The voice of AI, Data and Innovation
About the Guest
Prashant Motewar
Prashant Motewar is Head of Business Technology and Data/AI Enablement at CoreWeave, where he leads strategy and execution across enterprise business systems with a focus on measurable business outcomes.
He is a technology executive, CIO leader, author, speaker, and board advisor with deep experience across enterprise architecture, AI/ML, automation, data strategy, supply chain, and digital transformation.
Before CoreWeave, Prashant spent more than 12 years at Equinix, including leading Enterprise IT, Data Science, and Analytics, and earlier held product, architecture, engineering, and supply-chain roles at Oracle.
Brief About the Episode
Enterprise AI adoption is no longer the hardest problem. Getting AI into production, scaling it across the business, and governing it without slowing innovation is.
In this conversation, Jas Kaur, CTO at Teqfocus, speaks with Prashant Motewar of CoreWeave explains why a small, centralized AI team cannot scale enterprise adoption on its own. His approach centers on democratizing AI tools and enterprise data, giving employees access to approved tools such as ChatGPT, Gemini, Codex and others, while making governed structured and unstructured enterprise context available through MCP servers.
The conversation goes deeper into CoreWeave’s internal application platform, where employees can move an idea toward production through standardized deployment, observability, traceability, security guardrails and an internal application catalog. Rather than treating governance as a final approval gate, the model embeds controls into the platform and development process.
The episode also explores business engineers, build-versus-buy decisions, AI adoption signals, finance and supply-chain use cases, and how telemetry and machine data could eventually help predict infrastructure problems and dispatch the right person to the right issue.
Key Learnings for Leaders
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Embed AI governance into the platform, not only into committees
Governance does not have to become the bottleneck to AI adoption. The model described uses role-based access, secure data boundaries, automated guardrails and a controlled production deployment process, with approvals where needed before code reaches production.
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Use adoption as a signal of business value.
Instead of focusing only on AI consumption or token costs, leaders can examine which internally built applications attract repeat usage. Strong adoption can indicate that an application is solving a relevant problem by simplifying, eliminating or standardizing work.
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Build versus buy should depend on the problem, data and capability
CoreWeave’s approach leans toward building where teams have clean data, clear requirements and the capability to solve a specific problem. Buying can still make sense where compliance requirements or mature third-party data models provide an advantage.
See how enterprise leaders can scale AI adoption while keeping data, governance and production readiness built in.
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