Generative AI In Finance: Is The Market Ready?

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Sunil Rajasekar is CEO of Billtrust.

The business world has always been quick to embrace transformative technologies, from the dawn of the internet to the widespread adoption of cloud computing. However, few innovations have the potential to reshape industries as profoundly as generative AI.

Initially garnering attention for its speculative promise, this technology has rapidly evolved into a powerful force for innovation, seamlessly integrating into various sectors. Financial management and strategic planning stand out as prime examples where generative AI can provide significant advantages to organizations and financial leaders—provided it’s adopted thoughtfully.

But what is the best way to adopt generative AI in finance—an industry I believe to be the last bastion of manual processes in a corporation?

Generative AI—The Early Innings

When my company, Billtrust, recently surveyed 375 CFOs across industries, the results highlighted the rapid adoption of generative AI within the finance function. In fact, 65% of financial leaders are currently integrating generative AI into their strategies, marking a dramatic increase in adoption rates within a remarkably short period. Among the areas where they’re applying it to guide, recommend, or personalize actions or insights are financial transaction processing (77%), risk assessment and management (65%), accounts receivable and collections (44%), invoicing and billing (37%), payment reconciliation (37%) and cash flow management (36%). Amid this embrace, however, is a notable juxtaposition: Only around half (49%) claim to be very knowledgeable about it.

This discrepancy between the adoption of generative AI and CFOs’ understanding of it isn’t all that surprising. It is, after all, very early innings for its implementation in B2B companies. The market is adopting it but at a slower pace than its B2C counterpart. It’s also doing so in an environment where the stakes are higher, and collaboration with customers is needed to alleviate common data/privacy concerns (more on that later).

But it also suggests that its implementation demands a mindful and phased approach. In my experience, finance is generally the last department resistant to complete tech transformation. And while this underscores generative AI’s immense potential in the space, it also highlights the need to tread cautiously so organizations don’t overrun their expertise. In fact, 34% of CFOs admit to believing it will take the next generation of finance leaders to fully scale the implementation of generative AI in finance!

This hesitation is evidence of the need to be mindful of guardrails.

Navigating The Challenges Of Change Management

Integrating generative AI into financial operations demands a meticulous approach tailored to an organization’s needs and objectives. Paramount to this is safeguarding data privacy and security, necessitating stringent measures to counteract evolving cyber threats. For example, a global financial institution seeking to implement generative AI in its payment processing system would prioritize encrypting sensitive customer data and implementing multifactor authentication protocols to mitigate security risks.

This involves not only tailoring the technology to enhance current processes without major disruption but also preparing teams for the change. Take, for instance, a supplier in the manufacturing industry leveraging generative AI to optimize its invoicing and collections processes. A well-defined implementation strategy, supported by training and clear communication, will facilitate a smoother transition, enabling teams to leverage generative AI’s capabilities to the fullest.

Education is also important. Getting stakeholders on board with AI requires a concerted effort to explain its benefits, functionalities and associated risks. Through targeted training sessions and accessible resources, finance teams can grasp AI’s transformative power and its potential to reshape not just individual tasks, but the entire business landscape.

Balancing considerations of security, efficiency and team readiness is essential for success. By acknowledging these requirements and strategizing accordingly, organizations can navigate the complexities of adoption and unlock the transformative potential of generative AI in financial management.

The Way Forward—Embrace Low-Hanging Fruit

My advice for businesses looking to get started with generative AI is to go after what’s readily attainable: the low-hanging fruit. By targeting known problems and areas where AI can yield immediate wins—such as customer support, sales, marketing and anomaly detection in payments—organizations can kick-start their journey toward enhanced efficiency and informed decision making.

Streamlining processes and reducing manual overhead form the crux of this approach. By leveraging AI to automate repetitive tasks and augment human capabilities, businesses can unlock newfound agility and productivity. This not only accelerates operations but also lays the groundwork for scaling AI integration across more complex workflows.

Furthermore, embracing cross-domain and cross-functional applications of AI holds immense potential. Imagine AI systems seamlessly conversing across different facets of finance, from payments to collections, unlocking synergies and driving efficiencies at every turn. While we’re not quite there yet, laying the groundwork now sets the stage for future innovation and collaboration.

Fortunately, our study of CFOs has already yielded success stories that show the power of the technology in finance: 49% of executives report enhanced decision making since adopting generative AI, 76% acknowledge increased operational efficiencies, and more than a third attribute a positive revenue growth trajectory to their use of generative AI.

These statistics underscore the technology’s role not just in streamlining tasks, but as a strategic asset crucial for financial growth. Of course, this requires a well-thought-out adoption approach.


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