Goran Paun

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For many executives, AI has already become part of the daily workflow. They use it to summarize reports, find information, draft emails and work through routine tasks. But familiarity with the tools can create a false sense of fluency—and make it harder to recognize the gap between using AI and knowing how to use it strategically.Microsoft research involving thousands of knowledge workers found that generative AI can reduce time spent on tasks such as writing, information retrieval and summarization. But those individual productivity gains do not automatically translate into changes in how organizations make decisions or operate. Research from McKinsey similarly finds that while AI adoption is widespread, most organizations are still struggling to turn it into significant enterprise-level impact.The challenge, then, is not simply getting leaders to use AI more. It is learning to recognize where AI can change the work itself—and developing the judgment to know when, where and how to make that change.So what does genuine AI fluency look like at the leadership level? Members of the Senior Executive AI Think Tank approach that question from different vantage points, spanning enterprise technology, research, finance, retail, design and AI implementation. Their experiences offer a closer look at what happens when leaders move beyond individual productivity and begin applying AI to decisions, workflows, business systems and organizational strategy.

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For all the talk about AI making customer service faster and more personal, there is still a harder question to answer: Does the customer actually feel better served? An instant answer or perfectly timed recommendation may be useful, but usefulness is not the same as feeling understood. When a customer has a complicated problem, is frustrated or simply wants to talk to someone, efficiency can quickly become the problem.That tension is becoming more important as companies bring AI into more customer interactions. Customers are increasingly comfortable with AI when it saves time or effort, but many still expect a human option when the stakes are higher. The challenge then is figuring out how technology can remove friction without taking away the judgment, reassurance and connection that make good service feel like good service.In this article, members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI and enterprise AI applications—explore that challenge from different angles. They discuss personalization and customer memory, where human involvement matters most, how AI can give employees more room for empathy, and how customer signals can shape better products. They also examine what it really means for AI to remember—and how leaders can tell whether an interaction leaves customers feeling understood, rather than simply processed.

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For executives leading AI transformation, one of the most important decisions is also one of the most difficult: deciding where sensitive data should be processed, stored and governed as artificial intelligence becomes part of the enterprise operating model. The architecture choices organizations make today will shape not only their ability to innovate, but also their ability to protect critical information, meet regulatory expectations and maintain trust.The challenge is that there is no single blueprint for secure AI adoption. Leaders must weigh competing priorities, including the speed and scalability of cloud platforms against the control and data sovereignty of private or hybrid environments, the need for strong governance against the risk of slowing innovation and the benefits of advanced AI capabilities against the responsibility to maintain visibility over how data is used. As AI systems create new layers of information through prompts, outputs, embeddings and logs, organizations must consider not only where data resides, but where it flows and whether they can control its entire lifecycle.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, examine the most important trade-offs leaders should consider when designing AI architectures for sensitive or regulated data. They also identify common mistakes organizations are making, from focusing only on storage location to overlooking data derivatives, governance gaps and the operational capabilities required to manage AI responsibly. Because architecture decisions are no longer just technical choices—they are business decisions tied to risk, resilience and long-term value.

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More than 200 economists, AI researchers and technology leaders recently signed the "We Must Act Now" statement, warning that artificial intelligence could reshape the global economy faster than the Industrial Revolution. While the statement calls for stronger institutions and guardrails, it also raises a more immediate question for executives: What should leaders actually do today?Members of the Senior Executive AI Think Tank believe the answer extends well beyond adopting the latest AI tools. Drawing on decades of experience leading enterprise AI, cloud infrastructure, digital transformation and technology strategy, these experts argue that organizations must rethink work itself—redesigning processes, investing in people and establishing governance before AI scales across the enterprise.Their advice comes as organizations accelerate adoption. According to McKinsey's latest State of AI research, AI adoption continues to expand rapidly across industries, with organizations increasingly reporting measurable business impact. Yet the same research also highlights persistent challenges around governance, workforce readiness and risk management, reinforcing the need for thoughtful leadership rather than technology-first decision-making.The following insights from members of the Senior Executive AI Think Tank reveal a consistent message: Organizations that treat AI as a catalyst for better decision-making, stronger employees and more resilient businesses—not simply a cost-cutting exercise—will be best positioned to thrive as AI transforms the economy.

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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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Artificial intelligence has become remarkably good at creating competent work. It can draft marketing copy, generate product descriptions, design visual assets and even emulate established brand voices in seconds. Yet as organizations adopt many of the same foundation models and workflows, a different challenge is emerging: sameness.Instead of creating stronger differentiation, AI often produces outputs that reflect statistical averages rather than distinctive thinking. The result is an increasing number of websites, advertisements and product messages that feel interchangeable.Members of the Senior Executive AI Think Tank, an invitation-only community of leaders advancing enterprise AI, argue that the real opportunity for differentiation lies far beyond selecting the latest LLM. Across industries ranging from design and marketing to cloud infrastructure and retail technology, they point to a common set of competitive advantages: proprietary knowledge, human judgment, organizational context and leadership that gives AI clear direction.Their insights reveal a fundamental shift in how executives should think about AI strategy. Rather than asking which model is best, organizations should ask what unique expertise, customer understanding and decision-making processes they can bring to those models. The following perspectives explore where lasting competitive advantage is emerging—and why the companies that stand out in the AI era may be the ones that invest most heavily in the capabilities machines can't replicate.

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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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AI observability is quickly becoming one of the most consequential shifts in enterprise AI—not because it adds more dashboards, but because it exposes how AI systems actually behave inside real business workflows. For executives, that visibility is both a breakthrough and a burden. It reveals model performance, data quality, user interaction patterns and system drift in real time, yet it often arrives in a form that is fragmented, technical and difficult to translate into decisions that matter at the board level.Organizations are rapidly scaling generative AI and machine learning systems across core operations, but many are struggling to operationalize oversight in a way that connects technical signals to measurable business outcomes. The result is a widening gap between AI capability and executive clarity—where systems are increasingly powerful, but not always understandable in business terms.Members of the Senior Executive AI Think Tank—a curated group of leaders in machine learning, generative AI and enterprise transformation—argue that the issue is not a lack of data. It is a lack of translation. AI observability, they note, only becomes strategically meaningful when organizations move beyond monitoring and toward decision-making frameworks that connect model behavior, risk signals and user impact directly to business KPIs.In the sections that follow, Think Tank members break down how organizations can close this gap in practice—from building operating models that turn observability into action, to identifying behavioral drift before it becomes business risk, to redefining governance so insights don’t remain trapped in technical teams. They also surface the most persistent obstacles executives face today—including signal overload, fragmented ownership and the absence of shared language between business and technical stakeholders—and offer concrete ways leaders can turn visibility into decisions that drive measurable value.

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Artificial intelligence is often framed as a race: faster models, bigger investments, larger datasets and more powerful infrastructure. But beneath the headlines lies a more consequential question for business leaders, policymakers and investors alike: Who gets to compete?A growing share of the AI ecosystem is controlled by a relatively small number of organizations with access to the world's largest compute resources, proprietary datasets and distribution channels. This means the debate is no longer simply about what AI can do but about whether the next wave of innovation will emerge from an open marketplace of ideas or from a handful of dominant ecosystems.To explore that question, we asked members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI, digital transformation and enterprise AI applications—what single rule they would change to improve AI competition.While their recommendations differ, a clear theme emerges: The future of AI should be shaped by innovation, trust and customer value rather than lock-in, opacity or concentrated control. The following insights offer a timely look at how technology and business leaders believe a more competitive—and in many cases safer—AI ecosystem can be built.

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As organizations race to develop generative engine optimization (GEO) strategies, many are approaching AI visibility the same way they approached search engine optimization over the last two decades: Publish more content, optimize keywords and try to improve rankings. Yet the rise of generative AI is changing how information is discovered, evaluated and surfaced.Members of the Senior Executive AI Think Tank—a curated group of executives, technologists, AI practitioners and digital transformation leaders—argue that many organizations are operating under flawed assumptions about how generative systems work. Their collective message is strikingly consistent: AI visibility is less about gaming algorithms and more about establishing trust, authority and credibility across the digital ecosystem.According to a 2024 Gartner forecast on generative AI and search, traditional search traffic is expected to decline significantly as users increasingly rely on AI assistants and conversational interfaces to find information. As AI-generated responses become a primary gateway to information, organizations must rethink how they establish authority online.The experts below explain why many GEO assumptions are misguided and where leaders should focus their efforts instead.