Ahead of the Game: How Accountants Can Implement AI Today

Let’s be honest – you don’t want to just hear and talk about AI technology all day, you want to see that it’s valuable. You’re up to speed on the basics. You know the ins and outs of tools like ChatGPT. It’s now time to start putting some research into action. 

Best-in-class accounting teams are moving past the talking stage by identifying areas in their accounting practices where AI-powered tools and automation can gain quick wins. These teams aren’t pursuing a miraculous AI overhaul in one day – instead, they’re thinking strategically about what AI applications work best where and now so that they can realize incremental progress. 

Building Better with Artificial Intelligence – Beginner, Intermediate, & Advanced Implementation   

For teams of all levels looking to see the benefits of AI, we’ve sourced a series of accounting-specific use cases, shared directly with us from accounting professionals on AI-forward teams. 

Beginner Implementation: Text Generation – Memo, Emails, etc.

Text Generation – Memo, Emails, etc. (Resume Review)

For instant impact, teams should look to outsource writing tasks to generative AI. Large-language models excel at producing written content in seconds, and while the writing may need some slight adjustments for tone or word choice, they tend to make for strong first drafts.

These are some common areas where accountants lean on AI’s writing finesse: 

• Emails: Need to send a quick email to the marketing team asking them to send over their invoices? Instead of brainstorming, tell the model exactly that and it will spin up a solid draft in a fraction of the time. 
• Policy Memos: Before toiling away at writing your own memo for changes to your company’s ASC 842 policy, give AI some key points to include and let it tackle the rest. 
• Resumes: Have AI polish up an existing resume, tell it to create a new resume template entirely, or drop in a job description and have it tailor your resume to said JD. 
• Summaries: Lean on AI to give you refreshers or rapid analysis of longer documents

‍

This list is far from exhaustive, and as you begin to leverage AI’s content generation abilities, you will likely find more and more accounting tasks that could benefit from its use.  

Technical Accounting AI 

General purpose LLMs like ChatGPT or Claude are useful for quick references, but they draw from the entire internet and need precise prompting.

For specific accounting questions, Numeric’s technical accounting AI is your best resource. 

Trained on GAAP guidance, public company filings, and more, it provides answers for any standard (US GAAP, IFRS, UK GAAP) and for any Big 4 audit firm guidance. From policy memo generation to quick ASC 606 clarifications, accountants rely on Numeric’s AI for accurate and immediate direction.

Intermediate Implementation:

Data Formatting 

AI is just as agile working with numbers as it is with words. When performing any data-heavy work, one cool feature is that you can ask AI to format or reformat data outputs as you see fit. This functionality works well for any data transfers across software, but also helps to clean up CSVs that aren’t exactly outfitted to your liking. 

Another tip for accountants is that you can have AI run data analysis on a given dataset: this feature helps to get a headstart on flux analysis, by providing first-draft explanations for variances within accounts. Here, AI’s quantitative and qualitative strengths combine to work away at one of the accounting profession’s thorniest processes. 

Coding Co-Pilot 

If you find yourself having to hound down engineers in the office to build code for accounting processes, then AI can expedite or even remove this back-and-forth altogether. Since AI models are able to understand the differences between programming languages, coding styles, and common practices, they can generate code suggestions that have accurate syntax and are contextually relevant to the prompter’s demand. \

Let's consider a scenario where an accountant needs to automate the reconciliation of transactions from a bank statement with the company's internal ledger. An AI model can help generate a Python script to automate this process, saving time and reducing the risk of human error. Here's an example script that uses a team’s existing CSV files:

Script Explanation:

1. Load Financial Data: The script loads transaction data from the bank statement and the internal ledger into pandas DataFrames.

2. Clean and Format Data: It ensures that the dates are in a proper datetime format.

3. Merge Data: The script merges the two DataFrames on the 'Date' and 'Amount' columns. The `how='outer'` parameter ensures that all transactions from both sources are included.

4. Reconciliation Status: It adds a new column to indicate whether each transaction is matched, only in the bank statement, or only in the ledger.

5. Save Results: The script saves the reconciled DataFrame to a new CSV file and prints a summary of the reconciliation status.

By using AI to generate such scripts, accountants can streamline their workflows, reduce manual effort, and regain the time to focus on more strategic tasks.

Embedded AI tools  

Accounting teams who have developed a strong baseline with free, general purpose AI tools can continue to build by investing in embedded AI that might sit in software they already have or in tools that they will need to procure. 

Embedded AI sits within an existing platform and assists you as you move through your workflow. Numeric employs embedded AI across its platform, particularly with its flux analysis AI writer – here, teams can have the AI writer generate variance explanations that identify key drivers of change month-over-month from their GL data.

The flux writer isn’t built to perform nearly any of the tasks that a free LLM can: it’s meant specifically to serve Numeric customers with one element of their month-end workflow. 

If it feels like ChatGPT and Claude aren’t quite having the impact that you need to reduce manual work, looking into embedded AI software is a logical next step to continue whittling away at your most time-consuming and repetitive tasks. To learn more about the range of AI accounting software that address financial processes including invoice processing, data entry, data extraction from financial statements, reconciliation of accounts payable/receivable, and more, read our list of embedded AI tools. 

Advanced Implementation:

Micro-Products like Internal GPTS

So, you’re ready to take your team to the proverbial summit of AI implementation? Well, consider this: It’s one thing to use an AI tool; it’s another to build your own. 

Yes, the most AI-advanced accounting teams aren’t just using ready-made tools from external providers – they are creating their own AI systems in the form of micro-products tailored to addressing specific bottlenecks. 

How? Enterprise customers of ChatGPT have the ability to create internal GPTs that service their needs – after these mini-machines are trained on company data & policies, they become a significantly useful repository of info that might even rival the contributions of any one employee. 

Sowmya Ranganathan, Controller at OpenAI, says that she sees the most benefit from these internal GPTs in streamlining non-essential work communications. 

For her team, she has set up a spend policy chatbot that has been trained to understand the nuances of the company’s spend policies; this way, if someone has a question about buying team swag, for instance, Sowmya doesn’t have to pause more vital work to answer this inquiry. Instead, she can direct them to the GPT for this and all future requests. 

A bot for something like a spend policy might seem menial when compared to the big-ticket items that AI and automation technologies appear capable of handling. But realistically, addressing these more niche problems leads to incremental wins that are just as key to team efficiency.

While the prospects of having a customized, team-tailored GPT might seem precluded by a need for in-depth coding knowledge, setting up one’s own GPT is far less complicated than one could imagine – as Ranganathan states, “it’s as simple as dragging and dropping, and typing things in. The only language you need to know is the language you speak to another person.” 

Strong AI Habits — the Foundation of Forward-thinking Teams

Ranganathan also provides some sage perspective with regard to how accountants should be thinking about AI adoption: 

“Whenever you are tasked with some kind of data wrangling, just pause. All of our accounting instincts point to using Excel or Google Sheets – you say to yourself, ‘I know exactly what to do here’ – but maybe that’s actually not the best thing to do.

Why? Because you now have this tool that’s very general purpose and it can write tools or code something up for you or address tasks in ways that you might not have otherwise done.” 

As AI continues to grow and transform in the coming months, accountants will have to be willing to move out of their comfort zone and throw themselves into trying these new technologies. Embracing these advancements will not only enhance efficiency but also open up opportunities to spend less time on routine tasks and more time on strategic decision-making and financial planning. Staying adaptable and proactive will be key to leveraging the full potential of AI in the accounting industry.