How Finance Pros Should Manage Data Context and Token Forecasting

In this episode of Future Finance, hosts Paul Barnhurst and Glenn Hopper discuss AI fatigue, better ways for finance teams to use AI, and the growing challenge of forecasting AI costs. They explore why finance leaders should focus less on chasing new models and more on building the data, context, and workflows needed to get real value from AI.

In this episode, you will discover:

• Why finance teams should stop chasing every new AI model.
• Why context is becoming more important than prompting alone.
• How better KPIs and data foundations improve AI results.
• How organizations can forecast and manage AI token costs.
• Why AI success should be measured by business outcomes.

Paul and Glenn explain why effective AI adoption isn't about using the most tokens or knowing every new tool. It's about using AI to help finance make better, faster decisions.

Follow Glenn:

LinkedIn: https://www.linkedin.com/in/gbhopperiii

Follow Paul:

LinkedIn: https://www.linkedin.com/in/thefpandaguy

Disclosure: Portions of this episode (such as the introduction or promotional segments) use AI-generated voice narration produced under human editorial review.

Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.

Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.

In Today’s Episode:

[00:00] – Trailer

[03:17] – AI Fatigue & Model Overload

[09:38] – Beyond Prompt Engineering

[16:03] – Building Better AI Context

[24:29] – Organizing Finance Knowledge

[27:29] – Token Maxing & AI Costs

[32:06] – Forecasting Token Spend

[38:23] – Focusing on Business Value

[41:43] – Closing Thoughts