Matan Mishan

expert panel

In the past, the easiest way to talk about a technology investment has been to put a number on it: How much did it cost, and what did the company get back? With AI, that calculation is becoming harder to take at face value.An AI system can save thousands of hours without reducing headcount. It can help employees make better decisions without generating an immediately measurable dollar value. It can turn a successful pilot into a new way of working—or quietly remain a one-off experiment that never scales. And it can change who holds critical expertise inside an organization, with consequences that may not appear on a balance sheet for years.That raises a more consequential question for CEOs: What should you measure if you want to know whether your AI strategy is actually making the organization better?Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, suggest that CEOs shouldn't abandon ROI—but here are the other metrics they recommend measuring to capture not only what AI delivers today, but what it enables the organization to become.

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.