WASHINGTON - s4story -- Leading in the era of AI requires a fundamental shift in mindset — from experimenting with isolated tools to orchestrating intelligence across the enterprise. Success will depend on how effectively organizations institutionalize AIOps, redefine systems of record, establish meaningful success metrics, and move beyond pilots into scaled production.

For most enterprises, the real challenge is evolving from initial efforts to integrate AI into operations toward a level of maturity where intelligent systems — including emerging capabilities such as agentic AI — are managed, measured, and continuously optimized for impact.

These were among the themes that came into sharp focus during a recent CIO.com executive dinner roundtable series in San Francisco and Chicago, co-hosted with leaders from Amazon Web Services (AWS) and IBM.

Executives across financial services, manufacturing, healthcare, publishing, higher-education, technology and the public sector gathered to discuss how autonomous AI systems are changing the fundamentals of leadership, control, and enterprise design.

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As enterprises race to harness these capabilities, executives are discovering that the real challenge lies not in what generative and agentic AI can do — but in how organizations choose to govern, scale, and measure them.

From Governance to Growth: San Francisco Insights

In San Francisco, Ally Gardner and Sam Malik of AWS, along with Mahmoud Elmashni and Kapil Gupta from IBM, framed the discussion around one of the most pressing questions facing technology leaders today: how to make agentic AI both powerful and responsible.

Participants agreed that the rise of agentic AI requires a fundamental redefinition of decision rights. As one CIO put it, "autonomy without accountability is chaos." Gupta concurred, noting that organizations are now developing tiered control models in which certain AI functions operate autonomously while human oversight remains mandatory for high-impact decisions.

This structured balance between automation and accountability is fast becoming a cornerstone of AI-era governance.

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"Agentic systems are incredibly fast at problem-solving, but leadership must determine where speed ends and judgment begins," said Elmashni. "That's where frameworks for responsibility and trust must evolve in parallel."

What once was viewed as bureaucratic friction is now being reframed as an innovation accelerator. Gupta explained that adaptive governance frameworks — built to evolve alongside AI models — are allowing organizations to innovate confidently. "Governance isn't about slowing things down anymore," Gupta said. "It's about providing a mechanism for continuous alignment between AI capability and business intent."

Read on here: https://bit.ly/StrategicAILeadership