The Data Science Projects Most Finance Teams Ignore

Most finance departments treat data science as someone else’s problem. Taylor Thomson embeds himself directly in AI implementation projects.

At WITHIN, Thomson leads cross-functional initiatives with data science teams to develop internal databases using generative AI technologies like GPT-4 and Bard. But the work focuses on solving specific operational bottlenecks rather than chasing AI headlines.

“We’re able to invest in best in class technology to help us be more efficient,” Thomson explains. The emphasis is on measurable efficiency gains, not implementing AI for its own sake.

One application involves processing client satisfaction surveys at scale. WITHIN achieves over 50% quarterly response rates—unusually high for B2B contexts. The challenge isn’t collecting feedback; it’s analyzing hundreds of open-ended responses to identify actionable patterns. AI handles the heavy lifting, surfacing themes that would take weeks to identify manually.

Another area involves competitive intelligence. Using Pathmatics (now part of Sensor Tower), WITHIN tracks social media spend across channels. AI processes this data to identify trends—which competitors are shifting budgets toward specific platforms, what creative approaches are gaining traction, where market dynamics are changing. His technical approach to revenue operations combines financial rigor with data science applications.

These aren’t flashy use cases. They’re operational improvements that compound over time. But they represent how finance leaders can actually add value through technology rather than just measuring what technology teams build.

Thomson also recognizes AI’s limitations. “Technology alone doesn’t create results,” he notes. WITHIN’s stack includes Salesforce, Outreach, and various other tools. Value comes from systematic usage and clear problem definition, not just tool acquisition.

This measured perspective contrasts with finance teams either ignoring data science entirely or making grand AI transformation announcements while changing little about actual processes. Thomson focuses on specific problems where AI provides genuine advantage: processing unstructured feedback, identifying patterns in competitive data, automating routine analytical tasks. Taylor Thomson’s work in Denver’s business community demonstrates how finance leaders can bridge technical and commercial functions.

For finance professionals wondering how to engage with data science productively, Thomson’s example suggests starting with operational pain points rather than technology exploration. Find bottlenecks where AI actually helps. Measure whether it’s working. Expand based on results rather than hype.

The companies getting AI right won’t be those with the boldest announcements. They’ll be the ones quietly solving problems while competitors debate strategies. Thomson’s position at WITHIN’s operations gives him the perspective to distinguish genuine value creation from expensive experimentation. His documented methods show how finance can lead technical implementation rather than just fund it.