Though AI is (still) the hottest technology topic, it’s not the overriding issue for enterprise security in 2025. Advanced AI will open up new attack vectors and also deliver new tools for protecting an organization’s data. But the underlying challenge is the sheer quantity of data that overworked cybersecurity teams face as they try to answer basic questions such as, “Are we under attack?”

Marketers at financial services companies have their work cut out for them. Their companies have a wealth of data, but that data is often fragmented among different systems and divisions, and protected-class data has a wide range of restrictions on how it can be used for different product lines.

As organizations mature in their execution of data and AI initiatives, a burning question remains: How do we measure the effectiveness of our teams and our impact on the business? This isn’t the perennial “What’s my data worth?” dilemma often asked rhetorically and answered theoretically. Today’s challenge is concrete: to define and track the metrics used to justify continued investment in data and AI innovation.

We often hear from customers that they’re excited about what they could do with data and AI but are not sure how to do it. Or that the tech teams are “all in” but they can’t convince the powers that be to move forward. It’s not that they don’t know what to do — they could list a number of initiatives or use cases that would benefit from insights from their data or to which they could apply AI. But many organizations seem to suffer from institutional paralysis.

We had a busy week catching up with customers and partners at Microsoft Ignite in Chicago and online. We shared lots of exciting innovations to help customers build applications, keep their data architecture open and flexible and harness the power of AI in so many ways. Here’s a quick recap.

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.

A revolution is underway as martech and adtech continue to converge around first-party data — and the revolution is happening on Snowflake. This seismic shift isn't a mere trend; it’s a game-changing transformation that will continue to shape the way that marketers and advertisers connect with consumers amid increasing privacy regulations, new identity frameworks and rising customer expectations.

Sports organizations have made major strides in recent years. Teams across the industry are transforming into data-driven organizations on the fan engagement, monetization and sports operations sides of the business. This trend is perfectly timed with the AI hype cycle. AI is proving that it’s here to stay. While 2023 brought panic and wonder, and 2024 saw widespread experimentation, 2025 will be the year sports teams get serious about AI's applications.

Snowflake leaders offer insight on AI, open source and cybersecurity development — and the fundamental leadership skills required — in the years ahead. As we come to the end of a calendar year, it’s natural to contemplate what the new year will hold for us. It’s an understatement to say that the future is very hard to predict, but it’s possible to both prepare for the likeliest outcomes and stay ready to adapt to the unexpected.

AI is proving that it’s here to stay. While 2023 brought panic and wonder, and 2024 saw widespread experimentation, 2025 will be the year that financial services enterprises 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.