FX swings, too many bank accounts, €30M stuck. Forecasts couldn’t keep up, so these 3 Treasurers rebuilt teams to make daily decisions with agentic AI.
Konica Minolta had €30M in delayed inflows, scattered across multiple systems and business units. Treasury was reacting too late. They deployed Forecast Agents, Reconciliation Agents, and Treasury Payments Agents—all part of an agentic AI system that works daily. Forecast accuracy improved by 20%, reconciliation time dropped by 75%, and they saved over €400K in financing costs by acting earlier.
Tri Star Energy was spending hours every morning consolidating over 100 files. Forecasts were built on static Excel models, updated manually. That lag created friction during expansion. Now, Forecast Agents dynamically test and select the most accurate model each day—based on real-time inputs. Accuracy improved by 25%, and daily prep was cut in half.
Pavion had 128 bank accounts spread across 22 legal entities. Treasury had no clear view of idle cash. With Agentic Bank Connectivity and Cash Positioning Agents, they closed 35 accounts, reduced reporting time by 75%, and surfaced idle liquidity that had been missed entirely.
What changed?
- Forecasts now adjust algorithms daily, based on actual data
- Inputs from AR, AP, payroll, tax, and intercompany flow in automatically
- Variance is explained with context, not just flagged
- Payment timing and reconciliation are built into forecast logic
If you’re still adjusting forecasts by hand—or trusting one model to handle everything — this session will show you what your peers did differently, and what it unlocked.
