{"id":23687,"date":"2025-04-28T10:23:51","date_gmt":"2025-04-28T14:23:51","guid":{"rendered":"https:\/\/www.insightpartners.com\/?p=23687"},"modified":"2025-04-28T10:23:51","modified_gmt":"2025-04-28T14:23:51","slug":"enterprise-ai-inflection","status":"publish","type":"post","link":"https:\/\/www.insightpartners.com\/ideas\/enterprise-ai-inflection\/","title":{"rendered":"Why enterprise AI is at an inflection point: Lessons from Google\u2019s Jason Gelman"},"content":{"rendered":"<p>The enterprise AI landscape is evolving fast. Generative models are getting cheaper, more powerful, and easier to deploy \u2014 but trust, reliability, and real-world utility are becoming harder to maintain. As AI shifts from experimentation to infrastructure, the companies that succeed will be the ones that treat it not as a shiny feature but as a system.<\/p>\n<p><a href=\"https:\/\/www.linkedin.com\/in\/jasonlgelman\/\">Jason Gelman<\/a>, who leads product at Google Cloud for Vertex AI, has been deep in that system for years. In a conversation with LinkedIn\u2019s <a href=\"https:\/\/www.linkedin.com\/in\/tanyadua\/\">Tanya Dua<\/a>, he shared where the market is heading \u2014 and what enterprises (and builders) need to do to keep up.<\/p>\n<h2>Key takeaways<\/h2>\n<ul data-start=\"114\" data-end=\"646\">\n<li data-start=\"114\" data-end=\"259\">\n<p data-start=\"116\" data-end=\"259\">AI is moving from experimentation to infrastructure, and companies that focus on reliability, integration, and observability will pull ahead.<\/p>\n<\/li>\n<li data-start=\"260\" data-end=\"387\">\n<p data-start=\"262\" data-end=\"387\">Stateless, API-native AI architectures are lowering barriers to adoption by eliminating the need for full cloud migrations.<\/p>\n<\/li>\n<li data-start=\"388\" data-end=\"522\">\n<p data-start=\"390\" data-end=\"522\">The strongest AI use cases are tightly focused, solving specific, high-value problems rather than aiming for broad generalization.<\/p>\n<\/li>\n<li data-start=\"523\" data-end=\"646\">\n<p data-start=\"525\" data-end=\"646\">In a fragmented model landscape, trust, cost-efficiency, and real-world performance are becoming the key differentiators.<\/p>\n<\/li>\n<\/ul>\n<p>These insights came from our\u00a0<a href=\"https:\/\/scaleup.events\/\">ScaleUp:AI<\/a>\u00a0event in November 2024, an industry-leading global conference that features topics across technologies and industries. Watch the full session below:<\/p>\n<p><iframe loading=\"lazy\" title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/Rd4Dgpac1bc?si=uWXs1V2oJiP6SHfc\" width=\"800\" height=\"450\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<h2>The shift from experimentation to orchestration<\/h2>\n<p>\u201cBefore OpenAI had released any models, before Microsoft was using them at scale, and before AWS started hosting LLMs \u2014 we had the whole stack in place,\u201d Gelman noted. That stack included Google\u2019s TPUs, custom silicon they\u2019ve been developing for over a decade, as well as early internal deployments that helped shape what eventually became Vertex AI.<\/p>\n<p>The message is clear: Infrastructure still matters. While much of the AI conversation focuses on model outputs and applications, Gelman emphasized that success in this space is increasingly defined by integration, reliability, and scale. \u201cWe\u2019ve really battle-tested that part of the stack in a way nobody else did,\u201d he added.<\/p>\n<p>That depth gives Google a unique position, but more broadly, it underscores a key shift in the AI adoption curve: companies are moving from stitching tools together to demanding cohesion, observability, and accountability from day one.<\/p>\n<h2>\u201cCloud and AI may be even a little bit more divorced than they used to be&#8221;<\/h2>\n<p>One of the most paradigm-shifting insights Gelman shared was how the relationship between AI and cloud has changed. \u201cCloud and AI may be even a little bit more divorced than they used to be,\u201d he said. \u201cNow you just use an API\u2026and that API is stateless. You don\u2019t have to move your data to the cloud.\u201d<\/p>\n<p>This is a major unlock for companies, especially mid-sized enterprises or data-sensitive sectors, that haven\u2019t completed (or don\u2019t want to begin) massive cloud migrations. With stateless, API-first architectures, AI adoption no longer requires a full replatforming effort.<\/p>\n<p>This change lowers the activation energy for innovation. Enterprises can now build with AI in a modular way, targeting specific problems, without uprooting existing infrastructure.<\/p>\n<h2>Why the best use cases are often the most focused<\/h2>\n<p>When asked about standout enterprise applications, Gelman pointed to Snapchat\u2019s deployment of Gemini in its My AI chatbot. \u201cTheir user base tends to skew younger\u2026 and we saw much higher engagement once there was a real purpose for the chatbot,\u201d he explained. In this case, the purpose was homework help\u2014a use case tailored to the audience and grounded in real utility.<\/p>\n<p>On the other end of the spectrum, Mayo Clinic is using Vertex AI to process petabytes of medical research, helping oncologists and other specialists keep pace with fast-moving scientific developments. \u201cI have a family member who&#8217;s an oncologist, and he says his team of doctors can&#8217;t figure out a care plan that really is up to date with the latest science just from memory anymore. There are too many papers coming out,\u201d Gelman said. \u201cOur models help process that sheer amount of data.\u201d<\/p>\n<p>In both cases, the common thread is focus. These applications aren\u2019t trying to do everything \u2014 they\u2019re solving high-friction problems in high-value contexts.<\/p>\n<h2>Flexibility is powerful, but model overload is real<\/h2>\n<p>One of Vertex AI\u2019s key selling points is its model-agnostic architecture. Enterprises can choose from Google\u2019s first-party Gemini models, open-source options like Llama, or partner models from Anthropic and others. But that flexibility creates a new challenge: How to decide?<\/p>\n<p>To solve this, Google is rolling out tools that help enterprises evaluate models not just based on benchmarks, but on real-world performance for their use cases.<\/p>\n<p>These tools are increasingly critical. As the market fragments, performance parity across similar-sized models will become more common. Differentiation will depend on understanding what works best where, and being able to measure that clearly.<\/p>\n<h2>The agent era: long-term potential, short-term caution<\/h2>\n<p>A<a href=\"https:\/\/www.insightpartners.com\/ideas\/state-of-the-ai-agent-ecosystem-use-cases-and-learnings-for-technology-builders-and-buyers\/\">gentic AI<\/a> \u2014 autonomous systems that can take action on your behalf \u2014 is the hottest topic in the space. But Gelman didn\u2019t hesitate to challenge the buzz. \u201cOverhyped,\u201d he said plainly, referring to the popular vision of an AI agent that books flights or manages your calendar. \u201cThat scenario\u2026we\u2019re not there yet.\u201d<\/p>\n<p>He explained that while the ambition is sound, the infrastructure is not. Agents need persistent context, task planning abilities, and robust observability frameworks. \u201cThe key to agents is really having a task and being able to break that task into planning steps,\u201d he said. \u201cBut right now, those models are still a frozen piece of code.\u201d<\/p>\n<p>That doesn\u2019t mean Google isn\u2019t investing. \u201cWe\u2019re working on both the infrastructure for the guardrails and the monitoring, and also on the research around model planning,\u201d he added. However, for now, enterprises are better off building robust workflows around narrow automation than relying entirely on general-purpose agents.<\/p>\n<h2>Cost, hallucinations, and trust<\/h2>\n<p>While much attention is paid to what AI can do, Gelman highlighted the real-world constraints that still frustrate customers. \u201cWe\u2019ve brought [the cost of AI] down by something like 92% this year,\u201d he said. Still, for some use cases, cost remains a blocker. For others, accuracy is the top concern.<\/p>\n<p>He was candid about hallucinations, too: \u201cHallucination is probably a technical shortcoming more than a pure ethical concern.\u201d While accuracy is improving, no model is perfect. The takeaway? Responsible deployment requires acknowledging fallibility and designing workflows that accommodate it.<\/p>\n<h2>Small models, multimodal systems, and introspective AI<\/h2>\n<p>As for what\u2019s next, Gelman sees two clear trends: Small models getting dramatically better, and large models becoming more reflective. \u201cSmaller models today are doing as well as state-of-the-art models were 12 to 18 months ago,\u201d he said. These models are faster and cheaper, and in many cases, good enough.<\/p>\n<p>At the same time, Google is exploring \u201cruntime inference introspection\u201d \u2014 models that pause, review, and refine their own outputs. That ability to reason, reflect, and course-correct may unlock a new generation of enterprise applications, especially when paired with multimodal inputs.<\/p>\n<p>Tools like Notebook LM are early signs of that future. \u201cIt asked questions I didn\u2019t even know I was supposed to ask,\u201d Gelman said of his own experience using it.<\/p>\n<h2>\u201cEnterprise readiness is the floor, not the ceiling&#8221;<\/h2>\n<p>Gelman ended with a reminder that often gets lost in the excitement: \u201cGoogle Cloud is different than Google Consumer,\u201d he said. \u201cWe don\u2019t see your prompts. We don\u2019t use your data to train our models.\u201d<\/p>\n<p>That clear line between enterprise and consumer AI, combined with encryption, stateless design, and global compliance, isn\u2019t just policy. It\u2019s architecture.<\/p>\n<p>The companies that lead in this next chapter won\u2019t just ship fast. They\u2019ll ship responsibly. Because as Gelman put it, \u201cEnterprise readiness is the floor, not the ceiling.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The enterprise AI landscape is evolving fast. Generative models are getting cheaper, more powerful, and easier to deploy \u2014 but trust, reliability, and real-world utility are becoming harder to maintain. As AI shifts from experimentation to infrastructure, the companies that succeed will be the ones that treat it not as a shiny feature but as [&hellip;]<\/p>\n","protected":false},"author":36,"featured_media":23689,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[141,21,35],"tags":[],"class_list":["post-23687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scaleup-ai","category-article","category-post-format-video","domain-ai-ml"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.1 (Yoast SEO v23.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Why enterprise AI is at an inflection point: Lessons from Google\u2019s Jason Gelman | Insight Partners<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.insightpartners.com\/ideas\/enterprise-ai-inflection\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why enterprise AI is at an inflection point: Lessons from Google\u2019s Jason Gelman\" \/>\n<meta property=\"og:description\" content=\"The enterprise AI landscape is evolving fast. 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