Lynn Comp

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The AI industry has embraced a simple idea about data centers: If AI needs more computing power, build bigger facilities and pack them with more chips. But as the infrastructure race accelerates, that assumption is becoming harder to defend. Power availability, grid capacity, cooling, water and the physical limits of getting new facilities online are emerging as constraints that raw compute cannot solve.The question matters because data centers are long-term investments being built around a technology that is changing at remarkable speed. The infrastructure designed for training enormous models may not be what businesses need as inference workloads grow, models become more efficient and AI moves closer to where data and users actually reside.There are also business questions hiding underneath the engineering one: How much compute does a company actually need? Where should it run? What happens when power costs change or a new generation of hardware makes today's architecture less attractive? And how should CEOs think about infrastructure that needs to remain useful even as the AI workloads it supports evolve?Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, offer a range of perspectives on what should come next. In the discussion that follows, they examine whether bigger data centers are really the answer, where power and efficiency fit into the equation, why inference deserves a different infrastructure strategy and which overlooked factors—from memory and networking to reliability and data governance—could determine whether an AI investment delivers lasting business value.

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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.

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Artificial intelligence may be transforming the enterprise, but behind every successful AI initiative is something far less glamorous: disciplined data engineering.As organizations race to deploy generative AI, agentic systems and increasingly sophisticated analytics, many are pouring resources into new models, cloud platforms and AI applications. Yet time and again, ambitious projects fail to deliver meaningful business value—not because the technology falls short, but because the underlying data is inconsistent, poorly governed or difficult to trust.According to McKinsey's latest State of AI research, organizations seeing the strongest returns from AI distinguish themselves not by the models they choose, but by the maturity of the data, governance and operating foundations supporting those models. In other words, AI success begins long before a prompt is entered or an algorithm is deployed.Members of the Senior Executive AI Think Tank, a curated community of executives specializing in machine learning, generative AI and enterprise AI applications, have witnessed this firsthand across industries ranging from healthcare and financial services to manufacturing, retail and cloud computing. Below, they outline the foundational data engineering capabilities they believe consistently deliver the greatest business value and why leaders should take more notice.

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Open-weight large language models have become one of enterprise AI's hottest topics. Their promise is compelling: greater control, improved privacy, customization and freedom from vendor lock-in. Yet many executives remain focused on one number—the licensing cost—while overlooking the far larger operational commitment required after deployment.Members of the Senior Executive AI Think Tank, a community of executives and practitioners leading AI strategy across industries, agree that organizations should evaluate open-weight models the same way they would any other mission-critical technology platform: through total cost of ownership, long-term operational resilience and measurable business outcomes.McKinsey has emphasized that successful AI adoption depends not only on selecting the right models but also on building the data infrastructure, operating models and organizational capabilities needed to scale them. For enterprises adopting open-weight models, those ongoing investments can quickly become a larger cost factor than the model itself.In this article, AI Think Tank members examine the hidden costs and strategic considerations behind open-weight AI adoption, sharing how executives should think about infrastructure, talent, security, governance, maintenance and the business cases where owning and operating these models can create a meaningful advantage—and when the hidden costs outweigh the benefits.

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When OpenAI unveiled Jalapeño, its first custom AI inference chip developed with Broadcom, the announcement represented more than a hardware milestone. It highlighted a broader shift in the AI industry: the race to make intelligence faster, more affordable and more accessible at scale. As the cost of running large language models declines, product leaders face a new question—not simply what AI can do, but what products become possible when intelligence is inexpensive enough to operate continuously.For much of the generative AI era, product teams have designed around scarcity. They have limited model usage, shortened context windows, reduced reasoning steps and carefully managed AI interactions because every inference call carries a cost. But as custom silicon and AI infrastructure improvements drive down those constraints, AI can move from an occasional feature users activate to an always-present capability embedded throughout workflows. Research from McKinsey & Company estimates that generative AI could create trillions of dollars in annual economic value, but capturing that opportunity will require organizations to integrate AI into core business processes rather than treat it as a standalone tool.Members of the Senior Executive AI Think Tank believe the next generation of AI products will not simply be faster versions of today’s copilots. Below, they explore how OpenAI’s Jalapeño chip could reshape product design, unlock previously uneconomical AI applications and redefine the competitive landscape for organizations building the next generation of intelligent products.

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Artificial intelligence is rapidly redefining the cybersecurity battlefield, shifting the balance between defenders and attackers at a pace many organizations are struggling to match. As enterprises embed generative AI, autonomous agents and machine learning into critical workflows, the attack surface is expanding just as quickly as defensive capabilities evolve.This tension is at the center of discussion among members of the Senior Executive AI Think Tank, a curated group of leaders specializing in enterprise AI, machine learning and responsible AI deployment. To them, AI is not just a technology upgrade—it is a structural shift in how cyber risk is created and managed.According to the National Institute of Standards and Technology’s AI Risk Management Framework, organizations adopting AI face heightened risks related to system reliability, security vulnerabilities and adversarial manipulation, even as they gain powerful new defensive tools. At the same time, a Google Threat Intelligence Group analysis on AI-enabled threat activity warns that adversaries are increasingly using generative AI to accelerate vulnerability discovery, exploit development and initial access—signaling a shift toward more automated and scalable cyber intrusion models.With this knowledge, senior executives are asking a pressing question: Over the next five years, should we be more optimistic about AI’s role in cybersecurity—or more concerned? And more importantly, what concrete actions should leaders take today to stay ahead of the curve?Their insights suggest the answer is not binary—but it is urgent.