Are engineering leaders asking the wrong questions when deciding what to build?

In this episode of Futureproof, Xano CEO Prakash Chandran talks with Chris Horn, SVP of Operations at Deriv, about what it takes to build AI inside a regulated, global software environment. Chris explains the difference between prototypes and proofs-of-concept, why data architecture is the real unlock, and how Deriv used a Shark-Tank-like model to introduce AI into internal operations. Together, they explore the mindset shift required for AI-native development — and why the most important question isn’t “Can we build it?” but “Should we build it?”

Topics covered include:

• Prototype vs. PoC: Why technical feasibility matters less than solving a real problem.
• AI as product work: The critical role of discovery, KPIs, and iteration in AI projects.
• Data as the foundation: How Deriv built a medallion architecture to get ready for AI.
• Internal AI first: Why customer-facing AI wasn’t the starting point (and what worked instead).
• Upskilling at scale: Building an AI-native culture through curiosity, training, and incentives.

Episode ID: 18501541-from-proof-of-concept-to-product-chris-horn-deriv

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