How to Use AI in Accounting (2026 Guide)

You’ve read the news, listened to the podcasts, and worked through the thinkpieces. You already know the accounting industry is changing. But where do we go from here? According to Karbon’s 2025 State of AI in Accounting report, 85% of accounting professionals feel positive about AI’s potential – but only 37% of firms are actually investing in AI training.¹ This is starting to create a clear divide between teams that are moving forward and those who are falling behind.

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The payoff for early adopters is already showing. Firms using AI save an average of 56 minutes per employee per day, and the same report found that advanced users save 71% more time than beginners.¹ Over a year, that adds up to roughly $19,000 in value per user. Companies with a clear AI strategy are also seeing twice the revenue growth of those taking a more ad-hoc approach.

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AI in accounting adoption went from 9% in 2024 to 41% in 2025.² The technology is already being used across AP, reconciliation, payroll, reporting, and more. In this piece, we’ll explore how accounting teams can approach AI adoption in a way that makes sense for them, and what to focus on in the early days of rolling out automation.

Where to start

How to know when you’re ready for AI 

According to CPA.com’s 2025 AI in Accounting Report, readiness comes down to five areas: “technology infrastructure, data quality, team openness, compliance posture, and vendor alignment.” ³ In simple terms: can your systems connect, is your data usable, and are people willing to change how they work?

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Strong signs that you’re ready to bring AI into your accounting workflows:

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• You’re either using a cloud-based accounting system or are ready to switch to one
• You have 12+ months of reasonably clean historical data
• You’re using, or would like to use, tools that connect with APIs (e.g. banks, payroll, expense tools)
• You already have accounting workflows or processes in place, no matter how simple
• Your CFO is on board with the change and has the budget to the make it happen

Start thinking about the quick wins

It’s easy to get tangled up in the complexity of AI in accounting, especially if you’ve previously relied on more old-school, manual ways of bookkeeping. To cut through confusion, the best way to envisage how AI could work for your business is consider the simplest things it can help you with. As in, the everyday, repetitive work that needs to be done – and takes up way more time than you’d like. 

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Did you know that invoice data extraction can cut AP processing time by approximately 65–75%?⁴ Or that bank reconciliation can handle three-way matching in minutes instead of hours? Or that expense categorization regularly reaches 94% accuracy?⁵ Karbon’s 2025 research also shows that 63% of firms save time on drafting routine communications using AI.¹

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Common “first wins” include:

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• Invoice capture and data extraction
• Automated reconciliation
• Expense report categorization
• Payment and bank matching

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These don’t change how your finance team operates – they just remove friction.

Stay away from painful implementation processes

How smoothly AI is rolled out often depends less on the AI itself and more on the system behind it. Legacy ERPs weren’t built with automation in mind, meaning that adding AI usually involves long implementation timelines, patchy data migrations, and expensive setups. This slows everything down and delays time to value. 

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For the easiest rollout, choose modern, AI-native accounting software alternatives that are designed to work with full transaction history, automations, and customizable workflows from the start. This makes implementation faster and less disruptive. 

What not to automate

Not everything should be handed over to AI. Judgment-heavy work still needs people. That includes advisory conversations, complex estimates, legal interpretation, and client-specific tax strategy. And any client interactions should still be handled by humans, because trust is everything. 

Selecting the right AI tools: comparison and decision framework

The main types of AI accounting tool in 2026

AI accounting tools generally fall into four buckets:

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1: Modern ERPs
End-to-end platforms that automate finance in a unified system and scale with your business. Built to be AI-native, meaning that AI is part of the core workflow from day one. Typically cloud-based with a modern UI and a fast setup. Maybe too feature-heavy for small teams who just need the basics.

2: Legacy ERPs

Older enterprise systems designed before automation and AI became standard. Powerful and widely used by large, global businesses – but still heavily reliant on manual workflows, and AI is typically layered on later. Long implementations, high costs, and little flexibility as workflows change.

3: Starter systems
Cloud-based platforms best for small teams and early-stage businesses. Easy to set up and with AI to automate basic, everyday accounting, but they tend to struggle as transaction volumes grow or structures become more complex.

4: Dedicated tools
Software focused on a single function, like AP, expenses, or reconciliation. They’re quick to adopt but most teams need several of them, which can create data silos and integration headaches as a business scales.

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The right choice depends on your company’s size, transaction volume, technical maturity, existing systems and, most importantly, the problems you’re trying to solve.

What actually matters when choosing a platform

According to CPA.com’s 2025 research,³ a few factors consistently separate successful implementations from failed ones. Data security and compliance come first, especially in regulated environments. Integration quality matters just as much, as poor connectivity is one of the biggest causes of rollout issues. Also, clear audit trails and transparency into how AI makes decisions are key for trust and compliance.

Red flags to watch out for

• Unclear privacy policies
• No possibility to opt out of AI training on your data
• “Black-box” AI decisions with no explanation 
• Limited or outsourced customer support 
• Missing or unclear compliance certifications

Matching tools to real use cases

Different teams, different needs. If you’re an audit-focused firm, you’ll benefit from full transaction coverage and strong documentation. Accounts payable teams, meanwhile, care most about speed and accuracy, and tax teams need tools that can handle multiple jurisdictions and keep up with regulatory changes.

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Firm size also plays a role in the automation needs:

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• Small businesses & startups: Basic automation with receipt capture
• Mid-market businesses: Workflow automation, including approval routing
• Large businesses: Enterprise features with custom configurations
• Global businesses: Multi-entity and multi-currency support

Don’t forget about integrations

AI only works if it connects cleanly to the rest of your tech stack. APIs determine how flexible a system is going to be as you grow, so make sure to look at integration possibilities when researching new tools. Strong bank and payment-provider integrations are a must, but check how easy it is to bring in data from other systems too: BI, CRM, HRIS…  whatever’s important to you now, and what might be in the future.

Bringing AI in your day-to-day accounting workflows

Step 1: Acknowledge where you are, and what you want to fix

Before you start implementing AI, be honest about where work slows down today. Whether it’s manual reconciliations, messy transaction data spread across tools, or something else, you want to identify the biggest pain points and the data sources involved (banks, teams, entities…). Think about what needs to be changed to make sure AI works effectively.

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Also: think about your controls. Clear approval rules, consistent categorization, and documented workflows make automation much easier. If you’re choosing a platform that brings multiple workflows into one system, this step is about deciding what you want to centralize and how you’d ideally like AI to fit into existing processes. You don’t need to worry about redesigning everything from scratch.

Step 2: Choose, and implement, software

Once you know what you’re trying to fix, implementation becomes much simpler. How onboarding looks will depend on the tool you choose. With a modern ERP, for example, setup’s typically fast and hands-off. Older systems generally have longer, more complicated data migrations.

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Once you’re near the finish line, make sure your provider gives you proper onboarding and training. Even the most intuitive tools benefit from guided setup, especially when teams are new to AI. 

Step 3: Measure your results

Once you’ve been up and running with your new AI software for a few weeks, you can already start comparing your current performance against your starting point. Maybe that’s time spent on reconciliations, transaction accuracy, close speed, or transaction volume handled per person. Hopefully, you’ll quickly have a clear idea of AI’s value. 

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ROI doesn’t need to be complicated. In most cases, it comes down to hours saved, fewer errors, and the ability to scale without adding headcount.

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You can also follow this formula: Total Annual Savings = (Hours Saved × Labor Cost/Hour) + (Error Reduction × Rework Cost) - Tool Subscription Cost.

Common implementation pitfalls

Problems? Most failed implementations come down to one of these:

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1. Trying to go live without doing a data cleanup first
2. Having no manual override options in the platform you’re using
3. Receiving insufficient user training or support
4. No option for audit-trail configuration
5. No baseline metrics to work from

Where can AI be used in finance?

You actually have many options. Here are some of the most popular.

Accounts payable (AP) 

AI changes how AP work gets done by cutting out manual entry and speeding up approvals. You can upload any invoice – solo or in bulk – and OCR technology will import the relevant info, extracting line items and vendor details. The system supports flexible approval workflows tailored to your team’s structure, sends automated reminders, and keeps all spend conversations and audit trails in one place. 

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Matching and exception handling are streamlined too. Auto matching handles 2-, 3-, or 4-way comparisons automatically. AI keeps an eye on mismatches, potential duplicates, and anything that looks off, flagging up any issues before they have the chance to make it to your books. With live dashboards showing aging, pending payments, and approvals in real time, teams can handle AP faster, with less manual work.

Accounts receivable (AR) 

You can use AI to simplify and speed up how revenue turns into cash. Incoming payments are automatically matched to the correct invoices, and partial payments and overpayments can be handled without manual involvement. Invoicing is automated too: sales orders convert into invoices, taxes and commissions are calculated, credit limits are set, and transactions are posted directly to the general ledger and Accounts receivable (AR). For subscription businesses, AI can also identify recurring billing patterns and creates reusable invoice templates.

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Key outcomes include:

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• ~80% accuracy in payment delay predictions ⁶
• Automated workflows can significantly reduce billing disputes and manual exceptions ¹⁰
• Improved cash forecast accuracy

Financial reporting

AI makes financial reporting easier to understand. And of course, it’s faster too. Instead of manually tracking how numbers change over time, let AI do it for you and highlight changes over months, quarters, or years. You can also take most of the manual work out of flux analysis and threshold monitoring, and save even more time by letting AI flag unusual movements, potential risks, or even cost-saving opportunities.

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Instead of dumping raw numbers into static reports, AI is also great at turning data into clear stories. The best accounting platforms will offer AI-generated visuals and summaries (great when there’s not much prep time before a board meeting), and interactive dashboards that let each team member focus on the metrics relevant to them. 

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The type of results you can expect: 

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• >50% reduction in report preparation time ⁷
• 90% faster variance analysis ⁸
• Near-zero manual errors ⁸
• Board-ready reports generated in hours instead of traditional multi-week reporting cycles ⁸

Account reconciliation

AI can automatically build schedules for things like depreciation and amortization, learn from past reconciliations to suggest smarter matching rules, and continuously monitor accounts for unusual items that need attention. Balance changes can be tracked automatically across periods, and roll-forwards happen without extra work.

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With AI tools, month-end general ledger reconciliation time goes down from 4-6 hours to ~30 minutes. ⁹ Companies achieve near-complete matching coverage vs traditional sampling. ¹⁰ Monthly close cycles shorten by ~2.5 days. ¹¹

Audit & compliance 

AI helps make audit automation more thorough and way less disruptive. It can continuously monitor transactions to spot anomalies and emerging risks in real time, while verifying large volumes of accounting records in bulk to surface suspicious patterns early. Top AI-native ERPs also come with full change tracking built in and give you live visibility into your control environment through a unified dashboard. No more compliance gaps or waiting for periodic reviews.

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External auditors can often reduce on‑site fieldwork when more testing is automated, internal audit teams gain meaningful efficiency, and AI‑driven fraud tools help cut detection times from long reviews toward near real‑time alerts.

AI barriers and how to deal with them

Change 

Resistance to AI is fading, but it hasn’t disappeared entirely. Thomson Reuters research ¹² on U.S. state courts shows that only 9% of respondents worry about AI causing widespread job loss. Still, it’s important for companies to reinforce to their teams that AI is here to support them, not replace them.

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Most hesitation comes from a lack of familiarity, uncertainty about accountability, or just classic change fatigue. The fix is practical: invest in training, make responsibilities clear, and keep humans in the loop for key decisions, and roll AI out gradually. 

Data security and compliance

Strong security and governance are non-negotiable in accounting. Whatever AI setup you go with, it needs to support GDPR and SOX requirements through role-based access, detailed audit trails, and clear data retention rules. Encryption, regular security audits, and verified certifications like SOC 2 Type II are musts. 

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Stay in control by making sure you have these covered: 

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• Encryption at rest and in transit
• Annual security audits
• SOC 2 Type II certification verification
• Data residency compliance
• Incident response planning

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Pro tip: if you use DualEntry, all these are a given. No extra effort needed.

Measuring success and improving over time

What’s worth measuring

The easiest way to know if AI is working is to track a few clear outcomes. Most teams focus on four areas: speed, cost, quality, and impact. That might mean hours saved on AP, faster month-end closes, lower costs per transaction, or just fewer errors overall.

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As a reference point, here are some standard benchmarks for AI implementations:

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• AP automation: approximately 65–75% cycle time reduction ⁴
• Month-end close: >50% faster report preparation ⁷
• Bank reconciliation: near-complete matching coverage vs traditional sampling ¹⁰

Don’t forget to collect feedback

AI systems get better with feedback. Monthly check-ins with individual team members using the new software can help you capture edge cases and friction points. 

Scaling triggers and expansion criteria

Some signs you’re ready to expand your AI toolkit further: 

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• Model accuracy consistently above internal baselines
• Cost per transaction below $0.15
• User adoption above 80%
• Positive ROI achievement
  
  

If you’re checking off at least one one of these, it’s usually safe to expand automation to new processes. If you find performance drops, it’s better to pause instead and fix whatever isn’t working. You should treat AI as an ongoing improvement effort rather than a one-time rollout. 

Conclusion

AI is already delivering real results in accounting. The teams using it are working faster, making fewer errors, and seeing big returns. As the technology gets more powerful, more companies are moving from experimenting with AI to building it into their day-to-day workflows.

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If you’re not there yet, don’t panic. The best way to keep the process pain-free is choosing a modern, AI-native platform that fits your company’s size and complexity, rather than forcing automation onto systems that weren’t built for it. And it’s just as important to manage the transition well: supporting your accountants, keeping security and compliance strong, and tracking performance as workflows become more automated.

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Accounting will keep changing as AI improves. Businesses that adopt it thoughtfully today, with the right systems in place, are setting themselves up to work more efficiently – and compete more effectively tomorrow. Those that don’t will have a much harder time keeping up.

How to Use AI in Accounting FAQs