Ensure AI’s ROI by Understanding Its TEI: Snowflake’s Total Economic Impact

In the rush to deploy the latest bright shiny object, some organizations initially bypass the business case to get AI into deployment quickly. In very competitive markets, a come-what-may approach might make sense. However, the path to successful AI implementation in the long term isn't just tech adoption; it's about understanding its impact on your business. 

This involves not only the quantifiable value that these initiatives deliver back to the business but also an understanding of the up-front and ongoing costs associated with building and maintaining AI infrastructure — and building the requisite skills. Without a clear picture of both sides of this equation, even the most promising AI projects can disappoint.

Data leaders are acutely aware of the stakes. As one executive observed, “When you execute an AI initiative, you are investing your career in it. It’s where careers are made.” Leaders need confidence that their projects will deliver a tangible return on investment. While some may argue that measuring AI's economic impact is like any other tech initiative, the unique characteristics of AI — its data-intensive nature, iterative development and potential for transformative but sometimes indirect benefits — can make this measurement more nuanced, and sometimes elusive.

To help organizations navigate this complex economic landscape, Snowflake commissioned a Total Economic Impact™ (TEI) study by Forrester Research. The TEI methodology evaluates the financial impact of technology investments by examining four key areas: benefits, costs, flexibility and risk. For the purposes of the study, Forrester interviewed four Snowflake customers and aggregated the interviewees’ experiences and results into a single composite organization. The resulting model captures both the qualitative and quantitative aspects of AI investments and estimates the return on investments at a whopping 354% over three years!

The first component of the TEI is top-line revenue growth, and that didn’t disappoint. On average, the composite Snowflake customer achieved a 6% increase in incremental revenue addressable with data-driven innovation. AI initiatives led to faster time to value, reduced customer churn, increased market share and minimized revenue loss. For example, a food services organization built supply chain optimization models that reduced revenue loss from stock-outs, expedited shipping and reduced inventory loss. On the sales side, the organization reduced customer churn by 4.5%, with a direct impact on top-line revenues. Likewise, production optimization at a mining organization, using near real-time data streaming from the field, delivered incremental revenues. 

Next the study looked at the impact of cost and time to value. Cost savings came from (1) real-time customer insights, which enabled the companies to make better, data-driven decisions; (2) improved supply chain management, which allowed proactive customer order management and optimized shipping costs; and (3) enhanced productivity for business analysts and non-data teams such as accounting, finance and supply chain. With faster access to accurate, consolidated data, interviewees’ organizations optimized their operations, reduced waste and increased efficiencies across these departments. 

“Before Snowflake, we had very limited access to our supply chain metrics. Every single supply chain dimension and metric had to be downloaded so that it can be used out of the systems themselves. With Snowflake, we migrated that data into a database and automated all of that. Instead of having to calculate customer profitability using all the Excel files, we can just reference tables and views instead.” —A U.S.-based food services organization

The third component was operational costs and the benefit of simplicity. Customers in the TEI study found that streamlining workflows across the data and analytics value chain significantly improved productivity. For data engineers, Snowflake deployments eliminated the need to manage complex infrastructure and hardware, automated processes through robust data pipelines and removed the burden of complex data integration tasks – resulting in a 35% time savings. Data scientists benefited from improved pipelines and less data prep required. The food services organization saved 10 full-time equivalents (FTEs) in analyst roles and expected further savings in the future.

Finally, the study explored the benefit of a fully managed, cloud-native architecture. It found that Snowflake simplified data operations by eliminating the need for legacy licensing costs, hardware management and refreshes, extensive configuration, planned downtime for upgrades and other routine maintenance. Interviewees said their organizations retired costly and complex legacy data systems, which previously required extensive resources for maintenance and upgrades. On average, the composite organization reduced their legacy infrastructure management team by six IT and database administrator FTEs. It reassigned IT and database administrators to other strategic tasks. 

For the full details of the customer use cases and the derived benefits, download The Total Economic Impact™ of the Snowflake AI Data Cloud: Cost Savings and Business Benefits Enabled by the Snowflake AI Data Cloud by Forrester Research report. 

And, check out the joint Forrester and Snowflake webinar in which I join forces with my former colleagues to explore trends in both AI adoption and platforms of the future, as well as the details of the TEI study.