How Finance Cut Forecasting to Hours With AI

How Finance Cut Forecasting to Hours With AI

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Oct 23, 2026 / 8 min read AI & ML Copy post link Open in Claude Open in ChatGPT

How Snowflake's Sales Finance Team Cut Its Monthly Forecasting Time from a Week to Hours with AI

Gayatri Kannan +2

On Snowflake's sales finance team, we used to spend more than a week developing our monthly forecast. Today, it takes a few hours.

What changed was how much AI knows about our data before anyone types a question.

That knowledge also made our AI more efficient. Our analytics team built a profile that gives the AI standing context about our data, terminology and workflows. With it, Snowflake CoCo reaches an answer in less than half the steps, so each question costs less even as the team asks more of them.

This post covers the CoCo profile behind those results, why it makes each answer more efficient and how our analysts built 36 skills on top of it.

Key takeaways

Snowflake's sales finance team cut its monthly forecast and variance analysis time from more than a week to a few hours.

A CoCo profile cut the steps per answer from roughly 3.0 to 1.3, which lowers the cost of each question (Snowflake internal CoCo usage, February to May 2026, n = 44 finance users).

Every answer traces back to a data source certified by the analytics team.

The team built 36 skills in two months, and 87% of first adopters still use CoCo daily.

A weeklong forecast process now takes hours

The sales finance team runs more than 30 recurring analyses each month for Snowflake's sales leaders, covering the topline forecast, sales pipeline, commissions and business performance reporting. The forecast is the most involved of these, and it comes with a variance analysis that explains what's moved since the previous forecast. In the past, building the forecast meant reviewing pipeline deals, reading account notes in the CRM, comparing against historical trends and checking current field behavior. The work was split across several analysts, and because it took more than a week, it could only run once a month. Using the topline forecast skill in CoCo, we now complete the same analysis in a few hours. That faster turnaround has changed the cadence. We now publish a midmonth update, so sales leaders see where the quarter is heading while there is still time to act. Any analyst can also rerun the skill to test a scenario.

"CoCo has truly become the first choice for all of our team's routine workflows and ad hoc analysis. Not only has it reduced turnaround times; it has unlocked the ability to process complex analysis. Any individual can use the skill to dive deeper and run scenario analysis." Gayatri Kannan

Senior Director of Sales Finance, Snowflake

Capable AI only helps when it knows your data

Our data changes daily, spread across dozens of certified sources and a CRM that reflects deals revised overnight. Our first attempts were familiar to anyone who has pointed AI at real finance work. The AI was capable, but it didn't know our world. It would reference a related but incorrect data source or ask for clarification that an experienced analyst wouldn't need. Both failures came from missing context: Every session, the AI had to infer things we knew but had never written down.

"When we first launched AI to the team, there were a lot of struggles with inconsistent and inaccurate results. It felt like we had to constantly remind a new hire about relevant terminology and data sources. Having a dedicated GTM Finance profile changed everything. The team is empowered to run complex analyses and rethink workflows themselves with minimal technical support and no governance concerns." Gayatri Kannan

Senior Director of Sales Finance, Snowflake

What a GTM Finance profile actually contains

Our partners on Snowflake's strategic finance analytics team built our CoCo profile for 'GTM Finance'.

A profile is standing context the AI reads before it answers anything, so nobody has to restate it in a prompt.

Without it, the AI would spend about three steps per session finding the right data source, resolving terminology and inferring what an experienced analyst already knows. With the profile in place, the first step of every conversation is already the work itself.

Layer What it holds Why finance needs it

Certified data sources 20+ sources certified by the analytics team Answers come from sources the analytics team has approved

Terminology Definitions for bookings, pipeline, quota and other core metrics No ambiguity on reported metrics

Repetitive analyses Our recurring monthly and quarterly work The AI recognizes the task

Output formats Our standard report and variance layouts Output arrives ready for review

Escalation paths Where to go when a number looks wrong A person resolves exceptions

Memory scaling: Why a profile makes each answer more efficient

Memory scaling is the effect where an AI agent gets cheaper per unit of work as it accumulates context about its environment. It answers in fewer steps, and each step uses compute. Across 44 finance users, those with a profile reached an answer in about 1.3 steps, compared with about 3 steps for those without one. Two things make each answer more efficient. The profile cuts the number of steps, and steps drive cost: Without a profile, each extra step adds about 0.05 credits (roughly 10 cents), so a five-step session costs about three and a half times a one-step session. The profile also makes sessions cheaper at the same length. At every step count in the data, a session with the profile cost less than one without it.

Finance analysts now build their own AI skills

Once the foundation was in place, our analysts could build their own skills on top of it, without relying heavily on the analytics team.

A skill is a reusable workflow the AI recognizes and runs on request, and building one requires no technical background.

In two months the library grew from three starter skills to 36, and several Excel workbooks became shared skills the broader team could run. Three months after launch, 87% of first adopters were still using CoCo daily. Here's a selection of the skills we built.

Skill What it replaced

Bookings BvA Manual month-end variance analysis across data and finance

Sales travel expense report Hours of cross-team reporting and manual email drafting

Topline forecast...