David Obasiolu

expert panel

​​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.

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.

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The FDA’s new generative AI tool, Elsa, could signal the start of AI-native government operations—streamlining scientific reviews, increasing public transparency, and reshaping how trust is earned in digital-era governance. But as Elsa ushers in new efficiencies, AI leaders warn: Success depends on human oversight, ethical frameworks, and explainable systems.

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Digg’s relaunch brings generative AI into the heart of content moderation, aiming to scale oversight across online communities. But can AI manage trust, context, and nuance without human judgment? Members of the AI Think Tank weigh in on what this move means for the future of digital community governance.

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With open-source models like Mistral and Llama gaining traction, the AI landscape is shifting fast. Members of the Senior Executive AI Think Tank break down how open and proprietary platforms will compete—and coexist—over the next 12 months.

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AI agents and memory-enabled models promise enterprise transformation—capturing institutional knowledge, automating workflows and making context-rich decisions. But without robust oversight, they pose security, privacy and accountability risks. Members of the Senior Executive AI Think Tank share insights on the risks and rewards of agentic AI in business.