Mohan Krishna Mannava

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As AI becomes inseparable from competitive strategy, executives are confronting a difficult question: Who actually owns AI? Traditional org charts, designed for slower cycles of change, often fail to clarify accountability when algorithms influence revenue, risk and brand trust simultaneously. Without oversight and clear ownership of responsibility, issues like “shadow AI” deployments that increase compliance and reputational risk can quickly get out of hand. To prevent this problem, executive teams are rethinking AI councils, Chief AI Officers and cross-functional pods as strategic infrastructure—not bureaucratic overhead. Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in machine learning, generative AI and enterprise AI deployment—argue that this structure matters, but not in the way most organizations assume. Below, they break down how leading organizations are restructuring for AI: what belongs at the center, what should be embedded in the business and how executive teams can assign clear ownership without slowing innovation.

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AI agents are no longer experimental tools tucked inside innovation labs. They are drafting contracts, recommending prices, screening candidates and reshaping how decisions are made across companies. As adoption accelerates, however, many organizations are discovering a sobering truth: Knowing how to use AI is not the same as knowing when not to. Members of the Senior Executive AI Think Tank—a curated group of technologists, executives and strategists shaping the future of applied AI—agree that the next frontier of AI maturity is literacy rooted in judgment. Training programs must now prepare employees not just to operate AI agents, but to question them, override them and escalate concerns when outputs conflict with human values, domain expertise or organizational risk. That concern is well founded: Organizations relying on unchecked automation face higher reputational and compliance risk, even when systems appear highly accurate. Similarly, confident but incorrect AI outputs—often called “hallucinations”—are becoming one of the biggest enterprise risks as generative AI scales. Against that backdrop, Senior Executive AI Think Tank members outline what effective AI literacy training must look like in practice—and why leaders must act now.

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The recent Disney–OpenAI partnership represents a turning point in the convergence of entertainment and artificial intelligence. By investing $1 billion in OpenAI and securing a three-year licensing deal for over 200 characters, Disney positions itself not only as a content powerhouse but as a first-mover in AI-driven storytelling, setting new competitive benchmarks for legacy media companies. This partnership also shines a light on the way generative AI is reshaping IP licensing, content production and audience engagement at scale. Jeff Katzenberg, former CEO of DreamWorks Animation, says AI could reduce the costs of creating an animated film by 90%, drastically changing the way creative works have historically been produced. So what does this mean for the future of storytelling in the media? And how can legacy media companies integrate frontier AI capabilities into content ecosystems without compromising IP, brand integrity or creative quality? Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—see the Disney–OpenAI alliance as a strategic signal that AI is moving from a peripheral tool to a core creative and operational engine. Below, they provide expert analysis and actionable strategies to help leaders navigate this rapidly evolving landscape.

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As major players like OpenAI, Google, Amazon and Anthropic continue to dominate AI infrastructure, smaller businesses and startups face a growing concern: how to compete in a landscape shaped by centralized compute, model development and vast resources. Major tech firms have invested billions in foundational models and own substantial portions of the infrastructure underlying generative AI. This can make it challenging for smaller companies to not only get off the ground, but get ahead. The Senior Executive AI Think Tank brings together seasoned experts in machine learning, generative AI and enterprise AI applications who believe that smaller firms can still win—in different ways. This article explores their insights on how startups should pivot from trying to match scale to leveraging agility, domain expertise and smarter infrastructure choices.

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The web is no longer just a destination—it’s becoming an intelligent partner. OpenAI’s introduction of Atlas, an “agentic browser” that can see, reason about and act directly on web pages, represents a paradigm shift in how people and organizations interact with information. Instead of manually searching, clicking and compiling data, users will soon be able to instruct AI to handle these tasks autonomously—transforming the browser from a viewing window into a dynamic workspace. The shift comes amid accelerating enterprise adoption of AI assistants. A 2025 report by Prialto found that 64% of executives believe AI has positively impacted their productivity. However, only 26% fully trust the AI tools they use, indicating a reliance on human oversight. Atlas promises to eliminate that friction by merging reasoning and execution directly within the browser. To understand how this evolution could redefine the digital workplace, we turned to the Senior Executive AI Think Tank—a curated group of leaders shaping machine learning, generative AI and enterprise AI adoption. Their insights reveal not just how Atlas may transform software expectations, but also how organizations can prepare for a world where browsers act as autonomous partners rather than passive tools.

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As enterprise AI adoption accelerates, so too does the complexity of choosing the right foundation. Should companies invest in proprietary platforms like GPT-4 or Claude, or build on open-source models such as Meta’s Llama or Mistral? The answer increasingly lies not in technical specs alone, but in how each option aligns with an organization’s cost structure, data governance needs and long-term innovation strategy. Recent research from McKinsey & Company underscores the growing momentum behind open systems: Over 50% of enterprises already report using open-source AI tools across their technology stack, and 76% expect to increase usage in the coming years. At the same time, proprietary platforms offer speed, reliability and white-glove scalability—often the shortest path to business impact. The trade-offs are real and consequential. To help executive decision-makers navigate these choices, we turned to members of the Senior Executive AI Think Tank—a group of enterprise AI, machine learning and innovation leaders who are shaping the way organizations operationalize artificial intelligence. In the sections below, they break down the pros and cons of each approach and offer actionable guidance on when to build, when to buy and how to orchestrate the right AI model strategy for your organization’s evolving needs.