• Share this post Keep up with us Summary Why AI becomes an operational capability only when it shows up in the P&L and business KPIsThe architectural shifts that determine whether AI scales-or stallsA 12-month start, stop, continue framework for enterprise AI leaders Why AI becomes an operational capability only when it shows up in the P&L and business KPIs The architectural shifts that determine whether AI scales-or stalls A 12-month start, stop, continue framework for enterprise AI leaders As enterprisesmove beyond pilots and proofs of concept, a new question is emerging in executive conversations: when does AI stop being a series of projects and start becoming part of how the business runs? • Naveen Zutshi, CIO at Databricks works closely with CIOs and business leaders navigating the shift from experimentation to enterprise-scale AI. • In this Q&A, Naveen draws on prior leadership roles at companies like Palo Alto Networks, Gap Inc., and Walmart, where he led complex modernization efforts that transformed legacy environments into scalable, cloud-first architectures. • What emerged in our conversation is clear: the inflection point is not about models. • It is about modernization, governance, and operational discipline. • AI Is Moving From Experiments to the P&L Catherine:What is the clearest sign you are seeing that AI experimentation is giving way to AI as an operational capability?

Article Summaries:

  • Enterprises are moving AI from isolated pilots to a core operational capability. CIO Naveen Zutshi notes three clear signs: AI is now embedded in daily work-especially in regulated sectors like healthcare and finance-business leaders are actively discussing and demonstrating AI impacts, and AI spend has shifted from discretionary innovation budgets to formal P&L line items. The transition is driven by modernization, governance, and operational discipline rather than model maturity. Key friction points remain talent shortages and legacy infrastructure; outdated systems slow productivity and drain skilled engineers. Modernizing compute, storage, and data architecture is presented as a no‑regret move to unlock AI’s full value.

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