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Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
KPMG recently reported that financial institutions are increasingly leaning on AI to run their operations — 71% used it in 2024, citing ROI as the biggest gain. Most organizations are using the technology for financial reporting and are beginning to widen their scope to include treasury management, risk management, and tax.
However, the study’s results also show that financial institutions with smaller revenues vastly fall on the “beginner” side of maturity adoption — less progress in AI, genAI, and adoption in specific departments — rather than the “leader” — more advanced AI progress in the past six months — suggesting that smaller financial institutions like community banks and credit unions are struggling to master AI or go beyond surface-level use.
As AI agents enter mainstream consumption and tech companies deliver managed services for the financial sector, smaller banks begin to see more affordable solutions. This enables them to leverage the benefits of AI — from more efficiency to specialized applications and enhanced human touch.
A significant part of bank staff operations is drained in managerial tasks. Whether it’s checking documents, filling in paperwork, or communicating with users, these duties involve constant repetition and concentration. While automating workflows with recent AI advancements (usually through no-code solutions) has eliminated the need to repeat many tasks, it’s still not enough in areas like documentation intake and verification.
AI agents, a more specialized and scalable version of the AI we know, are now able to undertake these intensive tasks with only the necessary human intervention. Bank loans, inheritance transfers, and opening an account, among other customer-facing processes, can be completed through a mix of staff and AI agent interactions that prioritize speed and accuracy.
For example, these agents can be built in-house or through a vendor to focus on a specific task like document verification. While not every representative or bank teller might know what precise documents from each US state look like (IDs, death certificates, etc), an AI agent can specialize in this area, increasing the speed of document verification to seconds and spotting errors or potential discrepancies.
Giving time back to staff at small financial institutions by leaving mundane work to AI agents means they can do more of what their users like best about them: provide human connection. Many people join credit unions and favor their community bank because of the one-on-one approach they offer as opposed to larger banks. In fact, a shift in banking might be underway, according to the Wall Street Journal, motivated by people’s preferences for better customer service.
Leaving repetitive tasks to AI will allow bank staff to dedicate more time to bonding with clients, understanding their needs, and helping them solve more complex requests with more availability.
And this personal touch shouldn’t have to go away when adopting more modern solutions. For instance, dedicating AI agents to improve the customer experience is possible. Consider an agent that condenses and displays user data for new bank representatives to understand member context and preferences, ultimately offering a more comprehensive and tailored in-person service.
This can also be applied to online AI chatbots trained on anonymized data to add a new communication channel. Offering this digitized customer service option gives clients the power to choose between reaching out in-person or online, improving interactions for all kinds of users.
Properly adopting AI agents is all about creating meaningful change rather than modernizing for the sake of it. Success lies in finding and keeping a true north when hiring a vendor to work on scalable AI agents, ensuring these new tools fit the business objectives and tech stack, ultimately delivering client and staff value.
The advantage of scalable solutions like AI agents is small financial institutions can choose to launch as many as needed with as narrow or broad a focus as they wish, adjusting to their budget and demands. This is all about assessing areas of improvement internally first, then working your way up by tending to the most pressing solutions. Thankfully, whether implementing document verification agents or data summary support, these options are also more affordable than ever, due to tech companies developing managed services that have democratized sophisticated AI adoption.
Modernizing operations at small financial institutions boils down to finding a healthy balance between technology and the human touch these organizations are known for. The growth of AI has made it so that tailored tools are available for all, enhancing human connection while speeding up processes and improving their accuracy.
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