Automation has significantly reduced manual work in accounting, but it still relies heavily on predefined rules. As processes become more complex and exceptions more frequent, this approach is no longer sufficient.
Agentic Automation emerges as the next step, enabling systems not only to execute tasks but also to understand data, make decisions, and take proactive actions. This lays the foundation for accounting departments to operate faster, more accurately, and move toward full automation.
Table of Contents
Why Accounting Departments Need Agentic Automation
Growing Operational Pressure
The volume of accounting transactions (accounts payable, accounts receivable, invoices, payments) is increasing rapidly as businesses scale. According to industry reports, the number of invoices processed by companies can grow by 15–25% annually.
At the same time, demands for transparency and compliance are rising, especially in audited environments or regulated industries such as finance and banking. This requires accounting teams to deliver not only speed, but near-perfect accuracy.
Common Pain Points
Despite the availability of software tools, many accounting departments still face familiar challenges:
- Manual data entry accounts for 30–50% of working time, with high error risk
- Slow reconciliation processes, heavily reliant on fragmented Excel files
- Disconnected workflows involving multiple systems and stakeholders
- Lack of real-time visibility into financial data
These bottlenecks not only reduce efficiency but also increase the risk of errors and operational delays.
Limitations of Traditional Automation
Traditional automation solutions such as RPA have helped reduce workload, but still come with significant limitations:
- Rule-based processing, with little flexibility for exceptions
- Up to 20–30% of transactions may still require manual handling
- Difficult to scale as processes become more complex
- Inability to understand unstructured data such as emails, scanned documents, or free text
As a result, businesses remain heavily dependent on human intervention for real-world scenarios.
The Shift to Agentic Automation
Agentic Automation represents the next evolution, where systems not only execute tasks but also understand, analyze, and make decisions.
Instead of strictly following predefined workflows, AI agents can:
- Read and interpret data from multiple sources
- Handle exceptions effectively
- Proactively execute next steps such as posting entries, sending alerts, or recommending actions
This enables businesses to reduce manual workload significantly (up to 60–80% in some processes), while improving both speed and accuracy. It also lays the groundwork for more autonomous financial operations with minimal human intervention.
How Agentic Automation Works in Practice
Agentic Automation functions as a “digital accounting assistant” capable of handling end-to-end processes—from data intake to decision-making and execution. Instead of fragmented manual steps, the entire workflow is connected into a seamless system that operates with minimal human intervention.
Core Flow
Data ingestion
The system automatically collects data from multiple sources such as ERP systems, emails, e-invoices, and Excel files, including both structured and unstructured data.
Context understanding
AI reads, extracts, and interprets key information (vendors, amounts, dates, terms), placing it into the correct business context.
Decision-making
By combining predefined rules with reasoning capabilities, the system determines the most appropriate action—even in cases that do not fully match predefined scenarios.
Action execution
The system automatically performs next steps such as posting journal entries, updating systems, sending notifications, or generating reports.
Continuous learning
Based on feedback and historical data, the system continuously improves its accuracy and processing speed over time.
Example Workflow (Invoice Processing)
The system receives an invoice via email → automatically extracts relevant data → matches it against purchase orders → if discrepancies are detected, it evaluates the severity and sends alerts or suggests actions → if valid, it records and updates the accounting system.
This approach eliminates or significantly reduces manual steps, accelerating processing speed and minimizing errors across the workflow.
Key Use Cases of Agentic Automation in Accounting
According to McKinsey, approximately 42% of finance and accounting activities can be fully automated, with an additional 19% partially automatable—highlighting the significant potential of Agentic Automation.
Accounts Payable (AP)
- Automated invoice data extraction
- Matching purchase orders, invoices, and payments
- Detecting duplicates or anomalies
- Recommending or auto-approving payments
According to IOFM, manual invoice processing costs $10–15 per invoice, while automation can reduce this to below $3.
Deloitte reports that automation can reduce invoice processing time by 60–80%.
Accounts Receivable (AR)
- Automated invoicing and payment reminders
- Personalized follow-ups based on customer behavior
- Automated cash application
- Payment prediction
According to PwC, AR automation can reduce 20–30% of collection time.
Financial Close and Reporting
- Automated data aggregation from multiple systems
- Financial report generation
- Variance analysis
- Accelerated closing cycles
Deloitte indicates that automation can reduce closing time from 7–10 days to 3–5 days.
McKinsey estimates a 40–60% reduction in manual workload in finance functions.
Expense Management
- Automated validation of expense claims
- Detection of unusual or non-compliant spending
- Expense classification and posting
Automation can reduce 50–70% of processing time.
According to ACFE, organizations lose around 5% of annual revenue to fraud, and automation helps detect anomalies earlier.
Audit and Compliance
- Continuous compliance monitoring
- Detection of unusual transactions
- Automated audit preparation
Deloitte reports a 30–50% reduction in audit preparation time through automation.
Challenges and Considerations
The challenge of Agentic Automation lies not in the technology itself, but in applying it within the complex, highly regulated accounting environment.
Data Challenges: “Garbage in, garbage out”
Agentic Automation relies heavily on input data quality. In reality, accounting data is often:
- Fragmented across systems
- Inconsistent in format
- Historically inaccurate
Without proper data standardization, automation simply scales errors instead of eliminating them.
Exceptions Are the Norm
In practice, exceptions are common:
- Incorrect invoice formats
- Missing information
- PO mismatches
- Special payment terms
These exceptions can account for 20–40% of transactions. Without proper handling, automation breaks down and requires human intervention.
Control and Transparency
As systems begin making decisions, the biggest risk is the “black box” problem.
To address this, organizations must ensure:
- Full auditability of decisions
- Clear logic behind actions
- Risk-based control mechanisms
Automation should enhance control—not replace it.
Changing the Role of Accountants
Agentic Automation shifts the role of accounting teams:
- From data entry → oversight
- From execution → analysis and control
- From reactive → proactive
Without proper training and alignment, resistance to change can limit the effectiveness of automation.
Conclusion
Agentic Automation not only improves accounting efficiency, but transforms financial operations into a more proactive and intelligent system.
Organizations can start by implementing platforms like AkaBot to automate core accounting processes, then expand with AI-driven applications such as ScalePay (for payment and reconciliation automation) and ScaleFlow (for workflow and growth automation).
Start small, measure impact, and scale progressively—this is the most effective way to bring Agentic Automation into practice.
