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Gartner predicts the average Fortune 500 enterprise will have over 150,000 AI agents by 2028. But who’s accountable when scaling from 100 to 1000+ AI agents? Five operating practices CIOs need for AI agent governance, including assigning human owners and defining when humans step in. Continue reading.
Developing a context layer for AI agents requies information architecture and AI governance considerations. A Collibra survey identifies key AI governance concerns, such as instilling trust in AI, addressing data quality, and preparing for evolving regulations. CIOs will need robust AI governance implementation strategies that at least keep pace with AI strategies and implementation. Continue reading.
CIOs facing pressure to deliver AI business value will need trusted platforms for agent development, especially for backoffice HCM and ERP data. At Workday Rising 2026, executives announced the Workday Trusted Agent Platform, 17 job-based AI agents, and new strategic Decision Intelligence agents for the CHRO, CFO, and CIO offices. Continue reading.
At the Cisco WebexOne 2026 conference, executives highlighted the lack of top-down AI transformation, emphasizing the need for strategic development over mere productivity gains. Cisco's approach involves empowering employees through experimentation while fostering a culture of curiosity and focus, which is essential for effective AI-driven transformation. Report by Isaac Sacolick from WebexOne 26. Continue reading.
Exponential increases in deployed AI agents requires new C-level leadership roles around growth, architecture, people/process, and risk management. CEOs have to lead responsibility assignments and setting a transformation imperative. StarCIO proposes responsibilities for CIO, CDO, CISO, CTrO, CMO, CCO, COO, CAIO, CHRO, CTO, CRO, and CFO, and where they need to collaborate. Continue reading.
Legacy ITSM platforms hinder employees in mid-market businesses, causing delays in issue resolution. Embracing AI can significantly enhance IT service operations by simplifying processes, providing insights, and improving employee experiences. AI-driven ITSM platforms facilitate faster responses and enable employee self-service capabilities, making them crucial for modern organizations. Continue reading.
AI tools can boost individual productivity but require leaders to re-engineer workflows for ongoing business value. By fostering AI literacy among all employees and utilizing platforms, partners, and learning programs, organizations can empower staff to adapt and innovate. Successful AI adoption hinges on collaboration and employee engagement in dynamic workflows. Continue reading.
We define the "hallucination tax," as costs incurred when AI outputs are incorrect but presented with confidence. Key strategies for managing this include developing a unified AI registry, establishing guardrails, and monitoring data contexts to enhance AI reliability and reduce costs. A recap of our recent Coffee With Digital Trailblazers with special guest Felix Van de Maele, CEO of Collibra. Continue reading.
In this reflective account, Isaac Sacolick recounts his experience during the 9/11 attacks, blending personal narrative with professional urgency. He details his journey to a merger meeting, interrupted by news of the World Trade Center collapse. Amidst confusion and panic, thoughts of colleagues and loved ones surface, revealing the day’s profound impact on his life. Continue reading.
A research study revealed over 150 AI tools for product leaders.A I is reshaping the roles of agile product managers and owners, pushing them towards customer-centric tasks while leveraging AI tools for efficiency. Examples include enhanced prototyping with AI coding harnesses. For effective use, integrating AI with a clear operational purpose is crucial. Continue reading.
The potential for an AI bubble to burst raises critical questions for CIOs on their AI strategy. While some deliver AI business value, many struggle with ROI and governance. 7 recommendations for CIOs include addressing detractors, catching up on AI governance, and addressing AI debt. Continue reading.
Knowledge management is a key process for developing the AI's context layer. The article explores opportunities and challenges in five industries: healthcare, financial services, construction, manufacturing, and higher ed. Common challenges include change management, executive alignment, data integration, and data governance. Opportunities include faster time-to-decision, improved CX, and enabling agentic AI operations. Continue reading.
My company, StarCIO, provides leadership, learning, and advisory programs for companies looking to accelerate delivering business value from digital transformation. Contact me if you’d like to learn more about partnering opportunities.
Greetings from Isaac Sacolick
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