Muthukumarapandian Chandrasekaran

expert panel

AI agents are becoming capable of doing more than generating information—they can now analyze code, identify vulnerabilities, write potential exploits and interact with the systems they are designed to help secure. Those capabilities can dramatically accelerate security work, but they also introduce new risks when an agent can move from identifying a problem to taking action without human intervention.So where should companies draw the line between useful autonomy and unacceptable risk? And what should an AI agent be allowed to do independently when its actions could affect production systems, sensitive data or security controls?Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—examine how leaders can use sandboxing, least-privilege access, deterministic controls and human oversight to contain risk, while also addressing less technical questions around liability, accountability and reversibility. Together, they offer a practical framework for deciding not just what AI agents can do, but what organizations should actually allow them to do.

expert panel

The AI industry has been celebrating increasingly capable demonstrations: a model beats a benchmark, an autonomous vehicle completes a route, an AI agent handles a task that once required a person. But a successful demonstration answers only one question: Can the technology work?Commercialization demands much harder answers: Can it work every day? What happens when it fails? Who is responsible? Can the surrounding operation absorb those failures? And do the economics still work when real customers are paying for the result? Nevada’s recent approval of robotaxi networks for Tesla, Waymo and Uber opens the door to thousands of autonomous vehicles operating commercially, and brings those questions into sharp focus.Members of the Senior Executive AI Think Tank bring perspectives from machine learning, enterprise technology, product management, data architecture, financial services, infrastructure, healthcare and design to this next stage of the AI conversation. Below, they examine what it takes to move beyond a working model and build a system that can withstand real-world complexity—from managing failures and infrastructure to earning regulatory and public trust, establishing accountability and making the economics work at scale.

expert panel

The machine says the patient is low risk. The veteran clinician says something feels wrong. The AI recommends cutting a workstream. The executive team knows that workstream is critical to a customer they are trying to win. The coding assistant produces perfectly functioning code—except it has quietly recreated a function that already exists somewhere else in the codebase.These aren't hypothetical scenarios. They are the kinds of moments leaders increasingly face as AI moves from experimentation into decisions that affect customers, employees, operations and the bottom line. Automatically trusting the machine ignores context, intuition and accountability. Automatically siding with the human can mean overlooking patterns and possibilities that AI can uncover. So what should happen when an AI system and an experienced human reach different conclusions?Members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI and enterprise AI applications—have encountered this tension firsthand across healthcare, manufacturing, software development, enterprise transformation, market intelligence and technology strategy. In the examples that follow, they share what happened when AI and human expertise diverged, how their teams responded and what those moments revealed about the roles each should play in high-stakes decision-making.