Episode
AI Strategy Finance Leaders Must Use to Avoid Siloed Tools and Failed Adoption with Vikram Bhandari
- Podcast
- FP&A Unlocked
- Published
- Mar 26, 2026
- Duration seconds
- 2994
- Processing state
not_requested- Canonical source
- https://fpa-unlocked.captivate.fm
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Summary
In this episode of FP&A Unlocked, Paul Barnhurst and Glenn Snyder are joined by Vikram Bhandari to explore how organizations should approach AI in finance and FP&A. They discuss why most companies fail with AI adoption, the importance of enterprise-wide strategy over siloed tools, and how finance teams can evolve from reporting to forward-looking decision-making. Vikram shares practical insights on AI readiness, forecasting, and how leaders can leverage AI to drive better business outcomes. Vikram is the Chief Technology & Innovation Officer at Riveron, where he leads AI, digital transformation, and finance modernization initiatives. With nearly 25 years of experience, Vikram works closely with CFOs to transform finance functions through technology. He previously served as the President and CEO of Yantra, a company he founded and led for over 15 years before its acquisition by Riveron. Expect to Learn: Why most AI implementations fail and how to avoid common mistakes The importance of enterprise AI strategy vs. siloed tool adoption How FP&A is evolving into a forward-looking, decision-making function What it takes to prepare your data, processes, and governance for AI Here are a few relevant quotes from the episode: The real question is not which AI tool is best, it’s whether your finance architecture is ready to absorb AI.” - Vikram Bhandari If you treat AI as a feature, you get incremental gains. If you treat it as a strategy, you get competitive advantage.” - Vikram Bhandari Vikram emphasizes that AI should be treated as a capability multiplier rather than a shortcut. He highlights the importance of strong data foundations, governance, and enterprise strategy to unlock real value. The episode reinforces that the future of FP&A lies in combining m…