Large Bank Faces Challenges Implementing Advanced AI System
Even as cutting-edge artificial intelligence systems promise to revolutionize various sectors, they are not without their challenges. One of the largest global banks recently encountered difficulties while trying to implement an advanced AI enterprise software system, highlighting the complexities faced by heavily regulated organizations when trying to harness new technology.
Discussions between sales executives and customers reveal that the bank found it challenging to deploy the AI infrastructure it purchased. The AI system in question, known as the "AI Factory", combines advanced chips and software designed to create, train, and manage large AI systems for big businesses.
The Roadblocks of AI Adoption
The bank's struggle to fully utilize the AI technology underscores the wider difficulties that companies face when trying to incorporate such advanced systems into their operations. Despite the eagerness to invest in AI infrastructure, operational and regulatory barriers often make the actual deployment of these systems far more complex.
One executive likened the situation to selling a high-speed race car to local car mechanics. The company had sold the bank a powerful piece of technology, but now it must assist them in understanding and utilizing it effectively.
Bridging the Gap Between Acquisition and Deployment
Another executive highlighted the need to provide more than just the hardware when selling AI Factory systems. It is essential to provide a software solution that can help business customers succeed. The gap between acquiring AI infrastructure and actually implementing it is a common issue across various industries.
Experts have noted that while purchasing hardware or signing a cloud contract is a business decision, deploying AI is an institutional change. It's much easier to approve a budget than to revamp workflows, retrain teams, and rewrite governance processes.
Navigating Technical and Regulatory Hurdles
The bank's challenges in implementing the AI system were not just limited to the lack of in-house machine learning operations skills. The bank also expressed concerns about the readiness of the AI enterprise software for its highly regulated banking industry.
Other concerns included meeting the bank's stringent security and governance requirements, such as documentation and support for air gapping, which is a technique used to enhance security by isolating systems from other networks. The bank also faced the challenge of supporting multiple AI models and software systems to meet diverse needs.
Addressing Customer Concerns
Senior leaders at the tech company are known to step in when customer issues arise. The discussions surrounding the bank's struggle highlighted the need for the AI company to improve its product and offer better assistance to its clients.
This incident is not an isolated case. It mirrors earlier challenges faced by the tech company in educating prospective clients about what their software can and cannot do.
AI Deployment Challenges Across Sectors
The obstacles faced by the bank are not unique to the banking sector. These challenges are prevalent across various industries. While banks have a long history of using AI for tasks like credit decisioning, they may be the first to encounter such issues because of the sheer scale of their data and customers.
Experts believe that the technology is often far ahead of what individual banks or companies can quickly implement, highlighting the need for more education and support to bridge the gap between technology acquisition and deployment.