IBM's Cohn: AI Moving From Experiment to Business Reality in Finance
IBM Vice Chairman Gary Cohn said artificial intelligence has shifted from a laboratory curiosity to a practical tool that financial firms are now using to cut costs and find new revenue. Speaking with Bloomberg, Cohn-who previously led Goldman Sachs and served as director of the U.S. National Economic Council-outlined how companies are moving past pilot projects to deploy AI at scale.
The transition hinges on solving a foundational problem: data management. Companies cannot extract value from AI models without the infrastructure to process and access data reliably, Cohn said.
Why Hybrid Cloud Matters for Finance
Cohn emphasized that financial firms need flexibility in where they store and process data. Hybrid cloud setups-mixing public cloud, private cloud, and on-premises systems-allow banks and asset managers to keep sensitive data in-house while accessing computational power elsewhere.
This matters in finance because regulatory requirements often dictate where data can live. A hybrid approach lets firms comply with rules while still using AI tools that demand significant computing resources.
IBM is positioning itself as a provider of the underlying infrastructure that makes this possible. The company integrates AI capabilities into its hybrid cloud offerings, allowing clients to deploy models across different environments without rebuilding systems.
The Workforce Question
Cohn acknowledged a real concern: AI will automate some jobs. But he pushed back on the idea that this is unprecedented. Previous technological shifts-from electricity to computing-eliminated certain roles while creating new ones.
The difference now is speed. Cohn said companies and governments need to invest in retraining programs so workers can move into emerging roles. IBM is investing in workforce development programs to prepare people to work alongside AI systems.
Cohn argued that the productivity gains from AI could drive broader economic growth. The catch: that only happens if societies plan for the transition and invest in education.
From Experimental to Operational
A few years ago, AI felt theoretical. Today it is embedded in routine business operations, producing measurable improvements in efficiency and decision-making, Cohn said.
This shift reflects better tools, clearer use cases, and growing confidence in AI's reliability. Companies are moving beyond asking whether AI works to asking how to scale it responsibly.
For finance professionals, the implication is direct: AI skills are becoming operational requirements, not optional. Understanding how to deploy and manage these systems is increasingly part of the job.
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