Aditya Vikram Kashyap

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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.

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The web is no longer just a destination—it’s becoming an intelligent partner. OpenAI’s introduction of Atlas, an “agentic browser” that can see, reason about and act directly on web pages, represents a paradigm shift in how people and organizations interact with information. Instead of manually searching, clicking and compiling data, users will soon be able to instruct AI to handle these tasks autonomously—transforming the browser from a viewing window into a dynamic workspace. The shift comes amid accelerating enterprise adoption of AI assistants. A 2025 report by Prialto found that 64% of executives believe AI has positively impacted their productivity. However, only 26% fully trust the AI tools they use, indicating a reliance on human oversight. Atlas promises to eliminate that friction by merging reasoning and execution directly within the browser. To understand how this evolution could redefine the digital workplace, we turned to the Senior Executive AI Think Tank—a curated group of leaders shaping machine learning, generative AI and enterprise AI adoption. Their insights reveal not just how Atlas may transform software expectations, but also how organizations can prepare for a world where browsers act as autonomous partners rather than passive tools.

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As enterprise AI adoption accelerates, so too does the complexity of choosing the right foundation. Should companies invest in proprietary platforms like GPT-4 or Claude, or build on open-source models such as Meta’s Llama or Mistral? The answer increasingly lies not in technical specs alone, but in how each option aligns with an organization’s cost structure, data governance needs and long-term innovation strategy. Recent research from McKinsey & Company underscores the growing momentum behind open systems: Over 50% of enterprises already report using open-source AI tools across their technology stack, and 76% expect to increase usage in the coming years. At the same time, proprietary platforms offer speed, reliability and white-glove scalability—often the shortest path to business impact. The trade-offs are real and consequential. To help executive decision-makers navigate these choices, we turned to members of the Senior Executive AI Think Tank—a group of enterprise AI, machine learning and innovation leaders who are shaping the way organizations operationalize artificial intelligence. In the sections below, they break down the pros and cons of each approach and offer actionable guidance on when to build, when to buy and how to orchestrate the right AI model strategy for your organization’s evolving needs.

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AI‑native startups are scaling faster than ever—some hitting milestones that traditional SaaS firms took years to reach. But things are starting to move even faster. A recent analysis by Stripe suggests AI startups reach $1 million in revenue in about 11.5 months compared with 15 months for the earlier top SaaS models. That velocity comes not just from better algorithms but from a fundamentally different organizational posture. Meanwhile, many legacy firms are still navigating the early stages of adoption—pilots, governance debates, technical debt struggles—and too often fall short of meaningful impact. According to Boston Consulting Group, 74% of companies struggle to derive value from AI, with just 26% achieving scale beyond proof of concept. The Senior Executive AI Think Tank brings together leaders immersed in machine learning, generative AI and enterprise AI applications. Their collective wisdom reveals that competing with AI challengers demands more than tech upgrades—it requires deep structural and cultural shifts. In this article, they explore those shifts and offer actionable strategies for traditional organizations to close the gap.

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The recent release of TIME’s 2025 TIME100 AI list underscores how much attention is focused on foundation models, generative agents and consumer‑facing AI tools. Yet a closer look suggests that many powerful AI applications are still flying under the radar.  That’s where the Senior Executive AI Think Tank comes in—a curated group of experts in machine learning, generative AI and enterprise AI applications who combine technical depth with executive perspective. In this article, they use real-world insight to examine which industries and use cases are underrepresented in lists like TIME’s and explore the biggest AI frontiers that deserve attention now.