See how leaders are turning AI into real outcomes at Snowflake Accelerate <\/p>\n
An asset manager, an ER doctor, a city budget director, a retail executive, a music label VP and a plant operations leader don\u2019t have much in common.<\/p>\n
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Until you look at their data and AI priorities.<\/p>\n
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Across financial services, healthcare, media, manufacturing, retail and government, the same three challenges keep showing up: turning AI into real operations, fixing the data foundation and building trust into systems that act autonomously.<\/p>\n
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Snowflake\u2019s Data Trends 2026 report captures that convergence. At Snowflake Accelerate, leaders across those industries will show how they\u2019re solving these challenges \u2014 through real systems, real data and production use cases. Here\u2019s a preview of the trends they\u2019re noticing.<\/p>\n
\nTrend 1: Agentic AI is no longer an experiment
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It seems that the window of experimental AI is closing, and across every industry the shift is the same: AI used to only answer questions, but now AI agents take action \u2014 continuously, autonomously and often operating at a scale beyond what most teams can manage manually.<\/p>\n
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In retail, 58% of companies are actively deploying AI<\/a>. Their consumers are already shopping online through agents that have never visited a brand\u2019s website. GEO and AEO are replacing keyword SEO, and it\u2019s happening now, not eventually. At Accelerate Retail & Consumer Goods, Shalion\u2019s chief data officer shows in real time how its AI monitors more than 2,000 retailers across more than 50 countries for Pepsi, Heineken and Lego \u2014 and how a Snowflake Cortex AI skill enriching 5,000 product SKUs around the clock was built in a single day. If you want to see what agentic commerce looks like in production, this is the session.<\/p>\n
<\/p>\n Register for A<\/a>ccelerate Retail & Consumer Goods<\/a> on April 22.\u00a0<\/p>\n
<\/p>\n In manufacturing, the stakes go beyond efficiency. With more than 10,000 Americans retiring per day, leading to 1.4 million skilled trade jobs being potentially unfilled by 2030, every retiring engineer takes institutional knowledge with them that no job posting can replace. WolfSpeed\u2019s answer to this problem includes 12 Snowflake Intelligence agents that have turned years of archived shift comments into a live, queryable knowledge base, so questions that once took weeks to answer now take seconds. At Accelerate Manufacturing, WolfSpeed will also demo a graph neural net that traces defects back through production in real time.\u00a0<\/p>\n
<\/p>\n Register for Accelerate Manufacturing<\/a> on April 29.\u00a0<\/p>\n
<\/p>\n The pattern holds everywhere: 63% of healthcare and life sciences organizations<\/a> are already experimenting with or actively deploying agentic AI. In government, Virginia State Police Chief Data Officer Steve McLaughlin described how finding a single line item on a purchase order \u2014 a task that once took minutes to hours \u2014 now takes 25 seconds. In financial services, BlackRock and S&P Global are deploying coordinated teams of AI agents across portfolio management, risk analytics and quantitative analysis. In some cases, organizations report collapsing workflows that once took days into mere moments.<\/p>\n
<\/p>\n The full picture \u2014 including what separates the 58% of companies deploying AI from the 42% that are still planning \u2014 is in the Data Trends 2026 Retail & Consumer Goods and Manufacturing ebooks.\u00a0<\/p>\n
<\/p>\n Download the ebooks to learn more:<\/p>\n
Every industry faces an inconvenient truth. We know how important agents are, but it turns out the challenge is centered somewhere else: the data.<\/p>\n
<\/p>\n For example, in healthcare, the median patient chart contains 46,000 words. The top end exceeds 1 million words, the length of the entire Harry Potter series. An ER doctor with 24 patients in the queue cannot read those charts. An AI agent can, but only if the underlying data is trusted and accessible. That \u201cif\u201d is where most AI initiatives stall. Innovalon is building agentic prior authorization workflows on Snowflake that reduce 200-page chart reviews from weeks to minutes, end to end, without a human in the middle. Dr. Paige Killian will walk through what that actually means for care delivery.<\/p>\n
<\/p>\n Register for Accelerate Healthcare & Life Sciences<\/a> on April 30.\u00a0<\/p>\n
<\/p>\n The same story plays out sector by sector. WolfSpeed\u2019s agents couldn\u2019t run until IT, OT and Internet of Things data was unified. The City of Nashville started with a single data set that logs 311 calls, but one integration gave 12 city departments visibility they\u2019d never shared before. Francis Crick Institute<\/a>, Europe\u2019s largest discovery life sciences research institute, built the Trusted Research Environment, connecting rare disease data sets across 38 countries, data that had never been safely shareable before.<\/p>\n
<\/p>\n As Mike Wheeler, Global Industry Advisor, Life Sciences at SAP put it: \u201cData itself is going to be the bottleneck to trusted AI. If you can\u2019t trust the data, the model is useless from the get-go.\u201d<\/p>\n
<\/p>\n The Healthcare and Life Sciences and Public Sector Data Trends 2026 reports go even deeper on what breaking down silos actually requires in regulated industries \u2014 including where the real barriers are and what the leaders who\u2019ve cleared them learned.\u00a0<\/p>\n
<\/p>\n Download the ebooks to learn more:<\/p>\n
Once agents act on data autonomously, the question becomes: How do you trust what they do at scale, across tools, across teams?<\/p>\n
<\/p>\n The answer shows up across industries: Governance is built into the data layer in a way that can\u2019t be bolted on afterward.<\/p>\n
<\/p>\n At Accelerate, Snowflake is announcing the\u00a0Open Semantic Interchange (OSI)<\/a> \u2014 a new open standard for machine-readable semantic context, co-founded with BlackRock, S&P Global, dbt Labs, Sigma and Hex. BlackRock\u2019s Dave Woodhead will explain what OSI can change: For instance, every Aladdin client had been independently rebuilding the same semantic model Aladdin had already built. OSI ends that. Time to value can be reduced from months to minutes in certain implementations, depending on use case and data readiness.<\/p>\n
<\/p>\n Register for Accelerate Financial Services<\/a> on April 28.\u00a0<\/p>\n
<\/p>\n In media and entertainment, governance looks different but follows the same logic. Warner Music Group runs quarterly AI hackathon days. It pairs engineers with legal, marketing and label teams, cataloging the wins before scaling. Viral Nation bakes brand safety into creator discovery at 180,000-creator scale. The advertising, media and entertainment session is where you\u2019ll see what governance as competitive advantage actually looks like in practice.<\/p>\n
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\nTrend 2: The data foundation is still the bottleneck
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\nTrend 3: Governance and semantics are the new competitive moat
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