Chandrakanth Lekkala

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For many workers, learning artificial intelligence tools has quietly become “a second job”—one layered onto already full workloads, unclear expectations and rising anxiety about job security. Instead of freeing time and cognitive energy, AI initiatives often increase pressure, leaving employees feeling overworked or even disposable. A 2024 McKinsey report on generative AI adoption found that employees are more likely to experience burnout when AI tools are introduced without role redesign or workload reduction, even as productivity expectations rise. Similarly, a recent study from The Upwork Research Institute reveals that while 96% of execs expect AI to improve worker productivity, 77% of employees feel it’s only increased their workload (with an alarming 1 in 3 employees saying they will quit their jobs within the next six months due to burnout). Members of the Senior Executive AI Think Tank—a curated group of leaders in machine learning, generative AI and enterprise AI applications—note that this growing problem is not necessarily due to employee resistance or lack of technical ability, but how organizations sequence AI adoption, structure learning and communicate intent. Below, Think Tank members offer a clear roadmap for introducing AI as a system-level change—not an extracurricular obligation—to help ensure this technology empowers people rather than exhausts them.

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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 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.

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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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AI infrastructure spending has entered an era of historic scale. Microsoft, Google, Amazon and others have collectively committed hundreds of billions of dollars to expand compute capacity, even as analysts warn that parts of the market may be racing ahead of sustainable demand. For enterprise leaders outside Big Tech, the stakes are just as high, but the margin for error is far smaller. While AI investment continues to accelerate, many organizations struggle to connect infrastructure outlays to near-term financial returns, raising concerns about capital efficiency and long-term value creation. Members of the Senior Executive AI Think Tank—a curated group of executives and leaders shaping enterprise AI strategy—argue that the debate should not center on whether to invest, but how. What follows is a playbook drawn directly from their insights—detailing how seasoned leaders evaluate billion-dollar bets, stage risk intelligently and ensure AI infrastructure becomes a durable advantage rather than an expensive monument to hype.