# Snowflake Cuts Monthly Forecasting Time to Hours Using AI > By leveraging AI and the innovative CoCo profile, Snowflake's finance team has slashed its forecasting time from over a week to mere hours, marking a pivotal change in operational efficiency and analytics capability. **Source**: snowflake.com | **Published**: 2026-10-23 | **Type**: case_study ## Key Facts - Snowflake reduced forecasting time from over a week to hours, enhancing agility in decision-making. - CoCo profile cut answer steps from 3.0 to 1.3, lowering question costs and increasing efficiency. - 87% of analysts still use CoCo daily, indicating strong adoption and potential for sustained productivity. - Each extra step in AI sessions costs about $0.05, highlighting financial benefits of streamlined processes. - Analysts independently built 36 skills, showcasing empowerment and potential for scalable innovation. ## Summary \## Summary Snowflake's sales finance team transformed its monthly forecasting process, reducing the time required from over a week to just a few hours. By implementing a CoCo profile that provided the AI with essential context about their data and workflows, the team achieved a significant increase in efficiency, allowing for more frequent updates and deeper analysis. \## Background Snowflake is a cloud-based data-warehousing company that provides a platform for data storage and analytics. Prior to the deployment of AI, the sales finance team faced challenges with a lengthy forecasting process that involved multiple analysts and took more than a week to complete. This process limited their ability to respond quickly to changes in the sales environment. \## Challenge The primary challenge was the inefficiency of the monthly forecasting process, which required extensive manual effort to compile data from various sources, analyze historical trends, and assess current sales behavior. This bottleneck restricted the team's ability to provide timely insights to sales leaders. \## Solution The sales finance team implemented a CoCo profile designed specifically for 'GTM Finance.' This profile provided the AI with a comprehensive understanding of the team's data sources, terminology, and recurring analyses. By reducing the number of steps required to reach an answer from approximately 3.0 to 1.3, the AI became significantly more efficient. The team also developed 36 new skills within two months, allowing analysts to conduct their own analyses without heavy reliance on the analytics team. \## Results The implementation of the CoCo profile led to a drastic reduction in the time spent on the monthly forecast and variance analysis, from over a week to just a few hours. The efficiency gains enabled the team to publish midmonth updates, providing sales leaders with timely insights. Additionally, 87% of first adopters continued to use CoCo daily three months after its launch. \## Key Insights 1. Establishing a clear context for AI through a dedicated profile can significantly enhance its efficiency and accuracy. 2. Empowering analysts to build their own skills fosters ownership and leads to more effective workflows. 3. Certifying data sources and defining terminology are crucial steps in ensuring consistent and reliable outputs from AI. \## Customer Testimonial "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 ## Entities - **Companies**: Snowflake - **Products**: CoCo - **People**: Gayatri Kannan - **Organizations**: Snowflake's sales finance team, Snowflake's strategic finance analytics team ## Key Concepts AI in finance, Forecasting efficiency, CoCo profile, Memory scaling, Skill building, Data certification, Terminology standardization, Analyst empowerment ## Definitions - **CoCo profile**: A CoCo profile is a standing context that the AI reads before answering questions, reducing the need for analysts to restate information. - **Memory scaling**: Memory scaling refers to the phenomenon where an AI agent becomes more efficient and cheaper per unit of work as it accumulates context about its environment. - **Skill**: A skill is a reusable workflow that the AI recognizes and can execute on request, allowing analysts to automate repetitive tasks. - **Certified data sources**: Certified data sources are data repositories approved by the analytics team to ensure accuracy and consistency in AI responses. - **Variance analysis**: Variance analysis is the process of comparing actual financial performance to forecasted performance to understand discrepancies. ## Use Cases - Monthly forecasting - Variance analysis - Sales pipeline reporting - Commission reconciliation - Sales travel expense reporting - Scenario analysis ## Frequently Asked Questions **How did AI improve forecasting time?** AI improved forecasting time by utilizing a CoCo profile that provides context about data and workflows, allowing the sales finance team to reduce the forecasting process from over a week to just a few hours. **What is the role of the analytics team in this process?** The analytics team certifies data sources and defines terminology, ensuring that the AI has accurate and consistent information to work with, which enhances the quality of the analyses performed. **How many skills were built using the CoCo profile?** The finance team built 36 skills on top of the CoCo profile within two months, significantly increasing their ability to automate and streamline various analyses. **What challenges did the team face when first implementing AI?** Initially, the team faced challenges with inconsistent and inaccurate results because the AI lacked context about their specific data and workflows, leading to misunderstandings and errors. **How can other teams replicate this success?** Other teams can replicate this success by certifying their data sources, encoding terminology and output formats, and allowing team members to build their own skills based on a reliable foundational context. ## Links - [Read on Welcome.AI](https://welcome.ai/content/snowflake-cuts-monthly-forecasting-time-to-hours-using-ai) - [Original source](https://www.snowflake.com/content/snowflake-site/global/en/blog/sales-finance-ai-forecast) - [Snowflake](https://welcome.ai/company/snowflake): Featured company --- Source: Welcome.AI | https://welcome.ai/content/snowflake-cuts-monthly-forecasting-time-to-hours-using-ai