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Some 70% of organizations are actively exploring or implementing large language model (LLM) use cases, but fewer than a third of generative AI experiments have made it into production. A common hurdle? The inability to access and leverage the data crucial for running AI applications effectively. Snowflake’s Accelerate 2025 virtual events dive into the challenges and myriad opportunities offered by AI.

Throughout my career, I’ve had the privilege of working across the full spectrum of enablement: internal enablement, partner enablement and customer enablement. Each of these domains brings unique challenges, audiences and approaches, but a common thread unites them all: the goal of fostering growth. At its core, training and enablement are not just about imparting knowledge or improving skills. While these are vital components, the true purpose transcends the transactional.

Sales reps dedicate just two hours each day to active selling, according to HubSpot research. At Snowflake, our sales team found they were wasting 10 to 15 minutes searching for the right content every time they needed to answer a single question, like “Can you explain how Snowflake handles data integration from various sources?” Valuable content was scattered across different platforms, forcing employees to hop between various tools to assemble the right information.

If you ask any advertiser about the most disruptive factor in recent years, they’ll probably hesitate between two contenders: privacy and AI. While AI is poised to have a transformative impact far beyond advertising in the future, one thing is certain: No organization today can address use cases involving consumer data without prioritizing privacy. Before we dive into the world of data clean rooms, let’s take a quick trip back in time to set the stage.

Being able to leverage unstructured data is a critical part of an effective data strategy for 2025 and beyond. To keep up with the competition and AI-accelerated pace of innovation, businesses must be able to mine the treasure trove of value buried in the mountains of unstructured data that comprise approximately 80% of all enterprise data — from call center logs, customer reviews, emails and claims reports to news, filings and transcripts.

As analytics steps into the era of enterprise AI, customers’ requirements for a robust platform that is easy to use, connected and trusted for their current and future data needs remain unchanged. "Serverless computing" has enabled customers to use cloud capabilities without provisioning, deploying and managing either hardware or software resources.

Snowflake is expanding its AI capabilities with the public preview of Cortex Agents, to help retrieve data insights by orchestrating across structured and unstructured datasets. Cortex Agents streamlines agentic application data access and orchestration for more reliable AI-driven decisions by building on top of enhancements to our Cortex AI retrieval services.

AI is proving that it’s here to stay. While 2023 brought wonder and 2024 saw widespread experimentation, 2025 will be the year that the advertising, media and entertainment industry gets serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

We’ve all experienced those moments as consumers — receiving an offer for something irrelevant or being addressed by the wrong name. For years now, I’ve received promotional emails and postcards from a global automotive brand addressed to someone named “Leighann Drake.” Neither I nor anyone in my family goes by that name, nor do we own a vehicle from that brand.

The emergence and growing adoption of generative AI and the agreement to and implementation of the EU AI Act uncannily coincided. These two factors have catalyzed an AI renaissance within many enterprises. Yes, companies were already applying AI here and there across their organizations — but responding to the impact of these two exogenous forces required a whole new way of thinking and doing. All of a sudden, all eyes were on AI.

AI is proving that it’s here to stay. While 2023 brought wonder and 2024 saw widespread experimentation, 2025 will be the year that healthcare and life sciences get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

For years, companies have operated under the prevailing notion that AI is reserved only for the corporate giants — the ones with the resources to make it work for them. But as technology speeds forward, organizations of all sizes are realizing that generative AI isn’t just aspirational; it’s accessible and applicable now.

People often ask me, “Why did you join Snowflake, and why did you choose to work on developer productivity?” I joined Snowflake to learn from world-class engineers and be part of the highly collaborative culture. These have been the secret sauce to Snowflake’s rocket-ship growth. Snowflake was embarking on a remarkable transformation of developer productivity, and I had to jump on the rocket ship as it was taking off!

AI is here to stay. While 2023 brought wonder and 2024 ushered in widespread experimentation, 2025 will mark the year that retailers get serious about AI's real-world applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production even as major AI innovators face competition from newer upstarts. At this point, the pace of AI evolution is outstripping the news cycle.

Generative AI’s potential to drive innovation, improve efficiency and create competitive advantages is enormous. However, the ability to fully realize the benefits of generative AI hinges on one crucial factor: data strategy. “Data Strategies for AI Leaders,” a report co-written by MIT and Snowflake, underscores how organizations must invest in robust data foundations to succeed in the AI era.