Will Conaway

expert panel

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.

expert panel

The notion of a “steady state” has quietly disappeared from modern enterprise leadership. In its place is a reality defined by continuous disruption, where artificial intelligence is not just accelerating change but compounding it. Organizations are no longer transforming in phases—they are operating in a constant state of reinvention. For executives, this requires a shift from managing change as an event to leading within change as an environment. Members of the Senior Executive AI Think Tank—a curated group of experts in machine learning, generative AI and enterprise AI applications—bring a front-line perspective to this challenge. Their work across healthcare, cloud architecture, enterprise platforms and AI governance show that the organizations that succeed are not those with the most advanced tools, but those with the most adaptive operating models and leadership mindsets. According to McKinsey’s 2025 report on the state of AI, companies are rapidly scaling AI adoption, yet many struggle to translate that investment into sustained business value—often because their structures, decision-making processes and cultures are not designed for continuous change. To help their fellow leaders better cope with these evolving demands, Think Tank members outline the capabilities executives can no longer treat as optional. Through real-world insights and expert perspectives, they explore how leaders are redesigning operating models, reshaping team expectations and building organizations that don’t just withstand disruption, but continuously learn and perform within it.

expert panel

The race to deploy artificial intelligence is accelerating—and so is the pressure on leaders to act. From boardrooms to product teams, executives are being asked the same question: How fast can we get AI into production? But as organizations rush to capitalize on generative AI, the risks—hallucinations, data leaks and brand damage—are becoming harder to ignore. A National Institute of Standards and Technology (NIST) report on AI risk management emphasizes that without proper governance, AI systems can introduce significant reliability, security and accountability risks into enterprise environments. Insights from the Senior Executive AI Think Tank suggest that this is not a simple trade-off between speed and safety. Instead, it’s a leadership challenge that requires rethinking how organizations define competitive advantage. Below, Think Tank members discuss whether being first with AI is truly the advantage leaders think it is—or if the real differentiator is trust built through disciplined execution, strong governance and a clear understanding of where AI delivers value.

expert panel

The race to dominate artificial intelligence has long been framed as a contest of scale—whoever spends the most on compute, talent and data should win. But Meta’s reported delay of its “Avocado” model, alongside discussions of licensing Google’s Gemini 3 technology, signals a turning point. According to members of the Senior Executive AI Think Tank, the frontier of AI is becoming harder to sustain even for the most well-funded organizations. A recent analysis of Big Tech’s AI spending highlights how companies are pouring tens of billions into infrastructure while facing diminishing returns in performance gains—proving that capital alone is no longer enough to secure leadership. This moment raises urgent questions for executives: If even hyperscalers struggle to keep up, what does competitive advantage in AI actually look like? And where does that leave smaller companies entering the race? Below, Think Tank members attempt to answer these questions while looking toward what’s next. Together, their perspectives outline a new playbook for AI competition—one that begins with a surprising change at the very top.

expert panel

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.

expert panel

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.