Divya Parekh

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Internal AI assistants are quickly becoming the connective tissue of modern enterprises, answering employee questions, accelerating sales cycles and guiding operational decisions. Yet as adoption grows, a quiet risk is emerging: AI systems are only as reliable as the knowledge they consume. Members of the Senior Executive AI Think Tank—a curated group of leaders working at the forefront of enterprise AI—warn that many organizations are underestimating the complexity of managing proprietary knowledge at scale. While executives often focus on model selection or vendor strategy, accuracy failures more often stem from outdated documents, weak governance and unclear ownership of information. Research from MIT Sloan Management Review shows that generative AI tools often produce biased or inaccurate outputs because they rely on vast, unvetted datasets and that most responsible‑AI programs aren’t yet equipped to mitigate these risks—reinforcing the need for disciplined, enterprise level knowledge governance. As organizations move from experimentation to production, Think Tank members offer key strategies for rethinking how knowledge is curated, validated and secured—without institutionalizing misinformation at machine speed.

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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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As artificial intelligence advances at breakneck speed, the question of trust has become more urgent than ever. How do senior leaders ensure that innovation doesn’t outpace safety—and that every stakeholder, from customers to regulators and employees, retains confidence in rapidly evolving AI systems? Members of the Senior Executive AI Think Tank—a curated group of seasoned AI leaders and ethics experts—are confronting this challenge head-on. With backgrounds at Microsoft, Salesforce, Morgan Stanley and beyond, these executives are uniquely positioned to share practical, real-world strategies for building trust even in regulatory gray areas. And their insights come at a critical moment: A recent global study by KPMG found that only 46% of people worldwide are willing to trust AI systems, despite widespread adoption and optimism about AI’s benefits. That “trust gap” is more than just a perception issue—it’s a barrier to realizing AI’s full business potential. Against this backdrop, the Think Tank’s lessons are not theoretical, but actionable frameworks for leading organizations in a world where regulation lags, public concern mounts and the stakes for getting trust wrong have never been higher.

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Influence is now a core leadership skill. Award-winning authority builder Divya Parekh explains why executives need a visible voice and how to build authentic, trust-driven influence that strengthens credibility, attracts top talent, and drives meaningful impact.

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As enterprises scale their use of artificial intelligence, a subtle but potent risk is emerging: employees increasingly turning to external AI tools without oversight. According to a 2025 report by 1Password, around one in four employees is using unapproved AI technology at work. This kind of “shadow AI” challenges traditional governance, security and alignment frameworks. But should this kind of AI use be banned outright? Or can its use be harnessed to spur innovation and encourage creativity and experimentation? The Senior Executive AI Think Tank—a curated group of senior leaders specializing in machine learning, generative AI and enterprise AI applications—has pooled its collective wisdom to help organizations transform unmanaged AI usage from a hidden threat into a structured lever of innovation, enhancing speed, agility and enterprise alignment.

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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.