If you lead a long-running business, you’ll likely have to renew your line of credit at some point. Despite its commonality, this process can be quite laborious. You’ll have to provide stacks of information to your bank, such as relevant financial details and tax returns. All these documents then go to an underwriter who has little firsthand experience with running a business. Due to this difference in perspective, they may not know the best questions to ask when reviewing your business information. This can lead to a frustrating back-and-forth between banks and business owners.
Even in the age of AI, manual processes like these remain in the business world and represent a stark difference from the science-fiction-worthy visions of tech CEOs. In May, Anthropic CEO Dario Amodei claimed that AI could wipe out half of all white-collar jobs within the next five years. In September, OpenAI CEO Sam Altman wrote that, because of AI technology advancements, “in the future, everyone’s lives can be better than anyone’s life is now.” Some are even calling AI the fourth Industrial Revolution, harkening back to the era that fundamentally transformed the nature of work.
Yet for all the talk of AI replacing the human workforce, the aforementioned loan underwriter is likely to have a job for many years to come. Why? There’s a ton of organizational work that the bank needs to perform before a process like loan underwriting can be automated or even AI-assisted.
This is largely due to data silos and organizational barriers. Banks often don’t store data in an integrated way that AI agents can parse. This creates a need for human employees to step in and also adds potentially frustrating steps to the process for clients.
What needs to happen?
Before AI can cash in on its promise, companies must first invest in thinking through these data strategies. This presents millions of dollars of work to ultimately streamline the process with AI. The work could look like breaking down data silos, enhancing and maintaining security, and integrating legacy technologies. All of these less glamorous undertakings are necessary predecessors to reaching the level of AI integration that tech CEOs are boldly promising.
For many corporations, their current AI adoption strategy looks simply like giving employees access to Claude or ChatGPT. It often doesn’t mean thinking critically about how this technology can be integrated in the long-term to streamline time-consuming, labor-intensive processes. Despite the massive investment that corporations have put into AI, a recent report by MIT researchers found that only 5% of businesses integrating generative AI succeeded in the “rapid revenue acceleration” they hoped for.
Because of this, clients end up not feeling the impact of AI, even if a corporation has implemented it in some way. AI-assisted chatbots, a common venue where clients may interact with a firm’s AI, often run into the same data silos and barriers and must refer clients to a human representative.
But there are plenty of openings for meaningful AI integration to improve efficiency, security, and the client experience. For example, J.P. Morgan Chase has implemented the technology for fraud detection. In April, the firm launched the NeuroShield system, which uses AI to recognize user behavior and patterns and better recognize suspicious transactions. The system boasted a 40% reduction in fraudulent transactions in testing. As technology improves, scams also become more intricate; it is vital that banks adopt innovative technology like NeuroShield to crack down on cybercriminals. J.P. Morgan Chase’s clients can rest assured that they have access to top-of-the-line fraud protection.
Corporations need not shy away from AI implementation, but they should proceed thoughtfully. With a technology that’s developing every day, the true measure of success won’t be which firm can integrate the most flashy AI tools right away. Rather, long-term winners will emerge down the line, and it’s likely these will be the firms that carefully consider how to best implement AI into their workflows, making the technology work for them and their clients rather than shifting their entire business model to suit a new venture that’s still finding its footing.