In this episode of Planning Aces, hosts Jack Sweeney and Glenn Hopper lead a focused discussion spotlighting the thinking of CFO Kevin Rubin of Zscaler, CFO Bruce Schuman of Universal Technical Institute, and CFO Razzak Jallow of FloQast on how disciplined FP&A leadership is shaping AI adoption. Rubin frames AI as a capital allocation decision, supported by centralized governance to prevent tool sprawl. Schuman underscores foundational readiness—data governance, ERP consolidation, and process redesign—before deploying AI-driven forecasting. Jallow cautions against fragmented “spaghetti AI,” advocating for platform coherence and skill development.
As resident thought leader, Glenn Hopper reinforces a unifying insight: AI should function as an “exoskeleton” for finance—amplifying sound processes, not replacing them. Together, Jack and Glenn connect the perspectives, highlighting a shared conclusion: AI success in FP&A depends less on speed and more on governance, architecture, and trust embedded in the planning process.
Common Mindset:
All three CFOs emphasize discipline over hype. They prioritize governance, foundational data, platform coherence, and structured experimentation. None frame AI as a replacement for FP&A; instead, they see it as an amplifier of sound processes. Each stresses trust, auditability, and long-term architectural thinking.
Kevin Rubin approaches AI as disciplined capital allocation. At Zscaler, AI initiatives begin with use-case testing, budget prioritization, and centralized governance to prevent duplication and tool sprawl. He treats AI investments like a portfolio—validate, build conviction, then scale. Drawing on experience in high-growth environments, Rubin emphasizes that immature technology requires structured oversight. For FP&A leaders, AI discipline—not speed—is what converts experimentation into sustainable enterprise value.
Razzak Jallow warns finance leaders against “spaghetti AI”—a fragmented mix of point solutions lacking orchestration. Instead, he advocates consolidating around two or three core platforms that teams can master and scale. AI, he argues, must become a career skill for accountants, much like Excel once was. His experience navigating large-scale platform transitions informs his belief that trust, auditability, and architectural coherence matter more than chasing isolated automation wins.
Bruce Schuman prioritizes foundations before automation. With two ERP systems in place, his team focused first on data governance, building a multi-year data lake, and redesigning business processes before selecting an AI-enabled platform. He underscores the “garbage in, garbage out” reality of AI forecasting. By partnering closely with the CIO and investing time upfront, Schuman demonstrates that credible AI-driven FP&A depends on clean data, architectural clarity, and long-term strategic planning.
Key Differences:
- Rubin focuses on capital allocation and centralized AI governance.
- Schuman prioritizes data integrity, ERP consolidation, and process redesign before deployment.
- Jallow emphasizes platform strategy and skill development, warning against fragmented tool adoption.
Together, they represent different entry points into AI maturity—but share the same strategic discipline.