Summary
Financial infrastructure company SEI has teamed up with IBM to update its internal operations using advanced artificial intelligence. This partnership focuses on using "agentic AI" to handle repetitive tasks and improve how the company manages data. By fixing old systems and using smart automation, SEI aims to provide a better experience for its clients while making its own work processes much faster. This move highlights a growing trend in the finance world where companies must clean up their data before they can successfully use new technology.
Main Impact
The primary impact of this collaboration is a major boost in operational speed and accuracy. By integrating intelligent agents into their daily work, financial firms can change how they handle large amounts of information. Instead of staff spending hours on manual data entry, AI tools can take over these routine jobs. This change allows the company to operate more efficiently and reduces the chance of human error. For the broader finance industry, this project serves as a model for how to move away from old, slow methods toward a modern, data-driven approach.
Key Details
What Happened
SEI is working closely with IBM Consulting to redesign its business processes. The project starts with a deep look at how SEI currently works. Experts from both companies are checking the firm's data structure and daily routines to find areas where AI can help the most. They are using a specific technical system called the IBM Enterprise Advantage platform. This platform serves as the base for building and launching AI tools that can make decisions and help employees work better. The goal is to ensure these AI "agents" work within safe boundaries while meeting the specific needs of the financial market.
Important Numbers and Facts
Research shows that when financial institutions use automation for basic tasks and data entry, they can cut down processing times by as much as 40 percent. This is a significant amount of time saved, which can then be used for more important work. The project also focuses heavily on "data hygiene," which means making sure all information is clean, organized, and correct. Without high-quality data, AI models can make mistakes or provide wrong answers. By focusing on these details, SEI and IBM are building a system that is both fast and reliable.
Background and Context
In the world of finance, many companies still rely on older computer systems that were built decades ago. These systems often do not work well with modern AI tools. Simply adding new software on top of a broken system usually leads to failure. This is why SEI and IBM are starting with an audit of existing workflows. They want to make sure the foundation is strong before they start using advanced AI. In a highly regulated industry like finance, following rules and managing risks is vital. Using AI requires a careful balance between innovation and safety to protect client information and follow the law.
Public or Industry Reaction
Leaders at both companies believe this is a necessary step for future growth. Sean Denham, a top executive at SEI, mentioned that investing in how the company operates is just as important as the products they sell. He noted that by using AI to handle boring tasks, employees can focus on building stronger relationships with clients and growing their own careers. Glenn Finch from IBM Consulting added that SEI’s deep knowledge of the finance industry, combined with IBM’s tech skills, will help the firm stand out in a competitive market. Industry experts see this as a sign that "agentic AI" is becoming a standard tool for large financial organizations.
What This Means Going Forward
As SEI rolls out these AI tools, the role of the human worker will likely change. Instead of being data processors, employees will become managers of AI systems and focus on solving complex problems for clients. This shift will require staff to learn new skills, but it also removes the most tedious parts of their jobs. For the rest of the finance sector, the success of this project will likely encourage more firms to invest in similar technology. We can expect to see more "intelligent agents" handling customer service, fraud detection, and basic accounting in the coming years. The focus will remain on keeping data clean and ensuring that AI always has human oversight to prevent errors.
Final Take
The partnership between SEI and IBM shows that the future of finance is not just about having the best AI, but about having the best data. By taking the time to fix old processes and organize their information, SEI is setting itself up for long-term success. This approach proves that when technology is used correctly, it does not just replace human effort—it makes human work more valuable. Companies that embrace this change will likely lead the market, while those that stick to manual methods may find it hard to keep up.
Frequently Asked Questions
What is agentic AI in finance?
Agentic AI refers to intelligent software tools that can perform specific tasks on their own. In finance, these agents can handle things like data entry, answering basic client questions, and organizing financial records without needing constant human help.
How does automation help financial workers?
Automation takes over repetitive and boring tasks, such as typing in data or checking simple forms. This frees up employees to focus on more important work, like helping clients with complex problems and building better business relationships.
Why is clean data important for AI?
AI models learn and make decisions based on the information they are given. If the data is messy or incorrect, the AI will make mistakes. Clean data ensures that the AI works accurately and follows financial regulations safely.