Parsing the hype, hopes, and practical applications of AI across finance solutions
- Is AI in finance a pot of gold at the end of the rainbow? Zoho's Raju Vegesna does some myth-busting and explores the the middle ground where AI has a useful place amid the hype.
Over the last few years, AI has fully woven itself into the fabric of finance software. It can be found supplementing security features such as fraud detection, screenings for loan eligibility, and automatic invoice capture. Sure, companies with large technology budgets can enjoy AI's time-saving and streamlining elements, but many software vendors, particularly cloud-based ones, are starting to include AI as a standard feature even in their more affordable products.
Still, many finance leaders remain skeptical. AI has been hailed as a game-changer with the potential to revolutionize the way accounting and finance teams approach their work and job responsibilities. At times, that potential has crossed over into hype — this idea that eventually AI will abstract all daily manual back-office functions, and entire systems can be self-managed with adaptive and empirical precision.
Sounds downright magical, but the truth more closely resembles a wish than a promise. Full autonomous decision-making is quite far away and, despite improved accessibility to the software, adoption — the true measure of success — remains a mixed bag. Some businesses are unable, or unwilling, to implement, maximize, and develop AI technology. Perhaps they lack IT support and funding, even if the use cases they are solving for are generally less complex. Many times, departments dealing in sensitive financial information are particularly concerned with data privacy and security, making them wary of cloud solutions (where AI often resides). Other times, new software puts off old pros, who worry their teams have neither the time, resources, nor the capability to customize and adapt to an entirely different system.
Striking the right balance with AI in finance
These are valid reasons for caution, but there is still a strong case for companies to introduce more AI functionality into their existing systems. AI's disruptive potential is less about revolutionizing the workplace and more about increasing the efficiency and accuracy of routine, mundane, and occasionally invisible tasks. Now is a good time for finance teams to start thinking about how AI can automate their tasks — well beyond the basics of anomaly detection.
In accounting, for example, integrated suites with automation capabilities can automatically sync an update to an invoice in one app with a connected central bookkeeping system, and the change will be reflected in future reimbursement. AI-enabled bank matching automatically confirms and connects corporate card purchases to invoice amounts, while object detection enables easier and more accurate receipt scanning. On the back end, these are complicated coordinated processes, which, using the right tools, can be accomplished without risk to data security, heavy IT involvement or customization, or the most sinister, existential risk to human involvement in back-office finance work.
Here lies the true nature of AI: humans remain an integral factor in its success. Take security — one of the main areas of concern. It's one thing to safeguard against threats passively but another to establish secure processes to proactively guard against breaches. By adopting a blended approach that includes both human and machine interaction, employees can address critical vulnerabilities that have been triggered through an automated process. Within unified systems that unite a company's finance apps under one vendor's umbrella, security software is automatically updated across the broader ecosystem, while employees maintain the ability to monitor all apps from within any individual one.
Over time, AI learns how to parse incoming data and populate the corresponding fields within the unified finance ecosystem. While this endeavor typically requires a wealth of data — which some companies simply might not have — AI has been advancing its ability to learn from limited data sets just as effectively as it would with larger volumes of data.
Part of the bigger picture
Smarter, more automated finance software enables all employees, even non-customer-facing ones, to remain customer-focused. Via chat portals, automated invoice processing, and machine learning, employees can receive updated data and leverage their software platform to better serve existing customers and ensure a smooth process for onboarding new ones. It helps no one if a member of the finance team sits within a silo, working with theoretical numbers for a fictional customer persona.
AI cannot fully replace human employees, nor should it. Yet, much of the excitement and apprehension around AI presumes it will do just that. It's up to finance teams to slot AI/ML services into pieces of the business that lack some muscle. Yes, the technology has been over-hyped as a back-office panacea, but skeptics shouldn't let hype dissuade them from the practical productivity and efficiency benefits of automation and AI/ML functionality in finance.