Fabio Danze Montini

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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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AI transformation rarely happens in isolation, often unfolding alongside broader digital modernization, cultural shifts and evolving business models. The challenge for senior leaders is not just deciding what to implement, but when and how fast. Poor sequencing can overwhelm teams, stall progress and create what many now call “pilot purgatory.” Insights from the Senior Executive AI Think Tank—a curated group of experts in machine learning, generative AI and enterprise-scale transformation—prove that momentum is not about speed alone. It’s about sequencing initiatives in a way that aligns with human capacity, organizational readiness and measurable value. A recent Forbes analysis on barriers to AI adoption highlights that many organizations struggle to fully integrate AI despite its promise, citing leadership inertia, skills gaps and unclear implementation strategies as persistent obstacles. In other words, the gap is rarely about the technology itself—it’s about how initiatives are staged, scaled and absorbed across the business. The following perspectives from Think Tank members offer an actionable roadmap for sequencing AI initiatives in a way that sustains momentum without overwhelming teams.

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The nature of teamwork is undergoing one of the most significant transformations since the rise of the digital workplace. As artificial intelligence moves from a supporting tool to an embedded collaborator, organizations are rethinking not only how work gets done, but what collaboration truly means. A widely cited report from McKinsey highlights that generative AI could automate up to 30 percent of hours worked across the U.S. economy by 2030, fundamentally reshaping roles and workflows. But this shift is not simply about efficiency—it is about redefining the human role within teams. Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in machine learning, generative AI and enterprise applications—believe teams will not necessarily disappear, but will instead evolve into hybrid ecosystems where human judgment, creativity and ethical oversight intersect with AI-driven speed, scale and synthesis. The following insights explore how that evolution will unfold—and what leaders must do to stay ahead.

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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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As artificial intelligence matures, one question looms large for executives: Where will durable revenue actually come from? Despite explosive adoption, many AI products still struggle to convert usage into sustainable profit. The shift from experimentation to enterprise value is now underway—and the stakes are high. Insights from the Senior Executive AI Think Tank—a curated group of leaders in machine learning, generative AI and enterprise systems—point to a clear trend: Profitability will not come from novelty, but from deeply embedded, outcome-driven applications. A recent Forbes report on AI ROI in the enterprise found that more than half of companies using AI are already seeing measurable revenue gains, with many reporting 6% to 10% growth, and some exceeding 10%. The findings reinforce a critical shift: Organizations are prioritizing AI solutions tied directly to business outcomes rather than experimental tools. What emerges from the Think Tank’s collective perspective is not a single dominant model, but a clear direction of travel. Enterprise copilots, verticalized AI systems, outcome-based pricing and workflow-native automation are converging into a new blueprint for profitability—one rooted in integration, accountability and measurable results. The following insights break down how these models are taking shape in practice, and what leaders must prioritize now to turn AI from a promising capability into a dependable revenue engine.

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Europe has spent the last decade establishing itself as the global leader in technology regulation. The General Data Protection Regulation (GDPR) reshaped how organizations handle personal data worldwide, and the European Union’s landmark AI Act aims to set guardrails for high-risk AI systems across industries. Yet policymakers now appear willing to recalibrate. European officials have begun discussing potential simplifications or delays to portions of the AI Act and related digital rules as they confront a widening innovation gap with the U.S. and China. The EU’s strict regulatory framework has slowed the pace of large-scale AI experimentation compared with other global tech hubs, putting them at a distinct disadvantage in the market. Members of the Senior Executive AI Think Tank—a curated network of leaders specializing in machine learning, generative AI and enterprise AI strategy—say the debate isn’t simply about regulation versus innovation. Instead, they argue that Europe’s regulatory approach has quietly limited several categories of AI development, from cross-border data platforms to real-time industrial automation. If policymakers move forward with regulatory adjustments, the ripple effects could be significant: Startups may gain the freedom to experiment faster, enterprises may finally scale AI deployments beyond pilot programs and the EU could evolve from global rule-setter into a more formidable technology competitor. Below, Think Tank members explain what Europe may have been holding back—and what could happen next.

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In boardrooms around the world, artificial intelligence has shifted from experimentation to execution. Enterprise leaders are no longer asking whether to deploy AI—they are asking how to scale it across jurisdictions that disagree on what “responsible” looks like. The regulatory map is anything but uniform. The European Union’s risk-based AI Act framework takes a precautionary stance, while the United States continues to rely on sector-specific oversight and executive guidance. At the same time, public trust remains fragile. According to Edelman’s 2024 Trust Barometer, a majority of global respondents report concern that innovation is moving too quickly without sufficient safeguards—an anxiety that directly affects adoption, investment and brand reputation. For AI leaders, this divergence creates both friction and opportunity. The organizations that treat ethics and governance as strategic design challenges—not compliance checklists—will be positioned to expand confidently across markets. Members of the Senior Executive AI Think Tank—a curated group of machine learning, generative AI and enterprise AI experts—argue that navigating global AI complexity requires a shift in mindset. Innovation and compliance are not opposing forces. When structured intentionally, they reinforce one another. The following strategies outline how leaders can operationalize that balance in practice.

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AI didn’t just make industry headlines in 2025; it got embedded into everyday knowledge-heavy work, from research and content creation to recruiting and analytics. McKinsey & Company’s November 2025 report on the state of AI noted that 88% of respondents now regularly use AI to handle at least one business function, representing a significant year-over-year jump. AI is changing how value is created, how decisions get made, and what “good work” looks like when speed and automation are always on the table. The AI revolution isn’t limited to business and industry; broader cultural shifts hint that artificial intelligence is moving from a novelty to a norm among consumers as well. With 61% of multinational survey respondents saying they’ve used a generative AI engine, it’s clear that AI is forging ahead as a personal tool for research, education, shopping and even entertainment.  Looking ahead into 2026, AI’s growing reach across industries and culture has big implications not just for technology teams, but for anyone whose work depends on interpretation, decision-making or trust. Drawing on their real-world expertise, members of the Senior Executive AI Think Tank share their perspectives on how AI is likely to shape business and culture in 2026, why those changes matter and which roles, tasks and industries may be hit by the next wave of disruption first.