As people analytics leaders, we often talk about the future of work and how technology will shape it. But at Snowflake, we’re not just talking about it — we’re actively building it to better support our customer community, particularly within the people domain. It’s an exciting time, and it’s also one where we need to be thoughtful about how we bring new solutions to life and consider the wider implications of new innovations.
For us at Snowflake, the conversation isn’t about whether we should use AI but about how we can use it to create more meaningful work across our teams. We’re approaching this by focusing on a few core principles that guide our strategy and help us navigate the complexities of organizational change.
Across industries, in people analytics, it’s easy to get caught up in the technical possibilities of AI. But as Snowflake’s Chief People Officer, Arnnon Geshuri, often says, “The key lesson was that you have to choose use cases that have a meaningful impact on people’s daily work.” This means we start with the human experience, not the technology.
A great example of this is how we’re using AI to analyze and standardize our job descriptions by building a custom app within Streamlit. We regularly hear from our customers that creating and updating job descriptions can be a manual, time-consuming process, so our teams developed a process to automate and standardize this for certain functions and countries in partnership with recruiting and our people ops teams. In doing so, we didn’t just save time; we unlocked a wealth of unstructured data that allowed us to see what skills we’re hiring for now and how those skills are shifting over time. It’s a foundational step that makes strategic workforce planning possible for us at Snowflake. We’re not just automating a task; we’re creating an insight engine.
Another pain point we’re tackling? The all-too-familiar frustration of searching for employee policies. We all know the drill: an endless hunt across a sea of knowledge articles for a simple answer. We’re leveraging Snowflake Intelligence (in public preview) to deliver policy and employee information in a natural, conversational way, moving beyond just surfacing a list of links.
The goal for our employees and for our customers using this technology is to make it feel like you’re asking a person, not a search engine. This doesn’t just improve the employee experience; it also allows operations teams to stop answering repetitive tickets and focus on the complex, nuanced questions that truly require a human touch. And just like with job descriptions, this work is about more than a quick fix — it’s about bringing all of the unstructured policy data into one place to drive consistency and scale.
One of the biggest hurdles to AI adoption is organizational acceptance. Most people who got into HR were drawn by the people aspect. So AI can be daunting for nontechnical employees. Our people team has taken steps to show teams how AI agents can enhance rather than replace human capabilities.
To build this trust, we started with our own people team. We demonstrated how automation could eliminate repetitive, transactional tasks — the kind of work that often gets in the way of meaningful employee support and engagement. By freeing up time, our people team can focus on higher-value activities such as coaching managers, providing personalized support to employees and building a stronger culture. The goal is to make HR jobs more fulfilling, not less.
Many organizations already have the data they need for AI, often sitting in systems such as a human capital management (HCM) system, applicant tracking system (ATS) or other employee systems of record. But as we’ve learned, having the data is not enough. People data can be messy and you need to invest time in a solid people data strategy from governance to cleanliness, as you must have a data strategy to have an AI strategy.
This is where Snowflake’s foundation as a data platform is so critical. At Snowflake, we’re bringing together data from disparate systems into a single, governed source of truth and offering that same functionality to our customers. This helps train AI models on clean, consistent and reliable data. Without this solid data foundation, the outputs could be unreliable, eroding the very trust we’re working so hard to build.
This is just the beginning. Our work is a testament to the power of a strong partnership between so many teams at Snowflake: our enterprise technology, recruiting, HRIS and people ops teams. With the support of our Chief People Officer, we’re able to take risks, learn and build a future where AI helps us create a better experience for everyone at Snowflake. We’re continuing to find ways to remove friction, automate the repetitive and allow teams to focus on what matters most: employees.
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