The Next Frontier: Four Key Developments to Watch in AI for Finance in 2024

The Next Frontier: Four Key Developments to Watch in AI for Finance in 2024

Labeling 2023 as anything short of a transformative year for Artificial Intelligence (AI) would be an understatement. The world witnessed a paradigm shift from deep skepticism to significant interest, especially in the finance sector. The success of innovations like ChatGPT has catapulted AI beyond speculative debates, with industry influencers comparing its impact to the advent of the Internet. Forrester, a reputable research firm, emphasizes the urgency for businesses to embrace Generative AI, positioning it as a fulcrum for enhancing employee and customer engagement.

Among those espousing the latter is highly regarded research firm Forrester.

“It’s not the time to ‘wait and see’ how this technology shakes out. Generative AI is here. It’s up to you to discover its value,” Forrester noted in recent research looking ahead at 2024. “Generative AI will be the fulcrum that businesses rely on to enhance, empower, and engage employees and customers — with or without you.”
Over time, the move toward AI will increasingly be about increasing speed and productivity with minimal extra investment or effort and, of course, drive greater value for ownership, management, staff, and customers alike, said Bart Willemsen, VP Analyst at Gartner.

“In the fast-evolving age of artificial intelligence (AI), these innovations can help your organization build and protect itself while generating value,” Willemsen said. “Some are driven by AI; others help you to operate and grow effectively and safely as customer expectations and business models evolve with AI. You may have pioneered some of these technologies already; others may be new, but all help you establish the infrastructure, governance, and tools that your organization and its employees need as we move toward enhanced resilience and autonomic activities.”

So, with another leap deeper into the mainstream of businesses around the world on tap, here are some key developments to watch in 2024:

Prediction 1: The End of Window-Shopping – The Widespread AI Transition Begins in Finance

Despite the heightened interest in AI within the finance realm in 2023, many companies remained in the exploration phase rather than fully integrating its capabilities. While some forward-thinking financial leaders incorporated Generative AI into their toolboxes, the majority engaged in extensive research, attending conferences, and exploring AI-based solutions. This “window-shopping” phase is anticipated to culminate in 2024, with finance teams transitioning from mere consideration to actual implementation.

One financial executive at a large supplier of construction components based in the Midwest acknowledged to Emagia at a credit conference, “I have my whole team out at these things, gathering information on developers using automation and AI to boost efficiency, and they’re bringing everything they can about what’s out there back so that we can make moves in 2024.”

It’s a common refrain: 2024 will mark a significant shift from the number of finance teams considering AI solutions, as they were in 2023, and those with hard targets for implementation.

And as noted by Forrester and human resources firm UKG, among others, the clock is ticking. To wit, A recent study by human resources firm UKG found that 62% of financial leaders it polled are deeply worried that their respective companies are not moving fast enough to implement and/or use AI.
what's coming next in generative ai

Prediction 2: Democratization of AI

One of the frequent catchphrases heard around AI is the “democratizations of AI.” But what does that actually mean?

Simply put: it is making sure GenAI technologies are accessible to a larger, wider amount of people. Within the finance function, it means ensuring that various levels of the company beyond upper management have access and training not just, for example, the CFO and one layer below him or her. That, rather than focusing AI efforts merely on upper management functions, has a positive benefit for businesses that is tangible.

“Democratizing access to generative AI across the organization offers the potential to automate a broad range of tasks, boosting productivity, reducing costs and offering new opportunities for growth,” Gartner said in a late 2023 research report. “It has the ability to transform the way virtually all enterprises compete and do work.”

Now, it has been well-documented in recent years that CFOs, VPs of treasury, and B2B credit directors are no longer as much “back room” jobs as they once were. And that will be on full display as finance leaders will not just have to oversee the selection and implementation of AI-based technologies; they’ll have to get buy-in from the rank-and-file.

Communicating what AI and automation approaches a company is going to be using, how frequently, and a myriad of other parameters will be paramount to gain the full efficiencies that await. Those who were fast to implement aren’t doing an optimal job either by the sounds of it, according to coverage by CNBC and studies by firms including UKG.

A recent UKG sturdy found 54% of professionals were unaware how their employer is actually utilizing AI now or planning to in the future, and 75% would be quicker to buy into automation plans if the company showed more transparency regarding its AI policies going forward.

The elephant in the room remains the reality that professionals in finance and beyond are concerned that AI is going to take their job, perhaps quickly. It is the number one question Emagia representatives have been asked at finance and credit conferences throughout the second half of 2023.

For our part, Emagia CEO and Founder Veena Gundavelli has repeatedly explained that developers at this company have for more than a decade now been in the business of creating efficiency boosting AI-based solutions that can best be described as a co-pilot or a digital coworker. The point is to reduce time on mundane tasks so that time can be spent on decisions and actions that actually move the needle for a company. But most top-level GenAI products in finance will continue to require human interaction for the most important actions.

The impetus remains for CFOs and other finance leaders at companies to communicate that message up and down the staff.

Gartner recommends the following as a sort of “how to get started” guide to introducing near company-wide AI tools:

  • Create a prioritized matrix of generative AI use cases based on technical feasibility and tangible business value, and clearly outline a time frame for piloting, deployment, and production across these use cases.
  • Employ a change management approach that prioritizes employee training and well-being by equipping them with the knowledge to use generative AI tools safely and confidently, while reassuring them on how these tools will be an assistant to them in automating routine tasks.
  • Build a portfolio of quick wins and differentiating and transformational generative AI use cases that combine initiatives with hard ROI and those delivering benefits and competitive advantage that are difficult to initially quantify directly in financial terms.

Prediction 3: The Rise of Closed-Cloud AI Options

Ultimately, the surge in AI-based tech solutions promises unprecedented innovation and efficiency gains for businesses. However, concerns linger within the finance sector, particularly regarding the deployment of such solutions through open or public cloud environments.

One primary area of worry centers around data security and privacy. Finance professionals handle sensitive and confidential information, ranging from proprietary data to customer details. The open or public cloud, with its shared infrastructure, raises concerns about the potential exposure of critical financial information to unauthorized entities. Finance professionals are rightfully cautious about entrusting such sensitive data to cloud environments that may lack the stringent controls required to mitigate security risks adequately.

Additionally, all the usual regulatory burdens of being in the finance function of a business remain in play.

Plus, with open-cloud solutions like ChatGPT, information of varying levels of quality or accuracy can be drawn in the process, calling into question the reliability of information gleaned from such sources at time.

This amplifies the appeal of more close-cloud solutions among financial professionals, particularly CFOs. The distinctive advantages offered by close-cloud solutions directly address the nuanced apprehensions that finance leaders harbor in the realm of data security, regulatory compliance, and operational reliability.

With closed-cloud solutions, more businesses would rely only on information or data chosen by the company. It’s one of the hallmarks of Emagia’s GiaGPT product – which calls on users to upload their own financial documents, data, or spreadsheets from which the AI-powered solution will draw insights from. Nothing from the outside world is considered, only the user’s own, quickly uploaded information that is not shared on public drives.

Prediction 4: Crowded Marketplace

Make no mistake: big spending on AI is coming and in various forms (e.g., purchase of outside solutions, development of internal AI systems, etc.).

For example, Deloitte estimated that enterprise spending on Generative AI will grow by 30%.

“We predict almost all enterprise software companies will embed generative AI in at least some of their products this year,” Deloitte speculated in a recent report. “We predict that the revenue uplift for enterprise software companies will be at a $10 billion run rate by the end of 2024. That is lower than some estimates, but still notable for the first year of a new market.”

But like any hot trend, the high levels of opportunity will attract fly-by-night companies that will crowd a market already containing firms with expertise or experience in AI. This proliferation of options will present a challenge for businesses beginning their search for providers without full knowledge of reputations or best practices therein.

Moreover, the scalability and integration of AI solutions within existing business ecosystems pose yet more layers of complexity. Businesses must evaluate the adaptability of AI technologies to their current infrastructure, ensuring a streamlined integration that enhances rather than disrupts existing workflows. This consideration includes factors such as data compatibility, interoperability with other technologies, and the potential for seamless collaboration with human workers.

In navigating this landscape, businesses also face the challenge of assessing the reliability and performance of AI solutions. The rapid pace of development can result in varying levels of maturity among different offerings, making it imperative for organizations to conduct thorough evaluations of the robustness, accuracy, and security features of potential AI tools. This process demands a nuanced understanding of the vendor landscape, requiring businesses to distinguish between well-established providers and emerging players.

The path to embracing and implementing AI without business in the U.S. and abroad is paved with the challenge of making informed choices in what will becoming an increasingly crowded marketplace. Organizations must strategically navigate this landscape, considering the specificity of their needs, the compatibility with existing infrastructure, and the reliability of the AI solutions to ensure a seamless integration that optimizes business processes and drives tangible value.

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