That shift marks an important turning point for the industry. Early AI efforts focused on testing models, launching pilots and identifying promising use cases. Today, firms are pushing AI into fraud detection, workflow automation, analytics and customer experience, according to exclusive data from CDW.
In the process, many are discovering that the biggest obstacle is not the AI model itself but the infrastructure supporting it. Institutions are struggling with data quality, integration challenges, governance and infrastructure readiness.
READ MORE: How data is being used to accelerate financial innovation.
Why Operational Readiness Determines AI Success
Those challenges help explain why cloud modernization has become such an important part of the AI conversation.
Aon’s reinsurance division, for example, is consolidating actuarial and claims data in Microsoft Azure SQL Database, enabling much faster processing for insurance risk modeling and replacing legacy CPU-based systems with cloud-based compute that can dramatically accelerate complex analyses.
That foundation also plays a critical role in security and governance, as our gathering of experts explained during our roundtable discussion. Cybersecurity leaders from Equifax, Fifth Third Bank and Five Star Bank describe how AI is increasing both the speed of attacks and the complexity of defending against them. Their responses — from AI-driven threat analysis to stronger identity controls and continuous monitoring — underscore that operational AI requires resilient infrastructure and governance as much as it requires innovation.
The institutions gaining the greatest advantage from AI are not simply experimenting with new tools. They are building scalable platforms capable of supporting AI securely, efficiently and at enterprise scale.
