Andre Shojaie

expert panel

For decades, the technology industry's infrastructure strategy has been remarkably straightforward: Build bigger data centers, add more fiber and deploy more compute capacity closer to users. But what if the next major leap in AI infrastructure happens above the planet rather than on it?That question is gaining attention as SpaceX continues expanding its Starlink satellite network and explores ways its orbital infrastructure could support AI-related computing and global data movement. While the concept of space-based AI infrastructure remains in its early stages, it represents a potentially significant shift in how organizations think about compute, connectivity and data distribution. Instead of relying exclusively on terrestrial networks, future AI systems could leverage orbital infrastructure to extend services into remote regions, improve resilience and create entirely new competitive dynamics.The idea is gaining traction at a time when demand for AI infrastructure is accelerating rapidly. According to a Goldman Sachs analysis, AI-related data center power demand is expected to increase dramatically through the end of the decade as organizations race to secure the compute capacity needed to support next-generation AI applications. As those investments accelerate, executives are increasingly asking whether future infrastructure strategies will be limited to Earth—or whether space will become a critical extension of the global AI stack.To better understand the opportunities and risks, members of the Senior Executive AI Think Tank shared their perspectives on how space-based AI infrastructure could reshape cloud providers, telecommunications companies and AI platform vendors over the next decade. Their insights reveal both extraordinary possibilities and significant challenges, from global connectivity and distributed computing to governance, economics and the growing concentration of infrastructure power.

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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.

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As generative AI reshapes industries from media and marketing to software development and healthcare, one question is becoming impossible for enterprises, policymakers and technology providers to ignore: Who should benefit when AI systems are trained on human-created content?That debate has intensified as courts and regulators scrutinize how AI models are built, how synthetic media is distributed and whether creators deserve compensation when their work contributes to commercial AI products. Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—say the future of AI depends on building sustainable systems that balance innovation with accountability, transparency and trust.Lawsuits and copyright disputes over AI training data have accelerated globally, while companies such as Adobe continue advocating for licensed datasets and provenance frameworks designed to verify content authenticity. At the same time, enterprise adoption of generative AI continues to surge, with a McKinsey study on the state of AI finding that organizations are rapidly increasing investments in generative AI initiatives despite ongoing governance concerns.The challenge now facing the industry is not simply whether AI companies should compensate creators, but how to build systems that make compensation, transparency and innovation sustainable at scale. Below, Think Tank members outline what that future could look like—from collective licensing models and provenance standards to creator opt-in frameworks, enterprise governance strategies and new approaches to trust in the age of generative AI.

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As multimodal AI moves rapidly from novelty to baseline expectation, companies are confronting a deeper challenge than simply adding new features. Users increasingly expect software to understand text, voice, images and video simultaneously, while preserving context seamlessly across every interaction. That shift is forcing organizations to rethink how products are designed, architected and differentiated.Members of the Senior Executive AI Think Tank say the next era of product competition will center less on standalone AI capabilities and more on orchestration, workflow intelligence and trust. Their insights arrive as major technology companies race to integrate multimodal capabilities into mainstream applications. Multimodal systems capable of understanding and generating across formats are becoming foundational to enterprise software strategy. At the same time, organizations are discovering that simply embedding AI into existing workflows does not automatically create better user experiences.Instead, experts argue, multimodal AI is changing the very definition of interface design. Products are evolving from static tools into adaptive systems that anticipate intent, reduce friction and collaborate more naturally with users. The insights that follow explore why multimodal AI is forcing companies to rethink everything from UX design and workflow orchestration to trust, memory and product differentiation—and what leaders must do now to stay competitive.

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​​The rapid expansion of artificial intelligence across government—from cybersecurity to citizen services—is reshaping national security itself. As AI moves into critical decision-making, companies building these systems are evolving from technology providers to strategic partners with real geopolitical influence. And adoption is accelerating fast. AI is moving from experimental pilots to mission-critical infrastructure, powering intelligence analysis, threat detection and operational decisions in real time. With this reliance comes high stakes: Errors carry strategic, legal and human consequences, making accountability, transparency and ethical boundaries essential. For AI companies, this creates a defining tension: how to support national security objectives while maintaining principled limits on technology use. Senior Executive AI Think Tank members—a curated group of leaders in AI governance, enterprise transformation and digital innovation—argue that firms establishing clear guardrails now will shape global standards, build trust and secure long-term advantage. Below, they explain how AI companies can balance national security partnerships with ethical guardrails—and what risks or opportunities they see in drawing firm lines on how this technology can be used.

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Across industries, executives are investing aggressively in artificial intelligence. Yet despite billions spent on experimentation, relatively few organizations have turned AI pilots into scalable platforms that generate repeatable value. According to PwC’s Global CEO Survey, 56% of CEOs report they’ve seen neither revenue nor cost benefits from investments in AI—a signal that experimentation alone is not enough to create enterprise impact. Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in enterprise AI, machine learning and digital transformation—say the problem is rarely technical. Instead, organizations struggle with leadership alignment, operating models, governance and cultural change. Below, their insights reveal a consistent theme: Scaling AI requires redesigning how companies operate—not simply deploying more technology.