Punit Bhatia

expert panel

AI has a habit of making yesterday’s infrastructure decisions look permanent—until the next model, vendor or use case comes along. For enterprise leaders, that creates a difficult problem: AI is advancing faster than the infrastructure built to support it, while data platforms, security controls and governance programs can take years to design and implement.So what should companies make permanent, and what should they make easy to replace? We asked members of the Senior Executive AI Think Tank—a curated group of leaders specializing in machine learning, generative AI and enterprise AI applications—to explore how leaders can balance the needs of today with an AI landscape that is likely to look very different tomorrow.Below, they examine what belongs at the foundation of an AI-ready enterprise—and how leaders can build enough flexibility into that foundation to adapt without starting over every time the technology changes.

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.

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The current AI conversation has been dominated by software. Organizations have raced to deploy chatbots, copilots and generative AI tools that promise to boost productivity, improve decision-making and automate knowledge work. But what happens when AI leaves the screen and enters the physical world?That future is already taking shape. AI-powered robots are moving beyond controlled factory environments and into warehouses, hospitals, retail operations and even homes. Companies including Amazon, Tesla and Figure AI are investing billions in autonomous systems capable of navigating complex environments, collaborating with humans and performing tasks that once required manual labor. At the same time, labor shortages, rising operating costs and demographic shifts are creating strong economic incentives for automation. According to the International Federation of Robotics, global demand for industrial robots has more than doubled over the past decade, with more than 4.6 million robots now operating in factories worldwide.Yet despite the excitement, fundamental questions remain unanswered: What milestone will signal that AI-powered robotics has evolved from a promising technology into a mainstream commercial reality? Will it be a breakthrough in capability? A dramatic reduction in cost? Regulatory approval? Or something less obvious?To explore these questions, we turned to members of the Senior Executive AI Think Tank, a curated group of leaders and practitioners specializing in machine learning, generative AI and enterprise AI applications. Below, they share the signals they believe executives should be watching and the conditions that will determine when AI-powered robotics truly crosses into the mainstream.

expert panel

Artificial intelligence has become the fastest-moving investment category in the corporate world. Boards are asking about it, investors expect it and competitors are announcing new initiatives seemingly every week. For many Fortune 500 CEOs, however, the challenge isn't deciding whether to invest in AI—it's deciding where to place the first major bet.The stakes are high because the wrong investment can consume millions of dollars while delivering little business value. Organizations across industries are launching AI labs, experimenting with custom models and deploying new tools at scale, yet many still struggle to achieve measurable returns.That reality raises an important question: If you were making your first significant AI investment today, where would you focus—and what would you avoid?To find out, we asked members of the Senior Executive AI Think Tank, a community of leaders and practitioners specializing in machine learning, generative AI and enterprise transformation. Their answers reveal a striking consensus about where AI creates value, why so many organizations get their priorities wrong and the foundational investments that should come before any large-scale AI deployment.

expert panel

For many organizations, AI training has become synonymous with productivity. Employees learn how to write better prompts, automate routine tasks and generate content faster than ever before. But as AI becomes embedded in everyday business decisions, a more important question is emerging: Are organizations teaching people how to use AI, or how to use it responsibly?AI can generate recommendations, summarize information and accelerate workflows, but it cannot assume accountability for outcomes. That responsibility still belongs to people. Yet many training programs spend far more time on tools than on judgment, ethics, governance and critical thinking.This concern is reflected in Deloitte's “The State of Generative AI in the Enterprise” research, which found that regulatory compliance concerns, risk management challenges and the lack of governance models rank among the leading barriers to scaling AI initiatives. As organizations move beyond experimentation, the challenge is no longer simply getting employees to use AI—it is ensuring they can use it responsibly.To explore what modern AI fluency should look like, we turned to members of the Senior Executive AI Think Tank, a curated community of experts in machine learning, generative AI and enterprise transformation. Their perspectives offer a roadmap for moving beyond AI tool proficiency and building the judgment, oversight and responsible-use practices that enable organizations to create lasting value from AI.