Webinar
See It in Action: A Morning with Snowflake CoWork
Video Overview
Business users lose time hunting for answers in email, dashboards and Slack. Snowflake CoWork brings that context into one place. It routes each request for you and can deliver a personalized morning briefing on a schedule, so you start the day on decisions instead of data gathering.
This demo follows a VP of sales at a fictional live-events company through a typical morning. Watch CoWork explain a surge in VIP upgrades at a Miami show and share the findings with the field team in Slack, without switching tools. Then deep research runs four sub-agents in parallel to dig into surveys, social sentiment and venue data, and returns a cited briefing.
Artifacts turn one-off dashboards into reusable, governed assets that teams build on together. Cortex Sense builds context across your artifacts and datasets automatically, so answers get richer as more data flows through Snowflake.
Today, our demo will focus on a fictional business, Snow Music, which manages live tour operations and a high traffic fan application. I will demo as the VP of sales, first showing how a business leader starts the day. Data everywhere, action items buried in unread emails and hundreds of Slack messages. The information is everywhere and another static dashboard isn't going to cut it. This is where Snowflake CoWork, your personal work agent comes in. CoWork brings together knowledge into one trusted enterprise agent, makes recommendations and takes actions.
Let's get started. I will begin morning triage by prompting CoWork. Start my day, what do I need to know? Notice that there are no complex menus or agent pickers here, exactly how a business user wants to do it. I simply ask my question and it automatically routes the request based on my context. Snowflake CoWork is analyzing data across many source tables, emails, Slack conversations, and more. It knows my behavior over time to deliver highly personalized responses, prioritizing what actually matters. Look at this update, tailored specifically for me. CoWork provides the critical information I should know about. While prompting this live is incredibly easy, I can also turn this into an automated flow by setting up a scheduled automation. Here, I'm showing how CoWork automations are set up. This analysis will be delivered straight into my inbox first thing in the morning, completely eliminating manual triage, allowing me to start taking actions immediately.
Let's dig into something I saw in my morning update. I noticed a surge in VIP fan upgrades at the Miami show. My regional manager has already started looking into this and shared a live interactive dashboard with me using Artifacts. Artifacts now include publishable dashboards with governed views of live data. It turns one-off dashboards into reusable insights so teams can ask questions and get insights building on each other's work. I can query this artifact directly and ask what is driving the VIP surge in Miami. CoWork answers in line instantly with a grounded chart. Now I need to loop in a local field team. To do so, I'll simply send a Slack message to the team with this link. And just like that, the message is directly shared via Slack using our MCP connectors to execute this task. I've diagnosed the surge, collaborated with my team, and looped in field reps without switching tools, all in one place from CoWork.
This VIP surge in Miami spans multiple data sources, including surveys, social sentiment, ticket resale, and venue wait times, requiring deeper exploration. To solve this, I've enabled deep research in my prompt to execute multiple step reasoning across data and web sources in one shot. Without deep research, this would take multiple analysts and days of manual work. Because it takes 10 to 15 minutes to fully execute, I've pre-run it here. Deep Research automatically spins up four parallel sub-agents to tackle different datasets at once, providing incredibly thorough research. It delivered a synthesized briefing with built-in citations. Now I understand what happened to my business and exactly why and what to do next.
Next, I have a very important meeting coming up. Usually, preparing for this means chasing a bit across platforms. Instead, I'll simply ask CoWork, prepare me for my next meeting with three most important talking points. CoWork securely scans meeting notes, Slack conversations, emails, and other sources to deliver a set of talking points. Now I'm completely prepared for my meeting. To make this repeatable process, I will use the follow-up prompt. Please prepare me for this meeting every Tuesday at 10 a.m. This automation will run every week right before my meeting. CoWork is personalized for me, operating as a true productivity partner.
Now I'm going to ask a question that spans across multiple datasets and artifacts. Which cities had strong fan engagement but underperformed on merch in the quarter. With Cortex Sense working under the hood, the context layer builds understanding across every artifact and data set from my organization. The more data and context the data flows through Snowflake, the richer the context becomes and every shared asset makes the model smarter. Cortex Sense is pulling from every artifact, including fan engagement and the business artifact my GM shared earlier. Neither source alone could answer this question. Cortex Sense automatically builds that understanding for you. No manual mapping, no waiting on the data team. Look at that. It combined fan engagement data from one artifact with merch revenue from another without me telling it where to look.
And because work doesn't stop when you leave your desk, this entire experience extends to a seamless mobile experience, enabling you to securely ask questions and take action on your data and workflows from anywhere on the go.
To wrap up, Snowflake CoWork is now a personal work agent for every business user. It understands your data and your work from day one, answers the hardest why questions with cited research, and runs the work on demand, on schedule, and across the tools your business already uses. All inside Snowflake's governance perimeter.
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