Parker Conrad wants you to believe that a significant portion of data analytics belongs inside human capital management systems. This claim strategically positions Rippling—originally an HR software company—to compete directly with dedicated business intelligence tools. By 2026, as companies increasingly seek to consolidate their tech stacks amid rising costs, this argument gains even more traction.
The pitch is that the modern data stack—the galaxy of tools companies currently cobble together from multiple vendors—can be collapsed into one. Currently, moving data from various business systems into a warehouse is a massive industry itself, handled by companies like Fivetran and Airbyte. Then you need storage and query capabilities (e.g., Snowflake), transformation and cleaning tools (e.g., dbt Labs), and a visualization layer (e.g., Tableau). Rippling aims to eliminate this complexity.
The Rippling Data Cloud: A Unified Approach
Conrad argues that Rippling knits all of these components into a single system, wrapped in something the others lack: a built-in understanding of your organization, its ever-evolving reporting structure, and everything impacted when any metric shifts. That is what Rippling Data Cloud, launching today, is designed to deliver.
To demonstrate, Conrad shares his screen from his San Francisco office, offering 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 quickly adds, but the ROI simply wasn't there. This is the kind of discovery most companies currently have no way of surfacing without manual effort. By 2026, as AI adoption surges, such insights become critical for cost control.
Real-Time Insights Across the Organization
He then shows a live dashboard built by simply asking Rippling AI to analyze the company's most recent compensation review cycle—distributions of performance ratings, promotion rates by department, salary ratios—all drillable to the individual level. Another dashboard cross-references support ticket volume from Salesforce with employee scheduling data, showing at a glance which teams are overwhelmed. The enrollments team, he notes, is severely understaffed; the travel team has more than double the unresolved tickets of the platform team.
AI Token Spend: The Executive's Preoccupation
But the example Conrad seems most excited about addresses a top concern for executives in 2026: AI token spend. He shows a dashboard combining data from Anthropic's usage logs, GitHub pull request data, and Rippling's performance ratings to identify which engineers are truly getting value from AI tools and which are burning money with little to show for it.
"The high performers spend the most, which you would sort of expect," Conrad says. However, the dashboard also flags engineers with high spend and high peer rejection rates on code reviews—those whose colleagues frequently ask them to redo work. "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 adds.
The analysis already prompted Rippling to cut spending limits for certain employees. The product can also be configured to alert managers—or automatically shut off access—when employees exceed a spending threshold.
On the question of impact to Rippling's own margins when customers exceed token allotments, Conrad doesn't get specific, but the implication is clear: smarter tracking benefits both parties.
via TechCrunch