AI Limitations and Opportunities in Treasury (Valorean Technologies)

In this episode, Craig Jeffery and Arjun Krishnan discuss five key limitations of enterprise AI in treasury: data quality, pattern-based reasoning without true understanding, difficulty handling novel situations, variable outputs, and limited explainability. They also explore the opportunities and solutions behind these constraints, including AI-assisted data cleanup, anomaly detection, stronger controls, human oversight, and safer agent-based system design.

Enterprise AI for Treasury: A Guide to Agentic Implementation: https://amzn.to/4vTH8Fv
AI: The Automation Spectrum and Examples in Treasury (Valorean Technologies) (2026): https://strategictreasurer.com/420-ai-automation-spectrum-and-examples-in-treasury/
Valorean Technologies: https://valorean.ai/

Timestamps:
00:00 Introduction
00:34 AI limitations and opportunities
02:05 Data quality shapes AI quality
05:35 AI correlates but does not understand
09:29 Arjun Krishnan's background
10:18 AI and genuinely novel situations
12:37 Why AI outputs can vary
16:29 Explainability and audit trails
19:14 The most critical limitations
20:32 Final thoughts and book
20:54 Outro

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