leadership

AI Without Sovereignty Is Just Outsourced Intelligence

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In his opinion piece, Floyd DCosta argues that enterprises adopting AI often gain capability but lack sovereignty—control over how AI models and data are used—creating long-term risks and dependencies on third-party vendors. He emphasizes AI sovereignty as essential, encompassing governance, transparency, data and model control, operational autonomy, and strategic independence, warning that without it, organizations may inadvertently cede their competitive intelligence and face regulatory and operational challenges.

https://www.cio.com/article/4147102/ai-without-sovereignty-is-just-outsourced-intelligence.html

Stop Building Security Goals Around Controls

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Devin Rudnicki, CISO at Fitch Group, emphasizes that security goals should be aligned with business outcomes rather than focused solely on controls, advocating for strategies anchored in corporate objectives, real cyber threats, and industry standards. She highlights three key metrics for security programs—value, risk, and maturity—and stresses the importance of presenting risk in actionable terms for leadership, balancing innovation speed with measured risk, and using automation to free human resources for higher-value work.

https://www.helpnetsecurity.com/2026/03/18/devin-rudnicki-fitch-group-ciso-business-alignment/

The Operational Excellence Playbook for AI Transformation

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The article outlines a framework for AI transformation grounded in operational excellence disciplines like maturity modeling, risk management, cost optimization, and change management, emphasizing that organizations must first establish a strong foundational maturity before adopting AI. It highlights that successful AI adoption depends more on building a robust data layer and ontology aligned with business objectives than merely selecting advanced AI models, and asserts that experienced CIOs who have matured their IT organizations are best positioned to lead AI transformations.

https://nationalcioreview.com/articles-insights/the-operational-excellence-playbook-for-ai-transformation/

The CTO Is Dead. Long Live the CTO

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The article argues that the traditional role of the CTO as the sole technical decision-maker is obsolete in the AI era, where advanced AI systems can rapidly design and optimize complex architectures beyond human capability. Instead, CTOs must shift from gatekeepers to architects of systems, focusing on building frameworks that amplify organizational impact, lead transformative change actively, manage technology economics, and continuously adapt to new tools and workflows. This new mandate demands a disciplined, strategic leader who orchestrates AI-human collaboration to drive speed, quality, and innovation at scale.

https://www.cio.com/article/4145039/the-cto-is-dead-long-live-the-cto.html

Using AI to Pick Team Leaders Without Crossing Ethical Lines

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The featured article discusses how AI can assist CIOs in identifying potential team leaders by analyzing performance data objectively, while cautioning that humans must maintain final hiring authority to avoid legal, ethical, and bias-related risks inherent in AI-based decision-making.

https://www.informationweek.com/it-leadership/using-ai-to-pick-team-leaders-without-crossing-legal-or-ethical-lines

Who in the C-Suite Should Own AI?

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The article discusses the critical question of which C-suite executive should own and oversee AI initiatives in organizations, highlighting the differing perspectives of roles such as the CIO, COO, CFO, Chief Risk Officer, CHRO, and Chief Data Officer. This ownership decision significantly impacts a company's AI strategy, investment, and the distribution of authority and influence among senior leaders.

https://hbr.org/2026/03/who-in-the-c-suite-should-own-ai

The Modern CIO Is No Longer a Technologist — They’re an Architect of Enterprise Decisions

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The article argues that the modern Chief Information Officer (CIO) role has evolved from being primarily a technologist focused on execution to becoming an architect of enterprise decision-making systems. It emphasizes that most technology transformation failures stem from flawed strategy, governance, and decision structures rather than execution problems, making CIOs accountable for designing clear outcomes, decision rights, tradeoff processes, and governance to enable sustained business value and agility.

https://www.cio.com/article/4144298/the-modern-cio-is-no-longer-a-technologist-theyre-an-architect-of-enterprise-decisions.html

CISO Conversations: Aimee Cardwell

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A key conversation highlights Aimee Cardwell's journey from Netscape to her current role as CISO in Residence at Transcend, emphasizing the need for collaboration, low ego, curiosity, and addressing burnout in cybersecurity teams. She advocates for strategic and tactical balance in leadership, continuous learning, and a team-focused approach to problem-solving. Cardwell also notes the challenges in demonstrating successful security efforts and the growing threat of sophisticated AI-generated phishing attacks.

https://www.securityweek.com/ciso-conversations-aimee-cardwell/

What Changes When You’ve Been a CISO More Than Once?

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CISO Series highlights insights from a February 2026 Reddit AMA with seasoned CISOs discussing job transitions, board communication, and vendor relations. Key points include the need for CISOs to translate technical risks into business terms for effective board discussions, the importance of building relationships over sales, and recognizing that while fundamental skills carry over, specific playbooks must adapt to new contexts. A clear distinction between full-time and retained CISO roles was also emphasized, reflecting on the necessity of understanding organizational commitment to cybersecurity outcomes.

https://cisoseries.com/what-changes-when-youve-been-a-ciso-more-than-once/

5 Metrics to Drive Successful AI Outcomes

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Despite significant AI investments, many enterprises struggle to achieve measurable results. This is often due to a misalignment between AI projects and strategic business goals, as well as a lack of understanding of how to measure AI success. To drive successful AI outcomes, organizations should align AI projects with strategic business goals, understand the true costs of AI, and measure success based on the impact on business outcomes rather than just financial metrics.

https://www.cio.com/article/4137420/5-metrics-to-drive-successful-ai-outcomes.html

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