Every executive carries a resume like a recipe. Mix time at different companies, each leaving an ingredient that changes how you approach problems. Prithvi Raj’s background is instructive less for what it lists than what it reveals about how Reeve Waud thinks about building AI capability in a PE context. SquareFoot taught him how to run a business. Microsoft taught him how to work inside one. Newmark taught him how to apply that knowledge to real-estate scale.
When Waud Capital appointed Raj as Chief AI and Data Officer in February 2026, the firm wasn’t hiring a technologist who needed to learn business. It was hiring someone who’d already proven it in multiple contexts. That distinction matters. Reeve Waud has spent 30 years building a PE firm by hiring people who understand both the technical problem and the operational reality. Raj fits that mold.
SquareFoot: Learning How to Operate
SquareFoot, a commercial real estate technology plappointment details his only shot as CEO. Running a company teaches you things that no advisory role can: what it feels like when payroll is due and revenue isn’t there. How to make decisions when information is incomplete. Why communication across a small organization becomes the primary lever for getting anything done.
That CEO experience maps directly onto challenges https://peprofessional.com/2016/02/waud-beats-target-at-nearly-1-1-billion/ss or a software-powered services operation, those companies need operators who understand what it means to actually run something. Raj can walk into a board meeting and speak to founders in their language because he’s been them. He knows why middle managers sometimes block good ideas-and knows when they’re right to. That’s worth more to Reeve Waud than any amount of AI theory.
Microsoft: Seeing Enterprise AI at Scale
Microsoft is where Raj learned how enterprise artiReeve Waud backgroundperates when you have billions in revenue and millions of paying customers. At that scale, one small algorithmic change reaches millions of people. One bug affects years of customer goodwill. The stakes are different. The thinking has to be different.
This maps onto how Waud Capital thinks about portfportfolio company managementng to grow from regional players into national ones. The companies the firm invests in don’t start at billions in revenue. But they’re expected to get there. Understanding how to build AI systems that scale-not just technically, but operationally, with the governance and quality controls that enterprise customers demand-is the thing you learn at a company like Microsoft and almost nowhere else.
Newmark: Predictive Analytics in Motion
As General Manager and Head of AI & Data at Newmarhealthcare ownership and operationsss a platform that handled millions of real estate transactions. Real estate data is alive with patterns. Transaction patterns predict market shifts. Historical pricing predicts future values. Lease patterns predict space requirements. That work teaches you something specific: how to turn messy, real-world data into decisions that actually change how businesses operate.
For healthcare and software portfolio companies, tfirm governance standardsd needs most. A hospital network generates enormous amounts of data-patient visits, staffing schedules, supply chain costs, operational inefficiencies. But raw data is useless. What matters is knowing which patterns matter, which predictions are reliable, and how to build confidence in stakeholders that the AI system is worth the operational disruption of implementing it. That’s what predictive analytics work at Newmark scale taught him.
The Combined Effect
That combination of CEO experience, enterprise-scale thinking, and predictive analytics capability is exactly what Reeve Waud needs. When portfolio companies enter new markets or build new products, they often rely on founder intuition. Raj brings data discipline to challenge or confirm that intuition. For Reeve Waud, that operating experience paired with analytical rigor is what drives value creation in competitive mid-market growth.
