Summary: Most AI conversations focus on doing existing work faster. That’s the wrong question. AI’s most distinctive value is making possible the analysis, investigation, and scenario modeling that never happened—because the manual effort couldn’t be justified. Every finance and IT team has questions they’ve stopped asking: the variance analysis nobody does at the SKU level, the vendor payment pattern review that could save six figures, the technical debt quantification that’s “too complex” for the current team. This session reframes the AI ROI question from “how much time does it save?” to “what decisions are you not making because the analysis was never worth doing?”

Outcome: Attendees identify specific high-value analyses in their own organizations that AI now makes feasible for the first time

Takeaway: A framework for identifying “below-the-line” work—analysis that was always valuable but never justifiable—and categories of hidden opportunity specific to finance and IT leaders

Key Discussion Points:

• The ROI trap: why “time saved” is the wrong measure of AI value
• Below-the-line work: analysis that was always valuable but sat below the threshold of justifiable manual effort
• Case study: a compliance analysis across dozens of inbox spreadsheets—work that would never have been approved manually—completed in one hour with AI assistance
• The questions you’ve stopped asking: how to recognize below-the-line opportunities in your own organization
• Finance-specific opportunities: SKU-level variance analysis, customer profitability segmentation, vendor payment pattern optimization, scenario modeling at granularity that was previously impractical
• IT-specific opportunities: license optimization analysis, integration failure pattern detection, technical debt quantification in financial terms
• Why “too complex for our team” is often a manual-effort barrier, not a capability barrier
• The forecasting problem: how one company’s request for AI help was really a request for analysis they couldn’t afford to do manually
• From “AI project” to “business question”: why starting with the problem instead of the technology changes everything
• Connecting below-the-line work to decisions: analysis without action is just expensive curiosity