Accounting process automation: your secret weapon

Accounting process automation can greatly reduce the time business finance teams spend on manual tasks. For example, Zeni’s automated accounting software saves founders an average of 70 hours per month.

To help you incorporate automation into your accounting workflow effectively, this guide will explore how modern software works, what you can expect it to handle, and where you’ll typically still need to involve a human expert.

How accounting automation works

Accounting automation tools use a variety of technologies to streamline tasks that would otherwise require significant human effort. This has expanded well beyond manual data entry to include increasingly sophisticated work, such as real-time financial analysis.

Some of the most notable types of software driving modern accounting process automation include:

• Optical character recognition (OCR): Scans images of documents like invoices and extracts the text, converting it into a format that is readily usable by accompanying systems.
• Robotic process automation (RPA): Follows predetermined, rule-based scripts in order to replicate straightforward human actions, such as transferring data from a bank feed to your accounting system.
• Machine learning algorithms: Studies structured and unstructured historical data, recognizes patterns, and teaches systems to make intelligent decisions without having to be explicitly told what to do.
• Natural language processing (NLP): Extracts meaning from human language, allowing software systems to understand and analyze written or spoken inputs.
• Generative artificial intelligence: Creates original content in response to a given prompt, including formats like text, images, audio, and even video.
• Agentic artificial intelligence: Makes decisions and interacts with other systems independently, allowing it to accomplish complex, multi-step accounting tasks with minimal human supervision.

Accounting processes AI is good at automating

1. Transaction categorization

Accounting automation software has long been capable of expense tracking and categorization using RPA. However, because the technology operates on rule-based logic, it can be ineffective at adjusting for situational nuances.

For example, it might simply read the merchant category code (MCC) of a transaction with Staples and automatically sort the expense into the Office Supplies category.

In contrast, AI in accounting can study large volumes of data, identify complex patterns, and use the lessons it learns to make context-aware decisions. As a result, it tends to produce fewer anomalous transactions and require less manual exception resolution.

If it sees that you’ve categorized certain large Staples purchases as “Equipment” every few years, it can learn to incorporate the size and timing of transactions with the vendor into its categorization process.

AI accounting software can also study your company’s activities continuously to grow more accurate over time. 

While legacy bookkeeping tools might make the same mistake over and over, AI systems use each correction as additional training data and update accordingly.

2. Account reconciliation

Reconciliation is another aspect of accounting that has been automated using RPA for years. However, just like with transaction categorization, the technology’s rigidity and reliance on predetermined rules often hold it back.

These systems generally reconcile internal and external records by looking for exact matches across details like dates, amounts, and vendors. 

If those characteristics vary even slightly, the software might fail to recognize transactions that legitimately correspond.

AI-powered account reconciliation software is much more flexible. Its analytical capabilities allow it to recognize when two records likely represent the same transaction using contextual clues, even when some of their details technically differ.

For example, if an expense posts to your general ledger on one date but appears on your bank statement a few days later with a slightly different description and vendor ID, AI tools can use the amount and historical patterns to determine that the records are still likely a match.

Once again, this typically results in fewer exceptions that require manual review, saving valuable time during the month-end close.

3. Recurring journal entries

Traditional automated accounting systems let a finance team streamline much of the work involved in routine journal entries. 

They can extract details from data sources, generate entries based on predefined rules, and post them to the general ledger after approval.

However, the process still requires a meaningful amount of manual accounting work. Someone has to set up the schedule, define the initial parameters, and step in for maintenance when changing circumstances break the workflow automation.

AI can manage the process more dynamically and with less human input. Instead of accountants having to build each schedule, it can analyze unstructured data and propose appropriate journal entries based on its understanding of the context.

For example, say your business receives an $8,000 invoice for a piece of equipment. A traditional system would need someone to tell it that the equipment value exceeds the capitalization threshold and to depreciate it over five years.

Meanwhile, an AI system could potentially recognize the nature of the purchase from the details of the invoice, then propose a suitable depreciation schedule based on the way you’ve treated similar capital asset purchases in the past.

4. Accounts payable

Bill payment is one of the traditionally manual accounting tasks that’s best suited to AI automation. 

While exact workflows vary between systems, many AI-powered accounting tools can effectively automate the process from start to finish, with capabilities like:

• Invoice processing: When you upload a bill, AI can extract the relevant details, such as the vendor, amount, expense category, and payment terms.
• Approval routing: AI-powered systems can route bills through various approval workflows, factoring in nuances like dollar thresholds or the vendor’s owner.
• Payment scheduling: Some AI systems can schedule the timing of payments intelligently to avoid late payment issues while preserving liquidity.
• Spend tracking: AI software can summarize your outstanding invoices by status and break down your spending to date with individual vendors.

In addition to vendor invoices, Zeni’s AP automation tool can apply these capabilities to your employee expense management, allowing you to automate the entire accounts payable function with the same solution.

You can learn more about how AI helps automate bill payment in our article, Accounts Payable Automation Examples.

5. Real-time financial analysis

Financial reporting has always been one of accounting software’s primary functions, translating your company’s raw financial data into a balance sheet, profit and loss statement, and statement of cash flows.

However, legacy bookkeeping software often updated these reports only periodically, causing them to lag slightly behind reality. In contrast, modern AI-powered systems can keep your financial statements accurate up to the minute.

AI systems can also take this one step further by analyzing your financial statements and surfacing useful insights in real time. This often involves identifying meaningful trends or shifts in performance that might warrant human attention.

For example, Zeni’s bookkeeping automation tool maintains a live key performance indicator (KPI) dashboard that tracks metrics like:

• Operating expenses
• Net burn and cash runway
• Revenue and gross margin

Similarly, Zeni’s AI accountant agent also performs ongoing flux analysis as part of its monthly close automation, identifying variances in spending across vendors, categories, and classes for review.

Some systems, like our AI CFO agent, can even go beyond historical analysis and perform forward-looking work to help inform strategic decision-making, such as what-if analysis and scenario modeling.

Processes that still need a human touch

1. Exception resolution

As powerful as AI accounting software has become, no technology is perfect. It can still encounter situations that it doesn’t know how to handle, requiring a human accountant to step in and resolve the issue manually.

In many cases, these exceptions occur because the accounting automation tool is working with imperfect or incomplete information. For example, this could mean that:

• Relevant data sources contain conflicting details
• An uploaded invoice or other document is missing information
• Historical training activities don’t include any comparable transactions

In other cases, AI models can simply make mistakes. While many systems have reached an impressive level of accuracy, they are still potentially susceptible to hallucinations or other errors in judgment, which can cause discrepancies.

Because of these risks, it’s still important to involve human experts when automating accounting processes. In addition to resolving any exceptions that arise, they can double-check the AI software’s work to ensure mistakes don’t slip through the cracks.

This is why Zeni’s bookkeeping subscriptions combine AI automation with dedicated support from our expert in-house accounting team. Together, they ensure your financial records remain 100% accurate.

2. Regulation interpretation

Day-to-day bookkeeping is often relatively cut and dried, making it an easy accounting task for AI to handle with sufficient training. 

Categorizing expenses with known vendors, reconciling long-standing accounts, and rolling over journal entries are all well within its capabilities.

However, some aspects of accounting tend to require significantly more nuanced judgment, such as those that involve:

• Revenue recognition standards
• Intangible assets and goodwill impairment
• Multi-entity and intercompany transactions

AI models may be able to handle some of these situations, especially if they’ve seen how you’ve treated them in the past. However, they’re fundamentally working off of pattern recognition rather than a true understanding of the underlying subject matter.

As a result, even the most intelligent accounting workflow automation tools can struggle to navigate these areas. 

They can suggest what they conclude is most likely to be the appropriate treatment, but this may not always reflect the proper application of the relevant rules.

Accounting standards and regulations also tend to evolve over time in response to things like emerging technologies or corporate scandals. This creates a risk that AI models may operate using outdated principles or obsolete procedures.

3. Strategic decision-making

AI excels at many aspects of financial planning and analysis (FP&A). Modern platforms often include features that can automate much of the manual work involved, streamlining time-consuming processes related to financial modeling.

For example, Zeni’s AI CFO can automatically perform flux analysis, give instant answers to questions about KPIs, and generate cash flow forecasts from scratch. These types of capabilities can greatly enhance the efficiency of your financial planning.

However, strategic decision-making requires more than just raw computational powers. Business leaders also need real-world experience and a deep contextual awareness that AI simply can’t replicate.

For example, financial executives often have to weigh qualitative variables like employee morale, client relationships, and their organization’s public reputation against purely quantitative goals like growth and profitability.

Because these types of intangible factors can’t be readily understood from financial data, AI often struggles to incorporate them effectively into its analysis, even though it may speak about them confidently.

AI’s reliance on historical data also means that it can have difficulties navigating genuinely unprecedented situations. Of course, due to the evolving nature of growing companies and ever-changing markets, these occur all the time.

If nothing else, software is also fundamentally incapable of taking personal or legal responsibility for decisions. As a result, the human executive’s capacity for accountability remains essential.

Automate your accounting with Zeni

Zeni is an all-in-one financial management platform with AI-powered automation at its core. Our flagship product is our AI bookkeeping software, which provides real-time financial visibility, analysis, and strategic recommendations.

In addition to expert human support, clients receive access to twin AI agents. Our AI Accountant automates the monthly close with step-by-step transparency, creating a clear audit trail. 

Meanwhile, our AI CFO serves as a strategic planning partner that helps you make better-informed decisions.

The platform also includes built-in accounts payable software, capable of automating both vendor payments and employee reimbursements end to end—from the same dashboard you use to manage everything else.

For a truly hands-off experience, you can outsource additional aspects of your financial operations to our integrated services. These include:

• Fractional CFO: Get executive-level guidance when navigating complex financial challenges, like fundraising or board meeting prep.
• Business taxes: Let our team of tax experts file your annual returns to ensure compliance and maximize your savings. 
• Payroll management: Hand off employee onboarding, termination, and payroll system management to our in-house specialists.

If you’re ready to automate accounting processes for your business, schedule a call today for a free software demonstration and consultation with our accounting professionals.