Markus Kopko

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.

expert panel

No longer confined to analytics dashboards and recommendation engines, AI systems are now initiating transactions, approving workflows, flagging anomalies and even orchestrating other software agents. With this sudden increase in autonomy, business leaders are left asking: Where should humans step back—and where must they stay firmly in control? According to a 2025 McKinsey survey on the state of AI, nearly nine out of 10 organizations now report using AI in at least one business function, yet most are still early in scaling these technologies and many lack robust governance and risk controls. As artificial intelligence advances from advisory tools to agentic systems capable of multi-step planning and execution, the leadership challenge shifts: defining not just what AI can do, but what it should do. Members of the Senior Executive AI Think Tank—a curated group of experts in machine learning, generative AI and enterprise-scale transformation—argue that the real issue isn’t capability but accountability. Across their industry expertise, they all converge on one theme: The boundary between human judgment and machine decision-making must be dynamic, evidence-based and anchored in responsibility. Here is how they recommend drawing—and redrawing—that line.