Recap & Recording: Corporate Treasury in 2026 and beyond

Our panel focused on practical steps – API-driven real-time cash visibility, tighter ERP/TMS integration, and AI-enhanced forecasting and scenario planning – while stressing that adoption depends on landscape complexity, data quality, and executive support.

Our speakers highlighted that the future corporate treasury will blend automation and human judgement: routine tasks will be automated (reconciliation, feeds, fraud detection), freeing treasurers to advise on funding, hedging and commercial value creation. The conversation stressed incremental pilots, clearer banking footprints, governance around AI outputs, and reskilling treasury teams so treasury can move from operational firefighting to board-level strategic contribution.

Alexander Fleischmann

• “Predictive forecasting shouldn’t be a black box. Compare traditional forecasts with AI forecasts, and don’t just trust the model blindly. Human intelligence still plays a key role.”
• “Lean landscapes make AI more effective: fewer ERPs, fewer global banks, clear core banking strategy—then AI can be truly helpful by 2030“.

Kurt Smith

• “The holy grail is always to get away from spreadsheets as much as possible… With AI-driven forecasting, the opportunities are the ability to leverage data for truly data-driven insights, run more what-if scenarios, and separate cause and effect from mere correlation.”
• “Stop focusing on financial markets and focus on using your expertise to improve commercial value… The board wants to know how you make the company a commercial success.“

Lourdes Antonio Luna

• “AI is already in TMS and ERP: data collection, auto-reconciliation… but integration across cash management, forecasting, working capital, supply chain, procurement is still a challenge.“

• “Key is scenario analysis not only for cash but also for risk. AI should be looked at as a holistic tool, not in silos.“