Accounting is a core business function that encompasses everything from processing invoices and collecting payments, to financial reporting and strategic advisory. As a crucial activity, accounting work tends to be recurring and high-volume. Companies handle thousands of transactions, documents, and records on a regular basis. Although critical, much of this work is also manual, repetitive, and inefficient – making accounting an ideal target for automation through AI.

AI and its associated technologies including machine learning, natural language processing, intelligent process automation, and predictive analytics have matured considerably in recent years. They are now capable of replicating many aspects of human intelligence and decision making. As a result, AI can take over and enhance a range of accounting processes to run them much more quickly, accurately, and cost-effectively than manual approaches. 

This represents a significant opportunity for accounting teams. By leveraging AI to eliminate grunt work and augment human capabilities, accounting staff can focus on delivering strategic business insights rather than simply processing numbers. They can keep up with growing transaction volumes and compliance needs without inflating headcount. Finance chiefs can rest assured that their books are being managed reliably without sacrificing productivity.

In this article, we will dive deeper into how AI is transforming accounting today. We will look at where automation can have the biggest impact, real-world examples of AI in accounting, tips for successful implementation, and the future outlook for this important digital shift.

Current Challenges with Manual Accounting

To understand why automating accounting with AI is so valuable, let’s examine some of the key challenges associated with current manual processes:

Prone to human error: Accounting work is meticulous and detailed. But it is also highly repetitive with huge volumes of transactions. This combination inevitably leads to occasional errors in data entry, classifications, calculations, reconciliations etc, resulting in inaccurate reporting. Fixing errors is time-consuming.

Inefficient processes: Much accounting activity involves manually entering bills, coding expenses, validating reports, moving data across systems and more. These recurring, mundane tasks are a poor use of human resources. 

Difficulty scaling: As companies grow, transaction volumes multiply. More customers, vendors, bills etc. This growth in accounting work makes it hard for departments to scale up effectively. Adding sufficient skilled staff fast enough becomes challenging.

Regulatory needs: New regulations emerge constantly around accounting, financial reporting, taxes and other compliance arenas. Keeping processes up-to-date with all these rules is an onerous must that demands substantial resources.

These factors all erode productivity in the accounting function. They lead to bloated costs, delayed work, frustration for staff, and executives being unable to get reports and insights as quickly as needed. Managing accounting manually is no longer sustainable for most growing businesses.

The Promise and Potential of AI-Driven Automation

Intelligently applied, AI has the capacity to eliminate many of these problems and transform accounting efficiency. Some of the major benefits include:

Minimizing errors: AI models can be highly accurate, learning from prior transactions to perform tasks precisely without deviation. This reduces the instances of human error significantly.

Saving time: Systems based on AI like RPA bots, can take over high-volume repetitive tasks like data entry, freeing up employee bandwidth for higher-value functions. 

Easy scalability: AI automation handles growing transaction volumes seamlessly by self-learning and adapting to new information. No need to add more staff.

Regulatory compliance: AI can be trained to stay abreast of new regulations and requirements to maintain adherence without active human input.

The central value is being able to apply accounting staff to functions that create real business value like advising executives, finding optimizations, strategic planning etc. rather than having them bogged down moving data between systems or double checking reports. AI turns accounting into a profit rather than cost center.

High Impact Accounting Processes to Automate with AI

Many accounting activities are ripe for enhancement through AI. Some examples of processes that can benefit considerably from automation include:

Data entry: Extracting information from invoices, receipts, bills and other documents is a huge manual effort. AI uses OCR and machine learning to automate data capture.

Transaction matching: AI can replicate accountants’ work of matching entries from disparate systems and spotting duplicates or gaps much faster.

Reconciliations: Account reconciliations can be automated by having AI models learn patterns and rules to reconcile accounts, like AR and AP ledgers.

Reporting: Static reporting with rigorous structures is well suited to automation for accuracy and speed.

Expense processing: Systems can be trained to recognize valid expenses, flag issues, approve routine spends and more.

Travel reimbursements: Using past approved expenses, AI can automatically process reimbursements against corporate policies. 

Financial close: AI helps automate close checklists, intercompany eliminations, journal entries and approvals.

These represent just some of the crucial accounting functions ripe for modernization through AI. The processes tend to involve high volumes of repetitive work applied to structured data – exactly what AI excels at when programmed correctly.

Practical Examples of AI Transforming Accounting

To make the benefits of AI more concrete, here are a few real-world examples of the technology already driving accounting automation and enhancement:

OCR for invoices: Software like Rossum uses advanced OCR and structured data extraction to pull invoice details into accounting systems accurately without any human review.

Account reconciliations: Reconciling accounts between systems like Oracle and NetSuite can be automated by RPA bots programmed by companies like Automation Anywhere.

Fraud detection: Versive developed AI models to analyze expense reports and detect potential fraud or compliance issues for early intervention.

FAQ bots: JP Morgan built an accounting chatbot that answers supplier FAQs to cut down on invoice and payment inquiries.

Journal entry analysis: MindBridge’s AI audits 100% of journal entries to spot risky or incorrect ones for further investigation.

Close automation: BlackLine added AI to enhance it’s accounting close suite to handle more procedures without human help.

The specialized use cases are practically endless. In aggregate, they amount to a revolution in how accounting teams operate and deliver value. Adoption is still early, but momentum is accelerating quickly.

Key AI Capabilities to Seek Out

As you evaluate AI automation solutions for accounting, some key features and capabilities to look for include:

Integration: Must interface well with your accounting system like NetSuite, SAP, Oracle etc. to move data seamlessly.

Text analysis: Natural language processing to extract unstructured data from documents, emails, notes etc.

Process automation: Ability to replicate and augment human workflows across accounting tasks.

Machine learning: System should improve continuously by analyzing patterns in accounting data. 

Prediction: Models need to forecast things like cash flows, late payments etc. for insights.

Explainability: AI should provide visibility into why it makes certain recommendations or decisions.

Self-learning: The more accounting data it consumes, the smarter it gets without hands-on training.

Security: Vendor should be able to speak to data protection, access controls, business continuity measures.

Pre-built applications focused specifically on accounting processes show the vendor has deep expertise in this area. Seeking out specialists rather than general automation tools is wise.

Best Practices for Implementation 

Here are some recommended best practices to ensure you implement accounting focused AI successfully:

Start small, but think big: Begin with a well-defined pilot project in one accounting process vs. trying to change everything at once. But have a roadmap to scale once proven.

Involve stakeholders: Get input from accounting staff who will use and be impacted by AI to assess needs and get buy-in. But provide training and reassurance.

Evaluate vendors thoroughly: Require demos, process walkthroughs, scalability discussions, total cost of ownership estimates, service capabilities, implementation timelines, performance benchmarks, and customer referrals.

Clean up data first: AI models only work well with high quality, standardized data. Take the time to clean up accounts, customers, vendors etc. before feeding data to AI.

Monitor closely at first: Observe AI recommendations versus human decisions in shadow mode before giving the system live control. Maintain supervision and adjust.

Continuous improvement: Leverage monitoring insights to further train AI models over time for even better performance. The AI platform should learn and evolve along with your accounting processes and data.

The Future Outlook for AI in Accounting 

While AI for accounting is gaining traction today, we are still just scratching the surface of how this technology will transform the function over the next decade. Here are some exciting developments on the horizon:

More packaged applications: Expect to see more plug-and-play AI software tailored specifically to accounting that is pre-trained and ready to adopt.

Tighter integration: AI capabilities will be embedded directly into major accounting systems like NetSuite rather than needing third-party tools.

From processing to advising: AI will elevate accounting staff from focusing on rote tasks to high-value advising driven by AI insights.

Competitive differentiation: AI-driven accounting will become a competitive edge for leaders rather than just playing catch up on costs.

Overall, AI represents the most pivotal evolution in accounting since perhaps the advent of double-entry bookkeeping centuries ago. By infusing accounting with scalable, self-learning automation, AI can transform every facet of the function. Companies need an AI strategy for staying nimble, efficient and insightful as the landscape evolves. The time to start exploring accounting automation is now.

Conclusion

Accounting provides the lifeblood information companies need to operate and thrive. But manual accounting processes are no longer scaling. Growing transaction volumes and compliance needs are stretching thin accounting staff focused on repetitive tasks versus strategy. AI represents a solution to evolve accounting to a more productive and value-add state.

Automating accounting workflows through technologies like RPA, machine learning, and natural language processing can minimize errors, reduce costs, and empower staff. By implementing AI accounting solutions tailored to your environment, companies can speed up critical functions like reporting, planning, and advising. This shifts accounting from obligations to opportunities.

Now is the time for forward-thinking accounting teams and finance executives to put AI automation on their agenda. The technology is reaching maturity alongside a wealth of applications purpose-built for accounting needs. Organizations that leverage AI will gain efficiency, insights, and a competitive edge over those that delay. Accounting should join the enterprise movement to AI that is revolutionizing business productivity across the board.