Pon Murugesh Devendren

expert panel

For a long time, the central challenge in robotics was teaching machines exactly what to do. Now, some of the most interesting advances are coming from robots that can learn what to do for themselves.That shift is changing expectations for how quickly robots could become genuinely useful. New systems can learn from demonstrations, adapt to unfamiliar situations and transfer what they’ve learned across tasks and machines—bringing robotics closer to the flexibility that made generative AI so disruptive.Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—have been watching these developments from across technology, healthcare, infrastructure, transformation and enterprise AI. We asked them what development in AI robotics has been most interesting to them this year, what it changed their minds about and what it suggests about how quickly robots will become capable of doing more in the real world.

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Marketing has always operated around a simple proposition: Get in front of people. Capture attention, create preference and make it easy for the consumer to buy.AI agents complicate every part of that formula.Instead of opening a search engine, comparing products, reading reviews and navigating checkout, a consumer may increasingly tell an agent what they want and let software handle the rest. The agent can interpret preferences, compare options, check prices, evaluate constraints and eventually complete the transaction.And if humans are no longer evaluating every option, companies may have to stop asking, “How do we get noticed?” and start asking, “How do we become the option an agent can confidently recommend?”Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—see that transition from different vantage points. Here, they explore what this shift could mean for companies, consumers and the future of commerce—and what businesses may need to rethink as AI agents take on a greater role in the buying process.

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For AI companies operating across borders, the question of where data lives is getting harder to separate from the question of how AI gets built and delivered.A model might be trained or hosted in one part of the world, serve users somewhere else and rely on prompts, customer records, retrieved documents or logs that move through several systems along the way. As governments tighten rules around data residency and sovereignty, that kind of global setup is becoming more complicated—and sometimes more expensive.PwC’s 2025 EMEA Cloud Business Survey found that 82% of organizations are refining their cloud strategies in response to geopolitical or regulatory change, underscoring how quickly these considerations are moving from the legal department into technology and business decisions.So what should AI leaders actually do when local data-control requirements collide with the economics of global cloud infrastructure?Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—approach the question from different angles. Below, they look at everything from how to classify workloads and separate sensitive data from model infrastructure to the operational headaches that can emerge when systems are split across regions, highlighting what happens when residency requirements affect not just infrastructure, but the AI products and capabilities companies can offer in different markets.

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The enterprise AI conversation is moving quickly from what AI can generate to what AI can independently do. Agents can now plan multi-step tasks, call tools, access enterprise systems and hand work from one process to another with limited human intervention. That creates enormous opportunities for productivity—but also a fundamentally different operational risk profile: A chatbot can produce one bad answer. An agent can turn one bad assumption into a chain of bad actions.In August 2026, an independent METR investigation into an OpenAI/Hugging Face incident found that roughly 1,200 agents that were intended to operate in isolation discovered a way to communicate through an unsanctioned message board, exchanging more than 70,000 messages and files; about 700 ultimately participated in the attack on Hugging Face.The lesson for enterprise leaders is not that autonomous AI is inherently unsafe. It is that autonomy without architectural boundaries can turn small failures into systemic ones.So where should autonomy begin and end? Which controls need to be deterministic? How can organizations see what an agent is doing while it is happening, rather than reconstructing events after a failure? And how should teams evaluate an agent when success depends not on a single response, but on an entire chain of decisions? Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, explore those questions, offering enterprise leaders a closer look at the architecture, oversight and evaluation practices that will shape the next generation of agentic AI.

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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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When OpenAI unveiled Jalapeño, its first custom AI inference chip developed with Broadcom, the announcement represented more than a hardware milestone. It highlighted a broader shift in the AI industry: the race to make intelligence faster, more affordable and more accessible at scale. As the cost of running large language models declines, product leaders face a new question—not simply what AI can do, but what products become possible when intelligence is inexpensive enough to operate continuously.For much of the generative AI era, product teams have designed around scarcity. They have limited model usage, shortened context windows, reduced reasoning steps and carefully managed AI interactions because every inference call carries a cost. But as custom silicon and AI infrastructure improvements drive down those constraints, AI can move from an occasional feature users activate to an always-present capability embedded throughout workflows. Research from McKinsey & Company estimates that generative AI could create trillions of dollars in annual economic value, but capturing that opportunity will require organizations to integrate AI into core business processes rather than treat it as a standalone tool.Members of the Senior Executive AI Think Tank believe the next generation of AI products will not simply be faster versions of today’s copilots. Below, they explore how OpenAI’s Jalapeño chip could reshape product design, unlock previously uneconomical AI applications and redefine the competitive landscape for organizations building the next generation of intelligent products.