Venkata Kondepati

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For many executives, AI has already become part of the daily workflow. They use it to summarize reports, find information, draft emails and work through routine tasks. But familiarity with the tools can create a false sense of fluency—and make it harder to recognize the gap between using AI and knowing how to use it strategically.Microsoft research involving thousands of knowledge workers found that generative AI can reduce time spent on tasks such as writing, information retrieval and summarization. But those individual productivity gains do not automatically translate into changes in how organizations make decisions or operate. Research from McKinsey similarly finds that while AI adoption is widespread, most organizations are still struggling to turn it into significant enterprise-level impact.The challenge, then, is not simply getting leaders to use AI more. It is learning to recognize where AI can change the work itself—and developing the judgment to know when, where and how to make that change.So what does genuine AI fluency look like at the leadership level? Members of the Senior Executive AI Think Tank approach that question from different vantage points, spanning enterprise technology, research, finance, retail, design and AI implementation. Their experiences offer a closer look at what happens when leaders move beyond individual productivity and begin applying AI to decisions, workflows, business systems and organizational strategy.

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For all the talk about AI making customer service faster and more personal, there is still a harder question to answer: Does the customer actually feel better served? An instant answer or perfectly timed recommendation may be useful, but usefulness is not the same as feeling understood. When a customer has a complicated problem, is frustrated or simply wants to talk to someone, efficiency can quickly become the problem.That tension is becoming more important as companies bring AI into more customer interactions. Customers are increasingly comfortable with AI when it saves time or effort, but many still expect a human option when the stakes are higher. The challenge then is figuring out how technology can remove friction without taking away the judgment, reassurance and connection that make good service feel like good service.In this article, members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI and enterprise AI applications—explore that challenge from different angles. They discuss personalization and customer memory, where human involvement matters most, how AI can give employees more room for empathy, and how customer signals can shape better products. They also examine what it really means for AI to remember—and how leaders can tell whether an interaction leaves customers feeling understood, rather than simply processed.

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Artificial intelligence may be transforming the enterprise, but behind every successful AI initiative is something far less glamorous: disciplined data engineering.As organizations race to deploy generative AI, agentic systems and increasingly sophisticated analytics, many are pouring resources into new models, cloud platforms and AI applications. Yet time and again, ambitious projects fail to deliver meaningful business value—not because the technology falls short, but because the underlying data is inconsistent, poorly governed or difficult to trust.According to McKinsey's latest State of AI research, organizations seeing the strongest returns from AI distinguish themselves not by the models they choose, but by the maturity of the data, governance and operating foundations supporting those models. In other words, AI success begins long before a prompt is entered or an algorithm is deployed.Members of the Senior Executive AI Think Tank, a curated community of executives specializing in machine learning, generative AI and enterprise AI applications, have witnessed this firsthand across industries ranging from healthcare and financial services to manufacturing, retail and cloud computing. Below, they outline the foundational data engineering capabilities they believe consistently deliver the greatest business value and why leaders should take more notice.

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More than 200 economists, AI researchers and technology leaders recently signed the "We Must Act Now" statement, warning that artificial intelligence could reshape the global economy faster than the Industrial Revolution. While the statement calls for stronger institutions and guardrails, it also raises a more immediate question for executives: What should leaders actually do today?Members of the Senior Executive AI Think Tank believe the answer extends well beyond adopting the latest AI tools. Drawing on decades of experience leading enterprise AI, cloud infrastructure, digital transformation and technology strategy, these experts argue that organizations must rethink work itself—redesigning processes, investing in people and establishing governance before AI scales across the enterprise.Their advice comes as organizations accelerate adoption. According to McKinsey's latest State of AI research, AI adoption continues to expand rapidly across industries, with organizations increasingly reporting measurable business impact. Yet the same research also highlights persistent challenges around governance, workforce readiness and risk management, reinforcing the need for thoughtful leadership rather than technology-first decision-making.The following insights from members of the Senior Executive AI Think Tank reveal a consistent message: Organizations that treat AI as a catalyst for better decision-making, stronger employees and more resilient businesses—not simply a cost-cutting exercise—will be best positioned to thrive as AI transforms the economy.

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For years, enterprise technology leaders have wrestled with a familiar dilemma: Embrace the speed and innovation of a vendor platform or invest in building enough internal capability to maintain strategic control. Generative AI has made that trade-off far more consequential. As organizations move beyond chatbots to autonomous agents that retrieve information, invoke tools and make decisions across business systems, the focus is increasingly on who controls the pathways connecting models, knowledge, applications and enterprise data.That challenge is driving renewed interest in customer-owned AI control planes—enterprise-managed gateways that sit between AI applications and the rapidly expanding ecosystem of models, Model Context Protocol (MCP) servers, agent hubs and knowledge sources. Rather than relying entirely on vendor-specific ecosystems, these architectures promise centralized governance, stronger security, greater architectural flexibility and the ability to adopt new AI capabilities without redesigning the entire technology stack. Yet they also introduce an important question: Does adding another layer simplify enterprise AI or simply shift complexity from vendors to internal engineering teams?Members of the Senior Executive AI Think Tank, a community of leaders shaping enterprise AI strategy across architecture, governance, cloud computing and digital transformation, largely agree that customer-owned control planes represent an important evolution—but only if organizations approach them with discipline. Below, they discuss why centralized gateways can help organizations reduce vendor lock-in without slowing innovation, what security and architecture teams need to see before they'll trust agentic AI at scale and why governance should be built into every model and tool interaction rather than bolted on later.

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Artificial intelligence is rapidly redefining the cybersecurity battlefield, shifting the balance between defenders and attackers at a pace many organizations are struggling to match. As enterprises embed generative AI, autonomous agents and machine learning into critical workflows, the attack surface is expanding just as quickly as defensive capabilities evolve.This tension is at the center of discussion among members of the Senior Executive AI Think Tank, a curated group of leaders specializing in enterprise AI, machine learning and responsible AI deployment. To them, AI is not just a technology upgrade—it is a structural shift in how cyber risk is created and managed.According to the National Institute of Standards and Technology’s AI Risk Management Framework, organizations adopting AI face heightened risks related to system reliability, security vulnerabilities and adversarial manipulation, even as they gain powerful new defensive tools. At the same time, a Google Threat Intelligence Group analysis on AI-enabled threat activity warns that adversaries are increasingly using generative AI to accelerate vulnerability discovery, exploit development and initial access—signaling a shift toward more automated and scalable cyber intrusion models.With this knowledge, senior executives are asking a pressing question: Over the next five years, should we be more optimistic about AI’s role in cybersecurity—or more concerned? And more importantly, what concrete actions should leaders take today to stay ahead of the curve?Their insights suggest the answer is not binary—but it is urgent.

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The current AI conversation has been dominated by software. Organizations have raced to deploy chatbots, copilots and generative AI tools that promise to boost productivity, improve decision-making and automate knowledge work. But what happens when AI leaves the screen and enters the physical world?That future is already taking shape. AI-powered robots are moving beyond controlled factory environments and into warehouses, hospitals, retail operations and even homes. Companies including Amazon, Tesla and Figure AI are investing billions in autonomous systems capable of navigating complex environments, collaborating with humans and performing tasks that once required manual labor. At the same time, labor shortages, rising operating costs and demographic shifts are creating strong economic incentives for automation. According to the International Federation of Robotics, global demand for industrial robots has more than doubled over the past decade, with more than 4.6 million robots now operating in factories worldwide.Yet despite the excitement, fundamental questions remain unanswered: What milestone will signal that AI-powered robotics has evolved from a promising technology into a mainstream commercial reality? Will it be a breakthrough in capability? A dramatic reduction in cost? Regulatory approval? Or something less obvious?To explore these questions, we turned to members of the Senior Executive AI Think Tank, a curated group of leaders and practitioners specializing in machine learning, generative AI and enterprise AI applications. Below, they share the signals they believe executives should be watching and the conditions that will determine when AI-powered robotics truly crosses into the mainstream.

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Artificial intelligence is often framed as a race: faster models, bigger investments, larger datasets and more powerful infrastructure. But beneath the headlines lies a more consequential question for business leaders, policymakers and investors alike: Who gets to compete?A growing share of the AI ecosystem is controlled by a relatively small number of organizations with access to the world's largest compute resources, proprietary datasets and distribution channels. This means the debate is no longer simply about what AI can do but about whether the next wave of innovation will emerge from an open marketplace of ideas or from a handful of dominant ecosystems.To explore that question, we asked members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI, digital transformation and enterprise AI applications—what single rule they would change to improve AI competition.While their recommendations differ, a clear theme emerges: The future of AI should be shaped by innovation, trust and customer value rather than lock-in, opacity or concentrated control. The following insights offer a timely look at how technology and business leaders believe a more competitive—and in many cases safer—AI ecosystem can be built.

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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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As generative AI reshapes industries from media and marketing to software development and healthcare, one question is becoming impossible for enterprises, policymakers and technology providers to ignore: Who should benefit when AI systems are trained on human-created content?That debate has intensified as courts and regulators scrutinize how AI models are built, how synthetic media is distributed and whether creators deserve compensation when their work contributes to commercial AI products. Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—say the future of AI depends on building sustainable systems that balance innovation with accountability, transparency and trust.Lawsuits and copyright disputes over AI training data have accelerated globally, while companies such as Adobe continue advocating for licensed datasets and provenance frameworks designed to verify content authenticity. At the same time, enterprise adoption of generative AI continues to surge, with a McKinsey study on the state of AI finding that organizations are rapidly increasing investments in generative AI initiatives despite ongoing governance concerns.The challenge now facing the industry is not simply whether AI companies should compensate creators, but how to build systems that make compensation, transparency and innovation sustainable at scale. Below, Think Tank members outline what that future could look like—from collective licensing models and provenance standards to creator opt-in frameworks, enterprise governance strategies and new approaches to trust in the age of generative AI.