Good morning. At Fortune 500 companies, AI governance has become a top priority for boards, as many are still working to deploy AI at scale.
Sedgwick, a global risk and claims administration partner, published its 2026 forecasting report identifying key AI trends across sectors. The results contend that 70% of Fortune 500 executives surveyed say their companies have AI risk committees, 67% report progress on AI infrastructure, and 41% have a dedicated AI governance team. Yet only 14% say they are fully ready for AI deployment, underscoring a growing gap between formal governance structures and real-world AI readiness.
Executives have clearly moved fast to formalize oversight. But the foundations needed to operationalize those frameworks—processes, controls, tooling, and skills embedded in day-to-day work—have not kept pace, according to the report. The findings are based on a survey of 300 senior leaders at Fortune 500 companies, including C-suite executives (CEO, COO, CFO, CHRO, CRO) as well as EVPs, SVPs, VPs, and directors.
Sedgwick finds that the leading implementation challenge is the rapid pace of AI change, followed by difficulties in executing governance and managing data privacy. Regulatory uncertainty and change management also rank as major hurdles. These barriers are mostly organizational and process-oriented rather than purely technical, suggesting that companies will succeed only if they align people, policy, and technology at the same time, according to the report.
‘AI has become a board-level mandate’
Those themes were front and center at the recent Fortune Brainstorm AI event in San Francisco last week, where a panel on the next phase of AI governance translated the numbers into lived experience. Navrina Singh, founder and CEO of Credo AI, an AI governance platform, outlined the three biggest gaps she sees with clients.
The first is visibility. Many organizations still lack a comprehensive view of where AI is being used across their business, Singh explained. Shadow AI and unsanctioned tools proliferate, while sanctioned projects are not always cataloged in a central inventory. Without this map of AI systems and use cases, governance bodies are effectively trying to manage risk they cannot fully see.
The second gap is conceptual. “There’s a myth that governance is the same as regulation,” Singh said. “Unfortunately, it’s not.” Governance, she argued, is much broader: It includes understanding and mitigating risk, but also proving out product quality, reliability, and alignment with organizational values. Treating governance as a compliance checkbox leaves major gaps in how AI actually behaves in production.
The final one is AI literacy. “You can’t govern something you don’t use or understand,” Singh said. If only a small AI team truly grasps the technology while the rest of the organization is buying or deploying AI-enabled tools, governance frameworks will not translate into responsible decisions on the ground.
Singh also highlighted how the AI landscape is evolving—from predictive models to generative AI and now to agentic systems that can act autonomously across workflows. “AI has become a board-level mandate,” she said. “If you’re not using AI as a company, you are going to be pretty irrelevant in the next, I would say, 18 to 24 months.”
What good governance looks like, Singh argued, is highly contextual. Organizations need to anchor governance in what they care about most. She offered the example of one of her clients, PepsiCo, which cares deeply about reputation and invests heavily in responsible AI. For the company, any AI system that interacts with customers—whether in customer service or via a chatbot—must be reliable, fair, and reflective of its brand values, she explained.
For other organizations, good governance may mean prioritizing auditability, bias mitigation, or resilience. The common thread, Singh said, is moving beyond structures on paper to operational practices that make AI safe, trustworthy, and fit for purpose.
Sheryl Estrada
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This story was originally featured on Fortune.com
