More than a year after OpenAI’s release of ChatGPT, the topic of artificial intelligence continues to be ubiquitous and its growth shows no signs of stopping. According to
Despite this exponential growth, AI has had a slower takeoff within finance and accounting — largely due to concerns about data security and its potential to change the ways employees currently work. This leaves significant room for finance professionals to explore the industry-specific benefits of AI, from support with reporting and metrics to updating employees on tax regulations and accounting standards.
Accounting applications of generative AI
One of the most celebrated benefits of generative AI is its ability to increase efficiency through the automation of manual tasks. For example, drafting client communications has long been viewed as one of the more tedious and time-consuming tasks left to finance professionals. However, these composition tasks can now be delegated to AI, offering employees a starting point that they can refine, rather than starting from scratch.
Along with other document management tasks — such as creating reusable templates for invoices, emails, purchase orders and expense forms — generative AI models offer accounting professionals a wealth of organization- and industry-specific assistance. Accountants can consult the platform for strategies related to optimizing early payment discount opportunities, improving accounts payable workflows, implementing internal fraud prevention controls or outlining steps and requirements for month- and year-end closing procedures. With its ability to explain content at various levels, ChatGPT can even serve as a cost-effective training and education resource for human employees.
It’s important to note that, although AI can provide finance professionals with vital information, it does not possess the capability to make critical financial decisions without human oversight. A clear example of this is in credit management: While artificial intelligence and predictive analytics can provide supplementary data regarding a customer’s propensity to pay on time, it cannot decide who should and should not receive credit terms. These functions continue to require the judgment of a human who can factor in additional issues, such as external economic factors and industry standards — underscoring the role of AI as a means for augmenting and supporting the efforts of human employees, rather than replacing them outright.
AI and workplace morale
Along with increasing overall efficiency, leveraging the benefits of generative AI has the potential to improve the workplace experiences of finance professionals. By automating tasks and implementing predictive analytics, AI streamlines, or even eliminates, the need for tedious manual work that might otherwise contribute to employee burnout.
With AI handling routine emails and other time-consuming tasks, AR reps can spend more of their time reaching out to high-value and high-risk customers, in addition to working personally with accounts that are struggling to make payments on a plan to get their account current. This, in turn, leads to improvements in customer experience: by studying past communications, AI can suggest the best methods and times for contacting individual accounts, enabling more personalized service overall.
Strategies for crafting effective prompts
Generative AI is still only as effective as the human input used to prompt it, but there are a few best practices finance professionals can follow to ensure they’re receiving the best possible output. It’s especially important to use clear and specific language in prompts or requests submitted to AI, since ambiguous wording can yield vague or irrelevant responses. Open-ended questions also tend to be more effective than those requiring a yes or no (e.g., “Why is the three-way matching process important in AP?” instead of “Is three-way matching important?”).
Stating your role and industry as the person asking the question can also be helpful when prompting generative AI, since this kind of contextual information helps the model tailor its response more specifically to your needs. You’ll also want to specify the format the response should take, such as a list, email or invoice. Data privacy and security are also important considerations throughout the drafting process. If you anticipate needing to include sensitive data in your AI prompts, it may be necessary to instate a company-wide AI use policy and upgrade to an enterprise account with enhanced security features.
However, there’s also a tricky balance between the amount of detail you include and the length of your prompt. Increasing the amount of contextual information is helpful only up to a point, beyond which the AI model may get lost in the details.
The following sample prompts strike this balance effectively and demonstrate the wide range of tasks that can be supported by AI:
Vendor management: “How can we evaluate vendor performance and negotiate better terms?” or “Draft three variations of a vendor inquiry email regarding a payment discrepancy.”
Invoice processing: “Can you categorize expenses from a credit card statement if I provide you with vendors’ names and amounts?” or “Provide guidelines for verifying and approving invoices for payment.”
Tax compliance: “What are the key tax compliance considerations for AP accountants?” or “Help me understand how to handle international invoices and VAT.”
Payment reminders: “Write three dunning emails with a polite but firm tone: one for when an invoice is 30 days past due, one for when an invoice is 60 days past due and one for when an invoice is 90 days past due.”
Data analysis: “What are the key performance indicators (KPIs) to monitor in accounts receivable?” or “Why is [insert metric] important in AR?”
Software upgrades: “Recommend accounting software or tools for improving AP efficiency” or “Provide insights on implementing automation in the AP department.”
Regulatory compliance: “I work in AP. Inform me about relevant recent changes in accounting standards” or “How can we ensure compliance with financial regulations in our AP operations?”
There’s no question that AI is here to stay, and its ability to optimize efficiency and maintain cash flow will only increase in coming years. For finance professionals who embrace it, AI will continue to provide a critical edge that not only improves their organization’s bottom line, but also leads to marked reductions in workplace stress and deeper, more meaningful relationships with customers.
