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About
Divya Parekh, a Thinkers50-recognized leadership coach and AI adoption advisor, is a strategic founder and executive partner who helps leaders and organizations build future-ready performance in an AI-accelerated world. She blends executive coaching, leadership development, and practical AI integration to increase decision velocity, strengthen execution, and create cultures built on clarity and accountability. Her work bridges strategy and psychology with real-world systems leaders can actually use, turning complexity into focused action and measurable results.
Divya Parekh
Published content
expert panel
Artificial intelligence has become the fastest-moving investment category in the corporate world. Boards are asking about it, investors expect it and competitors are announcing new initiatives seemingly every week. For many Fortune 500 CEOs, however, the challenge isn't deciding whether to invest in AI—it's deciding where to place the first major bet.The stakes are high because the wrong investment can consume millions of dollars while delivering little business value. Organizations across industries are launching AI labs, experimenting with custom models and deploying new tools at scale, yet many still struggle to achieve measurable returns.That reality raises an important question: If you were making your first significant AI investment today, where would you focus—and what would you avoid?To find out, we asked members of the Senior Executive AI Think Tank, a community of leaders and practitioners specializing in machine learning, generative AI and enterprise transformation. Their answers reveal a striking consensus about where AI creates value, why so many organizations get their priorities wrong and the foundational investments that should come before any large-scale AI deployment.
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For many organizations, AI training has become synonymous with productivity. Employees learn how to write better prompts, automate routine tasks and generate content faster than ever before. But as AI becomes embedded in everyday business decisions, a more important question is emerging: Are organizations teaching people how to use AI, or how to use it responsibly?AI can generate recommendations, summarize information and accelerate workflows, but it cannot assume accountability for outcomes. That responsibility still belongs to people. Yet many training programs spend far more time on tools than on judgment, ethics, governance and critical thinking.This concern is reflected in Deloitte's “The State of Generative AI in the Enterprise” research, which found that regulatory compliance concerns, risk management challenges and the lack of governance models rank among the leading barriers to scaling AI initiatives. As organizations move beyond experimentation, the challenge is no longer simply getting employees to use AI—it is ensuring they can use it responsibly.To explore what modern AI fluency should look like, we turned to members of the Senior Executive AI Think Tank, a curated community of experts in machine learning, generative AI and enterprise transformation. Their perspectives offer a roadmap for moving beyond AI tool proficiency and building the judgment, oversight and responsible-use practices that enable organizations to create lasting value from AI.
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The race to make AI indispensable in everyday life may have found its most compelling use case: health. As Google expands Gemini-powered health coaching capabilities and AI becomes increasingly embedded in wearables, smartphones and wellness platforms, the prospect of a 24/7 personalized health assistant is moving from science fiction to consumer reality.Members of the Senior Executive AI Think Tank believe AI health assistants possess characteristics few other AI applications can match: continuous engagement, highly personal relevance and the ability to influence daily behavior. Their optimism, however, comes with significant caveats.According to a Nature Digital Medicine analysis of large language models in healthcare, AI systems are advancing rapidly across clinical and consumer health applications, but researchers argue that stronger oversight, transparency and governance are necessary to ensure safe and responsible deployment.Think Tank members largely agree that AI health assistants have the potential to become the first truly mainstream consumer AI product, but they also emphasize that widespread adoption will depend on getting the safeguards right. Their insights reveal where the greatest opportunities lie, where the biggest risks remain and what organizations must do to build systems worthy of users' trust.
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As multimodal AI moves rapidly from novelty to baseline expectation, companies are confronting a deeper challenge than simply adding new features. Users increasingly expect software to understand text, voice, images and video simultaneously, while preserving context seamlessly across every interaction. That shift is forcing organizations to rethink how products are designed, architected and differentiated.Members of the Senior Executive AI Think Tank say the next era of product competition will center less on standalone AI capabilities and more on orchestration, workflow intelligence and trust. Their insights arrive as major technology companies race to integrate multimodal capabilities into mainstream applications. Multimodal systems capable of understanding and generating across formats are becoming foundational to enterprise software strategy. At the same time, organizations are discovering that simply embedding AI into existing workflows does not automatically create better user experiences.Instead, experts argue, multimodal AI is changing the very definition of interface design. Products are evolving from static tools into adaptive systems that anticipate intent, reduce friction and collaborate more naturally with users. The insights that follow explore why multimodal AI is forcing companies to rethink everything from UX design and workflow orchestration to trust, memory and product differentiation—and what leaders must do now to stay competitive.
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The nature of teamwork is undergoing one of the most significant transformations since the rise of the digital workplace. As artificial intelligence moves from a supporting tool to an embedded collaborator, organizations are rethinking not only how work gets done, but what collaboration truly means. A widely cited report from McKinsey highlights that generative AI could automate up to 30 percent of hours worked across the U.S. economy by 2030, fundamentally reshaping roles and workflows. But this shift is not simply about efficiency—it is about redefining the human role within teams. Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in machine learning, generative AI and enterprise applications—believe teams will not necessarily disappear, but will instead evolve into hybrid ecosystems where human judgment, creativity and ethical oversight intersect with AI-driven speed, scale and synthesis. The following insights explore how that evolution will unfold—and what leaders must do to stay ahead.
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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.
Company details
THE DP GROUP, LLC
Company bio
The DP Group is a leadership and management consulting firm that helps executives and organizations build AI-ready performance systems while protecting the human core that drives results. We partner with CEOs and senior leaders to reduce noise, accelerate decision-making, and embed practical AI into everyday execution, from communication and planning to performance management and strategic delivery. The work is both strategic and deeply human: we strengthen clarity, accountability, and culture so leaders can drive outcomes without burning out their people or eroding trust. The result is measurable and felt: faster execution, sharper priorities, stronger leadership presence, and teams that can move with confidence in an AI-accelerated world.