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AI is hailed as a game-changer in accounting, delivering accuracy, speed, and actionable insights that can give businesses a distinct competitive edge.
98% of finance professionals believe AI is either extremely important or somewhat important, according to The State of AI in Finance, a study commissioned by Tipalti.
But how can AP teams derive the promised benefits of AI in accounting?
This guide covers the benefits and challenges of AI in accounting, best practices for successful AI adoption, and the criteria to evaluate AI-enabled accounting software.
What Is AI in Accounting? Technologies, Tools, and How They Work
AI in accounting refers to a collection of related technologies, including generative AI, machine learning (ML), deep learning, and natural language processing (NLP), to automate data entry, coding, reconciliation, reporting, and other accounting tasks.
AI tools go beyond process automation to analyze data sets, identify patterns, generate insights, and make predictions. Their ability to learn from data and adapt over time makes them valuable to finance professionals.
The latest evolution is agentic AI. Unlike traditional AI models that analyze or generate content, AI agents can execute multi-step finance workflows, coordinate across systems, and recommend or complete actions in accordance with defined business rules and human approval. From algorithms that detect fraud to problem-solving capabilities, each type of AI brings unique strengths.
Types of AI in accounting include:
- AI Agents (Agentic AI): AI agents combine reasoning, workflow orchestration, memory, and tool use to complete multi-step accounting tasks. Rather than simply generating responses, they can gather information across finance systems, recommend actions, execute approved workflows, and escalate exceptions when human judgment is required.
- Machine learning (ML): ML algorithms, including artificial neural networks, learn from financial data over time to improve accuracy and detect patterns.
- Natural language processing (NLP): Technology that helps AI understand and generate human language, useful for reports, chatbots, and data queries.
- ChatGPT and other large language models (LLMs): Generative AI tools that can draft reports, answer finance questions, and assist with coding or analysis.
- Computer vision: AI that can “see” and interpret images, such as reading scanned receipts or invoices.
- Expert systems for decision-making: Rule-based AI that mimics human judgment to support financial and compliance decisions.
- Robotics: Physical or software bots (RPA) that handle repetitive, rules-based tasks like data entry or transaction matching.
Benefits of AI in Accounting
Beyond applications, it’s important to understand the tangible advantages AI brings to accounting teams and organizations. AI accounting benefits include:
1. Higher Accuracy
AI enables error detection and reduces human errors compared to manual tasks or standalone OCR.
Studies show that standalone OCR accuracy ranges from 79% to 92%.
While 92% may sound impressive, at 10,000 invoices per month, it still translates to roughly 800 invoices with errors requiring manual intervention.
AI can improve invoice-processing accuracy when paired with machine learning, validation rules, confidence scoring, and human review workflows.
By combining OCR, fine-tuned AI models, business-rule validation, and human review for low-confidence outputs, a custom healthcare invoice-processing system reported 99% field-level accuracy.
Accuracy gains from AI extend across multiple areas of accounts payable, including data analytics, expense management, and cash forecasting.
For instance, ASNL, a multinational company, used a targeted AI-enabled cash forecasting tool to achieve 97% accuracy.
Accounting professionals also highlight AI’s ability to enhance accuracy and efficiency in tax filing, enabling firms to dynamically adjust to complex tax compliance scenarios based on historical data models.
2. Faster AP Processes
AI accelerates AP workflows, enabling faster invoice processing, payments, reconciliation, and monthly close.
ML and AI enable AP automation software to accurately code invoices, eliminating manual work.
Generative AI or voice-activated AI assistants can help AP professionals perform tasks without manual intervention, such as checking invoice status, approving payments, and creating custom reports.
Seamless data integration across the tech stack can reduce the time required to log in to different systems to review and approve invoices.
AI can also analyze contracts, purchase orders, service agreements, historical transactions, and communications to identify invoice discrepancies, recommend resolutions, and, in some cases, automatically resolve disputes.
This helps reduce delays, improve supplier relationships, and accelerate payment cycles.
For instance, ITB-Med, a medtech company, saved over 500 hours annually, reduced manual errors by 90%, and cut its month-end close by 50% with Tipalti.
3. Stronger Financial Controls
AI strengthens internal controls by automating invoice approval routing, validating invoice data against business rules, verifying vendor and payee information, and performing two-way or three-way PO matching.
It also flags anomalies, duplicate invoices, and potential fraud risks while maintaining a complete audit trail of approvals, exceptions, and payment decisions.
These capabilities improve compliance, increase visibility into financial processes, and reduce the risk of errors and unauthorized payments.
4. Scalability
Integration of AI into the accounting streamlines operations, reduces errors, and reduces manual effort. This efficiency gain leads to faster invoice processing, faster monthly close, and faster payment processing. AI enhances accuracy and transparency, improving decision-making and resource allocation.
Splice, a music creation platform, used Tipalti’s AI-powered AP automation to accelerate its financial close by 30–40%.
The higher level of automation driven by AI helped Splice save 36 days of manual work annually, reduce approval times to minutes, and cut payment processing from up to eight weeks to less than an hour.
This gave them the bandwidth to scale confidently.
5. Cost Savings
AI helps reduce operational costs by:
- Reducing errors and manual effort to improve the AP team’s productivity
- Helping capture early payment discounts and avoid late payment fees
- Optimizing cash flow by recommending the best payment timing based on real-time financial health
- Improving spend visibility with AI-driven insights that support better budgeting, forecasting, and investment decisions
- Evaluating supplier performance to strengthen negotiations and secure better contract terms
- Automating tax and regulatory compliance to reduce the risk of penalties and compliance-related costs.
KlarisIP, a consulting firm, reduced operational costs by 30% and cut contributor onboarding time from 100 days to just two weeks after implementing Tipalti’s AI-driven software.
By automating supplier onboarding, tax form collection, and global payments in multiple currencies, the company significantly reduced manual work while ensuring tax compliance.
6. Improved Compliance
AI in accounting strengthens tax and global regulatory compliance by automating supplier validation, reducing compliance risk, and preventing payments to unauthorized or high-risk entities.
Enterprise AI platforms continuously screen payees against AML, OFAC, sanctions, and anti-narcotics watchlists while applying country-specific compliance rules to help organizations meet global regulatory requirements.
These capabilities are especially valuable for multi-entity organizations operating in high-risk countries with complex regulatory requirements.
For example, a global director of finance operations noted that their team uses AI and machine learning to help ensure compliant payment processing in high-risk markets such as Vietnam.
7. Better Insights and Improved Decision Quality
Financial tools, such as AI-powered automation, empower treasurers and CFOs to become more proactive within the business.
By analyzing large datasets and identifying patterns, AI can improve forecasts, highlight emerging risks, and surface actionable insights. This allows finance leaders to move beyond static reporting and toward more forward-looking analysis.
For example, Britta Dottger, Roche’s head of treasury, notes that:
AI is already helping treasury teams decide how long, how much, and in what currency to invest in.
Britta Dottger, head of treasury at Roche
With time and access to deeper analysis, they can focus on higher-value skills, such as strategic thinking, future planning, change initiatives, and communication.
Data Quality Is the Key to Unlocking AI Value
In a nutshell, AI transforms AP by handling labor-intensive tasks and enabling professionals to focus on higher-value activities.
But it is important to understand how finance teams can harness AI to optimize these benefits.
As the depth and accuracy of AI depend on the quality of data available, having a clear data strategy is vital. Apart from ensuring accuracy and completeness of data, continuous testing and monitoring of AI systems can help ensure AI outputs meet prescribed accuracy, reliability, and compliance standards.
Who’s Leading the Way in Using Accounting AI?
Businesses, public accounting firms, nonprofits, and governmental entities, including U.S. government departments, are using accounting AI.
Notably, AI technology is becoming embedded in the software that they use. Some are using the latest version of ChatGPT, while many firms are building domain-specific AI models for accounting.
The accounting industry encompasses public accounting firms and accountants who work as employees or independent contractors for clients, including businesses and organizations.
Use of AI by CPA Firms
A 2025 Thomson Reuters Institute survey of accounting firms, tax professionals, and corporate tax departments compiled these results:
The percentage of respondents who said their organizations were actively using GenAI nearly doubled in 2025, compared to 2024.
The top-tier Big 4 CPA firms (Deloitte, PwC, Ernst & Young (EY), and KPMG) are continuing to lead the way in AI adoption.
CPA firms are using AI for:
- Audit document review: Gen AI and agentic AI are used in document processing. These tools perform document review and provide recommendations to improve clarity and consistency.
- Unifying tech stack: Firms, including EY, use AI to combine their tech stack across strategy, transactions, transformation, risk, insurance and tax.
- Tax compliance and advisory: AI is being used in tax research, generating predictive insights to plan future tax implications, automating data extraction and analysis, and identifying applicable deductions and credits to ensure compliance and optimization of tax liabilities.
- Accounting automation: AI-powered software helps firms categorize expenses, reconcile accounts, and generate financial reports.
- Risk management: AI, ML, and AI agents enable predictive risk modeling, anomaly detection, fraud monitoring, regulatory compliance, and automated reporting by analyzing historical financial data.
Rising AI Adoption Among Mid-Sized Firms
AI provides an unprecedented opportunity for mid-sized firms to enhance productivity, efficiency, and capacity.
66% of mid-market firms are AI implementers, according to a KPMG report on AI in finance. The adoption rate is higher in some countries, like the UK, where 86% of mid-tier accounting firms have adopted AI in their workflows.
According to Erik Asgeirsson:
When harnessed the right way, [AI] offers the potential to revolutionize the way firms of all sizes deliver value and business insights.
Erik Asgeirsson, president and CEO, CPA.com
Thomson Reuters shares some understanding of the use of generative AI by tax professionals:
[M]ore than half of all legal, tax, risk & fraud and government professionals have used GenAI in some fashion, with a wide range of use cases already arising. In fact, the report shows that professionals are not only expecting GenAI to become a more common part of their work, but they’re feeling more positive about the impact GenAI will have on their profession.
Thomson Reuters
Will AI Replace Accountants?
AI will not replace accountants but is more likely to augment human capabilities, according to the World Economic Forum’s (WEF) Future of Jobs Report 2025.
Researchers at the WEF assessed AI’s capacity to replace 2,800 workplace skills and found that no skill had a very high likelihood of being replaced.
Most skills had a low or very low risk of being replaced by AI. The findings reinforce that human–AI collaboration is the dominant model for the future of work.
Gen AI, according to the report, is still limited in handling tasks that require physical execution, nuanced judgment, hands-on application, and human interaction that requires soft skills.
However, AI will reshape the role of accountants and AP professionals, shifting their focus from manual tasks to higher-value strategic work.
Future Skill Shifts
As AI enhances accounting automation, the focus will shift toward analysis, scenario planning, controls, and business partnering.
PwC’s 2026 AI Global Jobs Barometer highlights that the skills needed for AI-exposed jobs are changing more than twice as fast as those for the least exposed roles.
AI-exposed junior roles are 7 times more likely to require skills such as leadership and strategic thinking that are traditionally associated with senior roles.
In addition to acquiring expertise in using AI tools and data management, accountants will need to fine-tune skills such as empathy, judgment, and creativity, which are now even more valuable as AI takes over routine work.
Risks and Challenges of AI in Accounting
While AI delivers tangible benefits, adoption in finance may not be easy. Knowing the barriers and risks of adopting AI is the first step to understanding how to address them.
Data Security
As AI systems contain vast amounts of sensitive data, they are susceptible to data breaches. 57% of surveyed finance professionals identified data security as the top barrier to AI adoption, according to KPMG’s report.
Poor Data Quality
Difficulty gathering consistent, high-quality data is another challenge when it comes to AI adoption in accounting. Implementing a data governance framework, centralizing data, and establishing data quality controls can help optimize data quality.
AI Accuracy Limitations and Hallucination Risks
AI-generated outputs are not always accurate and may produce incorrect or fabricated information (hallucinations).
High data quality, human review, validation, and strong governance can help overcome AI accuracy limitations.
Legacy System Integration Challenges
Legacy finance systems that lack modern APIs or standardized data can make AI integration complex, time-consuming, and costly.
Successful implementation often requires modern finance automation platforms that enable seamless integration across the finance technology stack.
Skills Gaps and Change Management
Lack of AI skills and talent is another major barrier to adoption.
According to a finance executive quoted in KPMG’s Global AI in Finance report, “Lack of clear knowledge of deploying AI is the main reason why AI efforts are failing.”
Leaders in AI implementation in finance address this barrier by implementing change management and education programs to equip their teams with the AI skills and innovation mindsets they need to succeed.
Unclear ROI
An investment approach that involves piloting AI to validate ROI before expanding adoption is key to optimizing AI’s value.
Another factor that helps maximize ROI is establishing clear accountability for AI strategy and governance.
The Global AI Pulse survey by KPMG reveals that organizations with clearly defined accountability derived more than three times the returns than those without it.
While these challenges can slow AI adoption, organizations that follow AI implementation best practices are far more likely to realize strong returns.
In fact, 84% of businesses report that AI delivered an ROI that met or exceeded their expectations, according to KPMG’s Global AI in Finance report.
Prepare Your Accounting Team for Agentic AI
Finance teams are ready for AI, but trust, controls, and governance determine how far it can scale. Learn how to move from experimentation to trusted execution with visibility, auditability, and human oversight built in.
How AI Can Be Used in Accounting
AI can be used in accounting to automate routine financial processes, improve accuracy, and generate insights for better decision-making.
Invoice Data Capture
OCR AI for invoice capture extracts invoice data from emails, PDFs, and scanned documents, identifying both header information and line-item details with greater accuracy than OCR alone.
Advanced AI can extract data from complex, multi-page invoices and receipts in different formats or languages, minimizing manual effort and errors.
Firms also use AI to summarize key points from contracts, invoices, and receipts.
Coding and Transaction Categorization
Machine learning learns from historical coding patterns to automatically assign general ledger (GL) codes, improving accuracy over time.
AI recognizes consistent coding patterns at header and line-item levels, including custom fields like department, location, tax codes, and expense accounts.
AI can also categorize transactions into the appropriate expense categories and automate tax coding by matching invoice tax amounts with the correct rates and codes, reducing manual work while improving consistency and accuracy.
A Stanford study found that AI classifies transactions into more granular categories rather than broad expense groups, giving finance teams richer data for reporting, forecasting, budgeting, and spend analysis.
PO Matching and Approval Routing
AI automatically matches invoices with purchase orders, goods receipts, vendor records, and tax data using two-way, three-way, and line-item matching to identify discrepancies and reduce manual reconciliation.
It also routes invoices to the appropriate approvers based on configurable rules and recommends approval decisions based on defined rules and historic patterns.
Exceptions are flagged for review, speeding up approvals while maintaining financial controls.
Bank Reconciliation
AI automatically matches bank transactions with invoices, payments, and ledger entries, identifies discrepancies, and flags exceptions for review.
This reduces manual reconciliation effort, improves accuracy, and enables faster financial close.
Month-End Close Acceleration
AI accelerates reconciliation steps, surfaces exceptions, and compresses close timelines.
For example, using Tipalti, an AI-enabled platform, Centerfield Media eliminated 20 weeks of manual work per year and reduced close times by 60%.
Research and Data Analysis
AI-powered predictive analytics helps generate financial forecasts, including cash flow, expenses, revenue, and profit, enabling smarter business planning.
The technology analyzes vast sets of data and identifies emerging trends, offering actionable intelligence to strengthen internal controls and improve operational resilience.
AI can assist your company with research related to taxation, accounting standards, global regulations, and economic, industry, and business research.
Business Intelligence and Decision-Making
AI creates real-time business intelligence through dashboards and embedded digital assistants, such as Tipalti’s AI Report Builder, which generates custom reports with natural language prompts.
AI-assisted scenario generation can meaningfully strengthen strategic decision-making.
Compliance and Audit Readiness
AI can support compliance workflows through automation, validation, controls, and review. For example, Tipalti uses AI to automate payee validation during onboarding and screens them against OFAC, AML, and anti–narcotics lists.
This helps ensure businesses make compliant international payments and avoid penalties.
AI can support tax compliance by tracking tax regulations, automating reporting, and identifying tax savings opportunities.
See AI in action: Watch how AI automates invoice processing, approvals, and reconciliation across the accounts payable workflow.
What’s Next: Emerging AI Capabilities in Finance
AI’s versatility is driving innovation across every area of finance, with new use cases being identified across sectors.
Organizations are adopting AI in these areas:
- Contract analytics: A combination of generative AI, NLP, and automation is helping companies analyze vendor contracts, including summarizing complex legal terms, identifying risks, and making recommendations for potential negotiation and payment terms.
- Recruitment and training: Companies are using AI to train finance staff and evaluate their performance.
- Cybersecurity: In addition to conducting cybersecurity threat analytics, AI is being used to ensure secure transactions. A Canadian bank, for instance, is merging AI with blockchain to strengthen the security of financial transactions, according to KPMG’s AI in finance report.
How to Adopt AI in Accounting Responsibly
Before adopting AI in accounting, it is important to establish a robust AI governance framework to ensure its responsible and effective deployment. Notably, 52% of firms surveyed by Thomson Reuters had no AI policy in place.
Leading implementers of AI, however, have developed AI governance frameworks that define how AI should be implemented, monitored, and used, according to KPMG’s report.
These frameworks help safeguard data privacy and security, promote ethical AI use, and establish accountability.
Once a framework is in place, organizations can follow the 6-step implementation roadmap given below:
1. Assess and Improve Data Quality First
As discussed earlier, realizing the full benefits of AI in accounting depends on high-quality data.
Here are some best practices to adopt to derive the above-mentioned benefits of AI:
| Best Practice | How Finance Teams Can Ensure AI Data Quality |
|---|---|
Develop AI and data governance policy | Develop a framework that encompasses all aspects of AI deployment and organizational commitment to responsible data and AI use. |
| Conduct a data audit | Record what data the company has, how and where data is stored, how data is collected (internal systems and external sources), how data is captured, and controlled. Assess what data the AI system has access to. |
| Maintain high-quality data | Use complete, accurate, consistent, and standardized data, and validate it regularly |
| Centralize data | Integrate data across ERP, banking, procurement, and other systems |
| Track inputs and outputs | Monitor AI inputs and outputs, document data sources, prompts, and timestamps, and maintain an audit trail |
| Monitor AI performance and model changes | Continuously test AI outputs and document AI model changes |
2. Identify Highest-Value Use Cases
The next step in AI adoption is mapping the current processes and identifying specific pain points that hinder efficiency. More importantly, prioritize the areas where AI can deliver the greatest strategic value and business impact.
According to Rob Goldstein of CPAs by Choice:
Before you even go down the AI road, make sure you’ve done a robust level of strategic planning to find out the areas where you can definitely get more value, more productivity, and where AI can help you with that.
Rob Goldstein, a partner at CPAs by Choice
According to the 2025 AFP Treasury Benchmarking Survey, the most common pain points include:
- Manual AP processes
- Cash and liquidity forecasting
- Payments management
- Improving cash flow/working capital
- Modernizing treasury operations
- Skill gaps (communication, strategic thinking, collaboration, and analytical decision-making)
3. Choose Embedded Software vs. Standalone Tools
While individual accountants often use general-purpose AI tools, standalone AI solutions are available for specific finance functions, such as screening suppliers for money laundering, terrorist financing, and other compliance risks.
However, managing multiple standalone AI tools can create fragmented workflows, duplicate data, and integration challenges. According to the 2025 AFP Treasury Benchmarking Survey, over half of respondents reported using at least 8 categories of planning tools and 10 types of reporting tools.
AI-powered AP automation platforms are now increasingly embedding fine-tuned, accounting-native AI tools that automate accounting tasks from start to finish with better accuracy while aligning with regulatory expectations.
An integrated AI-enabled AP software, such as Tipalti, consolidates data from multiple sources, streamlines AP workflows, and eliminates the need to use multiple standalone tools.
4. Pilot With a Contained Use Case and Define Success Metrics
Before going ahead with full implementation, start with one or two defined problems where AI can reduce friction or enhance decision-making.
Define the success metrics, scope, and review process before launching the pilot.
For example, if you’re piloting AI in invoice processing, success metrics could include reducing processing time by 50%, achieving 98% invoice capture accuracy, and cutting manual interventions by 70%.
Once the metrics are in place, ensure you invest in data, skills, processes,and tools to support the implementation.
Capture before-and-after metrics, such as invoice processing time, accuracy, or exception rate.
5. Establish Human-In-The-Loop Protocols and Maintain Audit Trails
Building human review into critical workflows such as payment approvals, exception handling, and compliance checks is vital to derive the optimum ROI from AI in accounting.
This should ideally be incorporated into the AI and data governance framework, which defines how AI systems, data, and decision-making processes are governed across the organization.
Maintain audit trails of AI-generated recommendations, actions, and decision logic using explainable AI for internal control review. Organizations should ensure AI recommendations are transparent and explainable so reviewers understand why an action was suggested before approving financial decisions.
6. Upskill Teams
The success of AI adoption depends on the people who implement, manage, and use it.
The right talent in place can help move AI initiatives from concept through execution.
This can require making a deliberate investment in upskilling existing finance staff, hiring new talent with AI and data analytics expertise, or creating dedicated roles to manage AI implementation.
Teams that can leverage, supervise, and evaluate AI agents in finance are better prepared to use AI responsibly and realize its full potential.
Denise Graziano, CEO of Graziano Associates, also advises addressing employee concerns through transparent communication to address their hesitations about using AI tools.
Ensure the finance staff is informed about the purpose of introducing AI, the problems it is expected to solve, and how their roles are likely to evolve.
Once the pilot is successful, expand AI into other areas that you have identified.
Maximizing the ROI of AI requires continuous refinement of processes, ongoing investment in people and skills, and regular monitoring of AI performance to ensure data, technology, and workflows remain aligned.
What the Future Holds for AI’s Role in Accounting
The benefits of AI are already evident in finance, with 60% of those currently using AI reporting they are “extremely optimistic” about the technology’s transformative power, as noted in Tipalti’s State of AI in Finance report.
A majority of finance professionals agree that AI is saving organizations time and money, enhancing decision-making, upleveling individual skills, improving the quality of work, and helping accelerate growth and scale.
Finance professionals also see AI use becoming the new norm, with current use cases expanding into more widespread agentic AI and execution automation.
But they also see the need for even more safeguards, human oversight, and control. The following predictions highlight where AI in finance is headed next.
Source: The State of AI in Finance
It comes as no surprise that investment in AI in accounting is growing.
AI in the accounting market is valued at $10.87 billion in 2026 and is projected to grow to $68.75 billion by 2031.
Mordor Intelligence
An important shift in AI adoption is the focus on operationalizing AI across finance instead of stopping at pilots.
Organizations are complementing AI deployment with governance frameworks that establish policies for data quality, human oversight, ethical use, data security, and compliance.
Clear governance frameworks, investing in staff capabilities to augment AI strengths, and strengthening data quality are some ways to optimize the benefits of AI in accounting.
AI Accounting Software: Categories and What to Look For
AI accounting software comes in different forms depending on a business’s size, complexity, and financial requirements.
Understanding these categories and the evaluation criteria can help organizations select a solution that aligns with their automation goals.
Here are the three main categories of AI accounting software:
1) AI-Native Bookkeeping Tools
AI-native bookkeeping platforms are designed primarily for small businesses and startups.
For example, Digits, an AI-enabled bookkeeping tool, automates routine bookkeeping tasks such as transaction categorization, invoicing, and basic financial reporting.
While they improve day-to-day accounting efficiency, they typically offer limited support for complex accounts payable, procurement, and global compliance workflows.
2) AI-Enhanced ERP Modules
Many ERP vendors and accounting systems, including Xero, now embed AI capabilities into their finance suites. These tools are designed to improve forecasting, automate reconciliations, detect anomalies, and assist with financial reporting.
They work best for mid-sized organizations already invested in a particular ERP ecosystem.
However, they may need additional solutions for end-to-end AP automation and managing global AP workflows.
3) AI-Powered AP Automation Platforms
AI in accounts payable streamlines the entire AP lifecycle from start to finish.
AP automation platforms leverage embedded AI to automate invoice capture, approval routing, PO matching, global payments, reconciliation, and compliance.
These platforms are well-suited for growing and enterprise organizations managing high invoice volumes, multiple entities, and global supplier networks.
Given the wide range of AI-powered AP automation platforms available, it can be challenging to determine which is right for your firm.
The table below lists the key evaluation criteria that can help you shortlist the software:
| Evaluation Criteria | What to Look For |
|---|---|
| Automation Depth | End-to-end, advanced AI-powered automation across invoice processing, approvals, payments, reconciliation, and reporting. |
| ERP Integration | Native, bidirectional integrations that synchronize data with all major ERPs and accounting systems in real time. |
| Compliance & Controls | Built-in tax validation, supplier verification, fraud detection, and access controls. |
| Human Oversight & Auditability | Human review for high-risk decisions, exception resolution, transparent AI recommendations, and clear audit trails |
How Tipalti Embeds AI Across Finance Workflows
Unlike standalone AI tools that automate individual tasks, Tipalti Finance AI embeds accounting-native AI across the entire accounts payable lifecycle, right from invoice capture and automated payment reconciliation to approval routing and custom report building.
AI is embedded in the workflows, and every AI recommendation is visible, traceable, and configurable, enabling critical human oversight and control.
This supervised automation with guardrails helps organizations improve efficiency without compromising financial controls or auditability.
Tipalti’s AI agents are designed to handle specific use cases across the AP workflow:
Invoice Capture Agent
The AI Invoice Capture Agent extracts header and line-item data from invoices in [num_languages] across multiple formats with high accuracy.
The Agent automates GL coding and validates invoice information, reducing manual data entry.
PO Matching Agent
The PO Matching Agent leverages AI and ML to match invoices with purchase orders and goods receipts, identifies discrepancies, and routes only exceptions for review.
This improves first-time match rates, reduces exception handling, and accelerates invoice approvals.
Bill Approver Agent
The Bill Approver Agent intelligently routes invoices to the appropriate approvers based on configurable approval policies, spending thresholds, departments, entities, and procurement rules.
AI Reporting Agent
The AI Reporting Agent enables finance teams to generate custom reports with natural language prompts.
The Agent analyzes spending trends, surfaces operational insights, and answers finance questions in natural language, minimizing the time and effort required for research and reporting.
Tax Form Scan Agent
The Agent automatically extracts data from tax forms to ensure supplier tax compliance and fast-track onboarding.
The true value of Tipalti’s AI-powered finance automation platform lies in its ability to deliver measurable business outcomes by replacing fragmented point solutions and improving the speed, accuracy, and efficiency of accounts payable operations.
Clients, such as LivTech, have implemented Tipalti to automate AP processes, increase operational efficiency, and scale confidently.
In the words of Cortney Grubb, Controller at LivTech:
Tipalti has turned what was once a 40-hour weekly task into just five hours, even as we’ve grown to 17 entities. Vendors get paid faster, our audit trails are stronger, and cashback rewards have added $30,000 to our bottom line—automation we’ll never give up.
Cortney Grubb, Controller at LivTech
See How Finance Teams Are Putting AI to Work
Tipalti Finance AI embeds AI Agents and an AI Assistant across your AP workflows — with the guardrails, audit trails, and human oversight finance teams require.
AI in Accounting FAQs
Can I Use AI for Accounting?
Yes. AI can automate tasks such as invoice processing, expense categorization, reconciliations, fraud detection, financial reporting, and cash flow forecasting. However, human oversight remains essential for strategic decisions, compliance, and complex accounting judgments.
Can CPAs Be Replaced by AI?
No. AI is designed to augment, not replace, CPAs. While it automates repetitive tasks, CPAs continue to play a critical role in financial analysis, regulatory compliance, audits, tax planning, and strategic decision-making.
What is the 30% Rule for AI?
The 30% rule for AI recommends that artificial intelligence solutions should handle 70% of repetitive work, while humans retain the remaining 30%.
This balance is important as AI adoption becomes widespread. By retaining 30% of control, humans can ensure oversight, creativity, and informed decision-making.
Is There a Free AI for Accounting?
Yes. General-purpose AI tools such as ChatGPT, Google Gemini, and Microsoft Copilot offer free versions that can assist with accounting tasks, such as drafting reports, explaining concepts, and analyzing spreadsheets.
However, businesses typically require dedicated AI-powered accounting or AP automation platforms for enterprise-grade automation, compliance, and financial controls.
