By Sarju Raja, Global Account Director at AvePoint, looks at the challenges facing financial institutions when implementing AI
A recent survey of global finance leaders from Workday and the Financial Times discovered that almost 40% of all leaders and a much higher percentage early-adopters believe that AI will be a gamechanger in their industry. The report indicates that use cases for AI in finance and FinTech are varied, but 34% of executives say that AI will help improve forecasting and budgeting decisions, while 32% say it will help improve strategic planning support.
Still, even as adoption ramps up, many financial institutions face barriers to successful AI implementation, with the quality and integrity of data now a growing concern. The 2024 AI and Information Management Report found, for example, that organizations with mature information management strategies are 1.5 times more likely to realize the full potential of AI. This spells trouble for many in finance as 63% of companies in the sector have data that is somewhat or completely siloed. Disconnected data can harm the effectiveness of AI tools, and with the cost of compliance with the EU AI Act looming, EU-based organizations could also face monetary consequences for lax data management (according to recent reports, a UK AI act is in the works).
In this piece, I’ll look at some of the big challenges that financial institutions often face when implementing AI. I’ll also share some strategies that leaders can use to minimize risk and get the most out of their AI investments.
Data Exposure During Implementation is Common and Costly
AI tools like Copilot for Microsoft 365 can be powerful additions to any workplace, but it’s important to remember that they rely on large amounts of sensitive information. Organizations—particularly those in the finance sector—must safeguard this information constantly and should take particular care to avoid exposures during implementation, when sensitive information can be particularly vulnerable.
The AI and Information Management Report found, for example, that 46% of AI-adopting organizations experienced unwanted data exposure when implementing AI, which exposes companies to substantial new financial risks. Given the sensitive nature of data in the finance industry, the need to limit data exposure is especially important.
The data risks associated with AI implementation also help explain why the EU (and potentially the UK) have sought to implement sweeping new AI regulations that impact the financial services sector. The EU AI Act, for example, classifies AI according to risk and limits usage accordingly, with the highest-risk activities receiving the most regulatory control. Because organizations in the financial services sector handle highly sensitive data, leaders should expect regulation of AI in their industries to be stringent.
Under the EU AI Act, AI-enabled FinTech falls into the regulator’s highest risk category, which means that users and vendors of AI FinTech will have to comply with significant new regulations, including continuous risk management, enhanced documentation, and more. While US and UK regulators have historically given more leeway to technology innovators, finance leaders in the UK and other western jurisdictions should still expect their industry to be one of the most heavily impacted by any pending or forthcoming AI regulation. The force of these new regulations puts an added imperative on financial services organizations to safeguard sensitive data before, during, and after the switch to AI.
Ongoing Data Governance is Non-Negotiable
In the age of AI, strong data governance is non-negotiable, due both to its importance to AI success, and due to the rapidly evolving regulatory environment. According to the AI and Information Management Report, for example, ¾ of organizations believe that they need a new information management and data governance strategy. Naturally, finance is not exempt from these changes. In fact, due to the wide-reaching scope of the sector and its importance to the global economy, financial services organizations will have to be particularly deliberate and proactive about implementing new data governance strategies to deal with the challenges of AI.
As AI continues to take hold around the financial services industry, leaders will have to work within their organizations to (1) establish clear data governance policies that align with shifting regulatory frameworks, (2) implement those policies, and (3) educate and train employees to help them work effectively. By following this path, organizations in the financial services industry can help limit risk, assure regulatory compliance, and achieve their business goals. But, at the same time, it’s important to acknowledge that strategies will have to be nimble. AI is evolving quickly, and strategies should be subject to change.
Drive Success with Strong Training and Enablement Programs
When a new technology enters the workplace, it often takes time to integrate it effectively into existing workflows, and AI is no exception. To get the most out of AI, finance leaders need to make sure that their people have the knowledge and skills required to use AI-enabled FinTech. It’s also important to champion the technology and its adopters within the organization. Training and enablement programs are crucial to overall AI success.
Still, in spite of the urgent need to arm employees with the skills to use this new technology, only 46% of companies say that they currently offer training on AI to their workforce. As AI use continues to increase—80% of organizations say that they plan to expand their use of AI in the coming year—finance leaders will have to invest more time and resources in training. Organizations should view these costs as part of a broader upfront investment that’s needed to ensure long-term success with AI-enabled FinTech. Investments in training now should greatly increase productivity down the road. There are some signs that investments in training are getting on the right track, however; organizations reported that they plan to spend 40% of their AI budgets on training in the coming year.
Ultimately, financial institutions have a lot riding on successful AI implementation. With new AI innovations coming almost every quarter, agility and flexibility will become paramount. Leaders can give themselves freedom to act by improving the quality, security, and integrity of the enterprise data that powers AI.
