When a recent Snowflake announcement included the declaration that “AI should run where your data lives,” it wasn’t just a product statement. It was a declaration of how the Snowflake Data Cloud plans to help modern enterprises win with AI.
Here at Hakkoda, we have always believed in a simple truth: that AI is only as good as the data that fuels it. Unfortunately for most enterprises, it can be a difficult and overwhelming task to maintain cohesive, accurate datasets across the multiple storage and processing systems that are needed in order to uphold the integrity of their data infrastructures.
Fortunately, Snowflake’s core offering, now supplemented with Snowflake Intelligence and a powerful Cortex AISQL framework, was built to help enterprises wrangle with this exact challenge. By collapsing the distance between data storage, transformation, and AI workloads, organizations remove costly bottlenecks, improve governance, and accelerate innovation.
More crucially, in a world where AI initiatives often stall due to fragmented pipelines and disjointed infrastructure, doing AI where the data lives is the surest path from proof of concept to measurable impact.
Production-Ready AI and What it Means
For Hakkoda, this transformative tool is the sort of advancement we aim to bring to our clients. Since every organization has unique needs, workflows, and core principals, it is important to meet those specific needs in a cost-effective and labor-efficient way.
By integrating Snowflake Intelligence’s capabilities with a strategic approach to ensuring data quality and governance, we can help businesses zero in on the insights that truly matter—whether that is optimizing supply chains, improving customer experiences, or accelerating R&D cycles.
Snowflake also recognizes that their AI requires more than just a comprehensive algorithm to work with large-scale datasets. In order for companies to trust their AI outputs, Snowflake Intelligence has built in performance monitoring, scalability, and compliance controls through the AI Observability, Provisioned Throughput, and AI Governance Gateway programs.
On top of those digital checkpoints, the Data Science Agent—powered by Anthropic—makes it possible to fast track learning new tools and programs with a few natural language prompts, lowering the barrier to production-ready AI even further.
Shifting AI Integration Strategies
The introduction of Snowflake Intelligence is, on one hand, a logical next step built on the tried-and-true premise that the tooling should come to the data and not the other way around.
For years, companies have struggled with the growing complexities of managing vast, siloed datasets spread across multiple divisions, systems, and formats. The results? Valuable insights get buried below layers of incomplete, inaccurate, and incompatible data sources as teams waste countless hours reconciling entire data estates, adding to skyrocketing operational costs.
Snowflake Intelligence tackles these challenges head-on through the implementation of Cortex AISQL. While far from abandoning an audience of analysts versed in the finer points of querying large data sets, Snowflake Intelligence’s conversational language interface means that companies will no longer need deep SQL expertise or custom dashboards to work with enterprise-scale data.
Thanks to this comprehensive feature, business leaders and non-technical team members can derive meaningful insights from more than just structured data, but from the countless documents, images, and audio files stored within their enterprise’s system.
From Months to Minutes
With Snowflake Intelligence, access to enterprise data is both convenient and markedly more efficient to query.
Imagine replacing weeks of manually searching and analyzing layers upon layers of data with a single prompt. In other words: imagine finding the answers you need by asking a simple question.
Snowflake’s Cortex AISQL integrative framework makes this possible by expanding SQL into a multimodal ‘ecosystem’ capable of sorting through vast networks worth of information.
AI for the Real World: Bringing Natural Language Insights to the Olympics
Snowflake is putting these capabilities to the test a remarkable real-world use case, meanwhile: Snowflake’s partnership to drive data collaboration at the 2028 Olympics and Paralympics.
By applying Snowflake’s compute power directly to their massive datasets and enabling non-technical users with natural language insight, organizers and operators will be able to streamline everything from ticket pricing, to scheduling, to foot traffic routes.
What once required multiple teams and months of planning can now be optimized by having access to near-real-time data—improving experiences for fans, participants, and event organizers.
Better Data, Better Business
Perhaps one of the most important takeaways from this year’s Snowflake Summit is that AI should not be an afterthought layered onto datasets scattered across your technology stack.
AI should live and operate where the data resides, and with the help of Snowflake Intelligence’s built in governance and performance guardrails, it has never been more reliable and efficient to do so.
At Hakkoda, we don’t just believe in this approach, we’ve built our reputation on it. By bridging Snowflake expertise with deep industry experience, we have helped some of the world’s most data-intensive organizations design and deploy modern architectures that make AI truly production-ready.
From architecting scalable Snowflake environments to enabling real-time, AI-driven decision-making, we know how to unlock the full potential of your data platform so your AI can deliver real business value, faster.
Ready to shorten time to value on your next AI investment? Start where your data lives and talk to one of our experts today.
