IT asks the board for $5 million to expand AI. The board asks why. The answer comes back technical: unit costs are falling and productivity should rise. But AI spend, project costs, and budgets sit in different systems, and business outcomes aren’t tracked anywhere. The board hears cost, not value.
There’s a better way forward, and it doesn’t mean starting over.
In this webinar, TBM practitioners Carollyn Gehrke of Cprime and Lauren Bateman of FedEx show how to apply Technology Business Management (TBM) to AI investments. You’ll learn how to connect AI cost and consumption to strategy, ownership, and measurable outcomes, then put it into practice with a four-step roadmap. The session closes with a live Q&A answering the questions finance and IT leaders ask most.
The key insight: a token count isn’t a business outcome. Rising AI spend can be a smart investment, and falling unit costs can still mean wasted money. The difference is whether you can show the board what the spend is buying.
Key Takeaways
- Reframe AI investment reviews around what you’re buying, for whom, and why now.
- Extend your existing TBM practice to AI instead of building a separate cost model.
- Scale governance as AI matures, from spend-limited experiments to full production visibility.
- Assign clear accountability so IT runs the platform, finance owns transparency, and the business owns outcomes.
- Pair FinOps and TBM to link consumption efficiency with investment value.
- Start with one decision-ready slice and expand as your data matures.
Watch the video
