Aishwarya Shah
Independent ResearcherIndependent Researcher
Aishwarya Shah
Published content
expert panel
AI can analyze markets, forecast business outcomes and generate strategic recommendations in seconds. For all executives, that means more information, more options and more ways to approach complex decisions. But having access to all that intelligence doesn't make an exceptional leader. Someone still has to decide which opportunities deserve attention, recognize when the numbers don't tell the whole story and take responsibility for the choices that shape an organization.Leadership has always involved navigating uncertainty and working with people who bring different priorities, experiences and expectations to the table. Those responsibilities remain firmly in human hands, even as AI becomes part of everyday business operations. How executives approach those responsibilities will have a lasting impact on their organizations.To explore how the role of the executive is evolving, we asked members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, to share their perspectives on the judgment, experience and leadership capabilities that will matter most as AI becomes increasingly embedded in business decision-making.
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Writing code has always taken time. Now, with AI, developers can produce in minutes what might once have taken hours. For companies under pressure to move faster and do more with less, that sounds like a clear win. But getting code written is only one part of building software. Someone still has to review it, test it, make sure it’s secure and keep it running once it reaches customers.So if teams are producing more code than ever, how do they know they’re actually getting more productive? Faster development doesn’t mean much if work piles up waiting for approval, bugs make their way into production or the cost of maintaining new software keeps climbing. And the more code a company produces, the more important it becomes to know what’s worth building in the first place.Members of the Senior Executive AI Think Tank, a curated group of experts in machine learning, generative AI and enterprise AI applications, weigh in on what AI-driven productivity really looks like, where the biggest bottlenecks are emerging and how leaders can tell whether faster coding is translating into better business results.
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For an AI company with limited computing resources, competing with well-funded technology giants can seem like an uphill battle. The largest AI developers can invest billions of dollars in advanced chips, massive training runs and increasingly sophisticated foundation models. Smaller companies rarely have the capital to match that spending—and trying to do so can be a costly strategic mistake.The opportunity lies in recognizing that customers do not necessarily need the biggest model. They need solutions that solve specific problems reliably, affordably and in ways that fit their businesses.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, share their perspectives on how AI companies with limited compute resources can compete in an increasingly crowded market. Drawing on their experience across AI development, enterprise technology and business strategy, they explore the challenges facing resource-constrained companies, the strategic decisions that can shape their competitive position and the opportunities emerging as AI technology evolves.
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AI progress is often reduced to a leaderboard: a model scores higher on reasoning, coding or multimodal benchmarks, and the industry moves on to the next release. But benchmarks are snapshots of what a model can demonstrate under controlled conditions. They do not always capture what happens when increasingly capable models are connected to an organization’s data, software, physical environments and decision-making processes.The 2026 AI Index from Stanford HAI reports that frontier AI capabilities are advancing faster than many established benchmarks can measure, with some evaluations reaching saturation within months.Members of the Senior Executive AI Think Tank see a similar pattern from the front lines of enterprise AI, machine learning, robotics, cybersecurity, cloud technology and digital product development. They point to a broader question for business leaders: What are today’s most capable AI models beginning to do that could matter far more than benchmark gains suggest? Here, they explore the emerging capabilities they believe deserve closer attention—and what those developments could mean for the way organizations build products, make decisions and operate in the years ahead.
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
Sep 11, 2026
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.
