Automation » The slow march of AI in finance: A contrast to potential and reality?

The slow march of AI in finance: A contrast to potential and reality?

The finance sector's slow adoption of AI stands in stark contrast to its vast potential, as revealed by Gartner's latest survey and illustrated through pioneering examples from industry participants

The world of finance is at a crossroads with AI.

A recent survey by Gartner highlighted a significant gap between the potential of AI in finance and its current utilisation: 61% of finance functions are either not planning or are only in the initial stages of AI implementation, trailing behind other administrative functions such as HR and IT.

This data comes from a survey conducted in June 2023, which included 130 finance leaders and 91 associates across various administrative support functions. The findings are particularly striking as only 9% of finance organisations are actively scaling and using AI technologies, in contrast to 20% in other sectors??.

The unfulfilled promise

The promise of AI in finance is grand.

Earlier this month, experts from the Hackett Group noted generative AI could dramatically alter the landscape of finance operations. By harnessing AI, finance departments could potentially see a 40% reduction in both SG&A costs and staff in the next five to seven years.

This significant reduction underscores the power of AI in streamlining processes and cutting operational costs.

The potential of AI in finance is further highlighted by its diverse applications, including accounting support, anomaly/error detection, and financial analysis. Such varied uses within finance show that AI’s impact is not limited to a few areas but spans the entire spectrum of financial operations.

Early successes and future potential

AI’s role in finance has already seen some successes. Teams leveraging AI have demonstrated remarkable efficiency and cost reductions. These teams have managed to function at 47% lower operational costs compared to their peers and have achieved a reduction in operational costs of 1.3%.

One of the earlier adopters was JP Morgan Chase; in 2017, the bank implemented an AI-powered contract analysis system that is able to analyse legal documents and extract important clauses, such as termination clauses and non-disclosure agreements, with an accuracy rate of 95%.

The system has significantly improved the bank’s efficiency in reviewing and managing contracts, reducing the time required for manual review by approximately 360,000 hours per year.

Similarly, research from EY noted a major US retailer combined data from points of sales, inventory, and pricing to create a ‘war room’ for the holiday season. Using AI analytics, the finance team worked with the retail outlets to create dynamic to-the-minute pricing that maximized the balance between revenues and unit sales.

 Oracle NetSuite’s AI-driven solutions, showcased at SuiteWorld 2023, also highlighted the potential of AI in finance, offering tools for real-time insights, automated processes, and supporting global growth.

Barriers to adoption

However, the adoption of AI in finance is not without its challenges. The primary obstacle to AI adoption in finance is not technology or cost, but other organizational priorities. Finance leaders cite reasons such as lack of technical capabilities, poor quality data, and insufficient use cases for their reluctance to invest in AI.

This perspective is echoed in the insights provided by Eric Emans, CFO of Nintex, who emphasises the importance of having fully optimised processes before adopting AI. Rushing into AI adoption without proper groundwork can pose significant risks.

“AI is a seriously impressive technology that will transform finance processes and how organisations approach risk management. But the stakes for not getting the AI recipe right are high,” he wrote. “Especially within finance departments where the well-being of an organisation rests on the ability of teams to predict financial conditions accurately.”

Additionally, the quality of data and employee upskilling are critical for successful AI integration. Employees and management must fully understand the technology and how it can improve their day-to-day tasks to see positive results. Implementing AI too quickly may mean finance teams lack the skills and understanding required to avoid critical mistakes and consequences in using AI.

Additionally, without proper training, employees may feel threatened by the pace of new technology and are more likely to resist rapid changes. CFOs must find ways to keep human interests at the centre of AI implementation through upskilling initiatives that help employees become more efficient using AI. Securing employee buy-in will also allow AI initiatives to scale throughout the department.

Ensuring that finance teams are prepared and have the necessary skills to utilise AI is crucial to avoid critical mistakes and maximise the benefits of AI????.

The ‘human’ element in AI integration

Balancing the technological advancement with the human element is key to successful AI integration. Rene Ho from Taulia highlights the importance of balancing automation with human touch, especially in sensitive areas like fraud detection.

“AI can help speed up the process of gathering and processing information for credit decisions, but the final decision is still made by a human,” he says.

 In 2014, PayPal implemented an AI-powered fraud detection system that uses machine learning algorithms to analyse transactions and identify fraudulent activity in real-time.

The system has been highly effective, reducing fraudulent transactions by 40%, saving PayPal approximately $700 million in losses in the first two years after implementation. This has enabled the company to provide better services to its clients while reducing costs and improving efficiency.

Moreover, the fear of AI replacing jobs necessitates CFOs to position AI as a collaborative tool rather than a replacement. This approach helps in securing employee buy-in and ensures effective use of AI.

It is crucial for operational staff to be involved in designing AI solutions and for managers and leaders to understand where AI fits strategically in the organisation. This collaborative approach can help in overcoming barriers to AI adoption and ensure that AI delivers the transformative impact that finance leaders expect.

Will the status quo change?

The current state of AI in finance, as reported by Gartner, shows a sector still hesitant to fully embrace AI, despite its significant potential benefits. The challenge for CFOs lies in overcoming barriers to adoption and effectively integrating AI into finance functions as a tool for efficiency and strategic decision-making.

By balancing technological advancement with the human element, and ensuring proper groundwork and upskilling, the finance sector can harness the full potential of AI, transforming not just their operations but also their strategic impact in the wider business landscape.

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