Back to Blog
There’s a lot of excitement about AI in accounting right now—and just as much confusion. According to a recent SMB survey, 31% of business owners haven’t adopted AI because they don’t see how it would help, and another 30% say they simply don’t understand how to use it. When leaders say they want “AI for accounting,” they’re often reacting to something simpler: too much manual work.
“They’re looking for more efficiency in their workflows, more automation,” says Carly Crossland, Product Manager at Accounting Seed. “But a lot of what I’m seeing is they don’t know how or when to use AI right now.”
That lack of clarity creates hesitation. Without a clear sense of which tool fits which task, businesses delay decisions and leave both automation and AI underused. Understanding where each one applies helps teams focus their effort and avoid investing in tools that don’t match the problems they’re trying to solve.
Automation vs AI: different tools for different problems
Before adding AI to your accounting stack, it helps to understand what automation and AI are designed to do.
- Accounting automation follows predefined rules using “if-then” logic: if an opportunity closes in your CRM, then trigger an invoice. If a payment comes in, then match it to the corresponding billing. If an invoice hits 30 days overdue, then send a reminder email. These processes work the same way every time, which is exactly what you want for repetitive tasks that don’t require judgment.
- AI in accounting handles variability. Rather than following a script, it can analyze data, recognize patterns, and make recommendations based on context. AI doesn’t need you to define every rule in advance; it can identify a potential duplicate payment even if the invoice numbers don’t match exactly, or flag an expense that looks unusual compared to historical patterns. But as is the case with automation, AI is only as good as the data it can see. When information is scattered across disconnected systems, AI can’t connect the dots.
Where accounting automation shines
Small business owners spend 20 hours per week on accounting tasks on average—with one in five logging 30 hours or more. Much of that time goes to repeatable work: posting journal entries, sending invoice reminders, matching payments, and routing approvals.
These tasks are strong candidates for automation.
“You really wouldn’t want to use AI for something that you know needs to work the same way every single time because it’s just not necessary,” Crossland says. “You can use automation and just have that run in the background.”
Even so, adoption remains uneven. One survey found only 49% of SMBs use digital tools to automate billing, invoicing, and payment collection.
“I still see plenty of organizations where they don’t know automation can help them,” Crossland notes. “They’re still doing things manually that they could solve with automation right now.”
|
Use automation when the task:
Examples: invoice generation, payment matching, approval routing, recurring journal entries, overdue payment reminders |
|---|
Where AI adds something new
AI starts to make sense when rules alone fall short—when tasks involve exceptions, nuance, or patterns that change over time.
Smarter collections
Traditional collections automation treats every customer the same. A reminder goes out five days late, regardless of history or relationship. It works, but it can be blunt.
Late payments remain a major issue. Nearly half of small business owners say delayed customer payments are one of their biggest cash flow problems. At the same time, aggressive outreach can strain relationships with otherwise reliable customers.
“The customer might think, ‘I know this person, and this is unlike them.’ And as a business, you don’t want to come off harsh and hurt an established relationship,” Crossland explains.
AI can adjust timing and tone based on past behavior, waiting a few extra days for a dependable customer or tailoring outreach so it feels more personal. These scenarios are hard to manage with fixed rules because they’re not the same every time.
Fraud detection
Nearly 80% of organizations reported being hit by payments fraud attacks or attempts in 2024. Automation can alert you when bank details change, but it can’t assess whether that change looks normal or suspicious.
“You can say, ‘If these exact fields match, flag it,’” Crossland says. “But what if the same invoice shows up across two lines of business? Automation doesn’t understand context.”
AI can look across vendors, amounts, timing, and behavior patterns to surface risks that don’t fit a single rule.
Duplicate payment prevention
Beyond exact-match rules, AI can identify potential duplicates even when the data doesn’t line up perfectly by comparing invoice amounts, dates, and vendor names across records to catch payments that look similar but aren’t identical.
“AI is smart enough to do an exact match and also a near-match,” Crossland explains. “It’ll point out, ‘You have these matching fields, but I also see these payables across different companies.’ That type of pattern recognition is something you’d have to specifically program automation to catch—and only after the problem already happened.”
Trend analysis and anomaly detection
Financial reporting is another area where AI can add significant value. Rather than waiting for a CFO to manually review reports, AI can continuously monitor your data and flag when something looks off such as revenue dipping unexpectedly, expenses spiking in a particular category, or patterns emerging that warrant attention.
“A CFO would go in and probably analyze these details,” Crossland says. “But the AI can be more proactive. It says, ‘Hey, I’m noticing this is a pattern right now.’ Automation would not be able to handle something like that.”
In one survey, 38% of accountants said predicting future performance is a top area they want to improve with better technology. AI-supported analysis helps address that need by highlighting issues sooner.
|
Use AI when the task:
Examples: detecting duplicate payments with mismatched data, personalizing collection outreach, flagging unusual expenses, identifying financial trends |
|---|
The foundational data AI requires
Many businesses run into trouble here: AI is only as useful as the data it can access.
Research shows that 85% of SMB finance leaders want AI capabilities embedded in the financial software they already use. At the same time, 90% of SMB owners said they wish they could combine their disparate tools into a single platform. When your CRM, accounting system, and payment tracking live in separate databases, AI can’t easily connect the dots.
“Your data being in one place is the most important piece,” Crossland says. “Without that, you don’t have full visibility. That challenge has existed with automation for years, but it becomes more pronounced with AI.”
External tools like ChatGPT highlight the issue. Ask a generic AI whether you sent a contract, and it can only suggest where to look.
“If an AI tool isn’t connected to your systems, it can’t answer questions about your business,” Crossland says. “An embedded AI has both access and context.”
3 steps to implementing AI in accounting
For SMBs deciding where AI fits, Crossland recommends a simple framework.
1. Get your data in order.
Many small businesses still re-enter data between systems. Today, 79% of SMB owners use two or more digital tools to run their business, often duplicating work. Centralized or well-integrated data is a prerequisite for meaningful AI use.
2. Separate automation from AI use cases.
Map your workflows and identify which tasks should behave the same way every time and which require judgment or interpretation.
3. Start with a specific problem.
SMBs that succeed with AI typically begin with a focused issue—such as reconciliation delays or fraud concerns—rather than attempting broad changes all at once.
Ready to build the foundation for smarter accounting?
Because Accounting Seed runs on Salesforce, data flows from opportunity to billing to cash in a single database. That shared structure supports both automation and AI without relying on disconnected systems or manual reconciliation between tools.
Automation is the starting point. Accounting Seed automates core accounting workflows across AR, AP, billing, and the general ledger to handle repeatable, rules-based work like invoice creation, payment application, approvals, and close tasks. With sales and finance operating from the same system, teams can automate processes end to end instead of stitching together point solutions.
AI Agents by Accounting Seed work on top of automated workflows, using the same underlying data to handle tasks that involve variability, exceptions, or analysis:
- A Collections Agent that analyzes invoices and predicts payment timing
- A Bill Pay Agent that flags duplicates and early payment discounts
- A General Ledger Agent that supports transaction queries and close activities
Each agent works from the same data set, delivering insights inside the system your team already uses.
Book a demo to see how accounting on Salesforce supports both automation today and AI where it makes sense.
See Accounting Seed in action
See how accounting on Salesforce can eliminate the need for costly integrations—and silos of mismatched information—by sharing the same database as your CRM.
