Guest Editorial by Jake Tyler, AI Market Lead for Glia
Generative AI and Large Language Models (LLMs) continue to create buzz around the technology’s potential to transform business processes and the member experience, causing credit unions to rush to determine how to best deploy AI tools and where to begin. An obvious starting point is leveraging AI to support member service, delivering valuable co-pilots to agents, deeper insights and reporting to managers and a better, faster experience to members themselves.
However, several important questions must first be answered, such as: Which solutions deliver real results? Which ones can be launched quickly, without extensive setup and complex integration efforts? And perhaps most notably, how safe and secure are these systems?
While AI presents a significant opportunity for quick innovation and streamlined operations, it is critical for credit unions to find ways to separate what is real versus hype and ensure the technology is implemented responsibly. With several high-profile stories about generative AI missteps that have resulted in material reputation or business damage, the stakes are simply too high in financial services. There is no room for trial-and-error learning; credit unions cannot risk losing their reputation as the trusted protectors of members’ money and finances.
The good news is, the safety, privacy and business and reputation risks in adopting AI can be addressed with a responsible approach. Responsible AI offers guardrails, guidelines and control to credit unions throughout member interactions, mitigating the common issues and concerns associated with traditional generative AI. This approach is made up of three key components: AI that is safe, proven and turnkey.
First, responsible AI must be safe, ensuring that no information ever leaves the platform and with proper protections embedded into the infrastructure. The information should be encrypted to facilitate private and secure data transfers – and data should never be used to train foundational models. Instead, curated, approved datasets relevant to credit unions’ operations and regulatory requirements should be leveraged, automatically redacting sensitive information before AI processing.
Credit unions should also prioritize an out-of-the-box, purpose-built AI with simple set-up, training and use. In other words, a turnkey solution that is pre-built specifically for financial services. This ensures that the technology is built with the complexities of banking in mind and has the proper practical uses, workflows, content management, reports and insights in place. And responsible AI should be proven, solving real problems and providing meaningful value, such as productivity and efficiency gains, out of the gate.
When approached responsibly, AI has the power to redefine the service experience for agents, managers and members alike. For example, AI has been proven to boost staff productivity by 20% because it can train frontline employees in minutes, streamline workflows and lower handle times. AI-powered virtual assistants can also automate up to 65% of member interactions across phone and digital channels, helping improve efficiency, reduce wait times and enable staff to focus more on complex tasks, all while boosting member satisfaction. Plus, managers gain deeper insights about the member service experience and agent performance, helping them make more informed business decisions.
The interest around AI will only continue to grow; after all, the technology is poised to transform the member and agent experience. As credit unions determine how to incorporate AI across their organization, it will be critical to prioritize doing so responsibly. With an abundance of AI technologies and providers available, credit unions that prioritize the implementation of safe, turnkey and proven responsible AI tools will be strongly positioned to provide immediate value for members, agents and managers – all while protecting their organization and members.
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