data Cloud, launching today, is designed to deliver. To see it in action, Conrad shares his screen from his San Francisco office, and then offers a window into what Rippling found when it turned the product on its own workforce. “There were employees doing things like, ‘Claude is so helpful for me — it analyzes my calendar and my email and puts together a plan for me,’” he says. “That person was spending at a run rate of $30,000 a year for this.” No one was doing anything wrong, he’s quick to add, but the ROI simply wasn’t there.

It’s the kind of finding that most companies currently have no way of surfacing. He then shows me a live dashboard he’s built by simply asking Rippling AI to analyze his company’s most recent compensation review cycle — distributions of performance ratings, promotion rates by department, salary ratios, all of it drillable to the individual level. Then he pulls up another, this one cross-referencing support ticket volume from Salesforce with employee scheduling data — enough to show, at a glance, which teams are drowning and which aren’t. The enrollments team, he notes, is severely understaffed. The travel team has more than double the unresolved tickets of the platform team. But the example Conrad seems most excited about is one closer to a preoccupation many executives share right now: AI token spend. He shows a dashboard combining data from Anthropic’s usage logs, GitHub pull request data, and Rippling’s own performance ratings to peer at which engineers are actually getting value from their AI tools and which are burning money without much to show for it. “The high performers spend the most, which you would sort of expect,” Conrad says. But the dashboard also flags engineers with high spend and high peer rejection rates on code reviews — these are people whose colleagues are frequently asking them to redo something. “If your peers are telling you to go back and do this over all the time, maybe you’re just generating a lot of slop,” he says. The analysis has already prompted Rippling to cut spending limits for certain employees.