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AI is everywhere. It’s in the headlines, dominating LinkedIn feeds, and promised as the solution to every business problem. For finance teams, AI is supposed to speed up month-end close, automate manual tasks, and free up accountants for strategic work.
But here’s the reality: 95% of corporate AI initiatives show zero return.
Despite upwards of $40 billion being invested in AI, according to MIT research, most companies—especially in the SMB space—aren’t seeing any progress. The hype isn’t matching the results.
So what’s going wrong? Where are leaders missing the mark with their AI initiatives?
The answer is simple: most organizations are starting with the wrong foundation, the wrong approach, and the wrong expectations.
Let’s break down the three most common reasons AI is falling flat in finance and what you can do about it.
Reason #1: Starting with the tool instead of the problem
The mistake: “We need AI right now. Let’s go use it.”
What you should be thinking: “We need to solve X, Y, and Z problem. Could AI help?”
When you start with “we need AI” instead of “we have a problem,” you end up launching generic AI pilots that don’t stick. You’re hoping for results but ending up with nothing—just like that 95% MIT reported on.
AI fatigue is real. If you hear “you need to use AI” one more time, you might lose your mind. But going into any initiative with a tool-first, problem-later mindset will always fail.
How to fix it
Start by taking inventory of jobs to be done:
- What areas of your work are manual and time-consuming?
- What work can’t be solved with simple automation?
- What’s causing real headaches for your team or impacting growth?
Maybe it’s reconciling transactions, categorizing expenses, or reviewing journal entries for month-end close. Whatever it is, identify the pain point first. Then explore whether AI can solve it.
Remember: Solving your problem is more important than using AI. AI may end up being the solution, but only if you start with the right question.
Reason #2: Your data is too messy and disconnected
The problem: AI can’t help you if your sales data, services data, and financial data live in five different systems and 20 different spreadsheets.
Most finance teams don’t have a true single source of truth. So when they plug in AI tools, those tools don’t have full context, and they can’t provide accurate information because the data isn’t aligned.
The results? Incomplete at best. Misleading at worst.
What you really need is grounded data—data that’s anchored in your specific systems (your CRM, accounting system, ERP), not just pulling generic responses from the internet like ChatGPT.
That way, AI can answer contextual questions about your specific business operations.
How to fix it
Before plugging AI into your processes, unify your systems. Bring your CRM, ERP, billing, and core accounting into one platform.
There’s a common phrase in accounting: clean data in, clean data out.
The quality of your data’s output—your reporting, your insights—is directly dependent on the quality of the data you put in. This is especially true when leveraging AI.
In a nutshell: clean data first allows for smart insights second.
If your data is scattered, AI will come up short every time.
Reason #3: Your teams aren’t aligned
The problem: Finance is often content with the systems they have. They want faster insights and maybe a little automation here and there, but they’re not actively shopping for an entirely new solution just to use AI.
On the flip side, IT owns the tech stack but often treats finance as an afterthought. They’re not thinking about how the accounting software is performing or whether it’s AI-ready.
When it comes to AI, no one’s owning the outcome and no one’s working together.
That’s why AI initiatives stall out. That’s why outdated software continues to hang on. And that’s why so many finance teams miss out on the automation and AI insights that could make their lives so much easier.
How to fix it
Finance and IT need to be aligned…not just on tools, but on outcomes.
These teams should collaborate to answer:
- What are our overarching business goals?
- What are the finance goals that will support these business goals?
- What gaps can we address together?
This alignment makes it possible to co-own AI initiatives from the start and roll them out in a way that sticks.
You also need a change management plan:
- Assign ownership of the initiative (someone to quarterback it)
- Build a clear onboarding plan with step-by-step guidance
- Educate teams on how AI supports their work—not replaces it
Without change management, even good AI becomes shelfware, eventually getting labeled as “another failed pilot.”
When’s the last time you sat down with your accounting team and asked, “What gaps can we address together?” In the rush to adopt AI, we often skip the most important step: actually talking to the people who will use it.
What it looks like when AI is done right
When your data is accurate and unified, you can unlock the full potential of AI agents to do truly meaningful work.
Here’s what’s possible with a solution like Accounting Seed on Salesforce:
- Collections agent can identify all invoices over 90 days past due, draft personalized collection emails, and send them on your behalf
- Bill pay agent can detect duplicate invoices, flag fraudulent activity, and recommend how to prioritize bills—all with human oversight
- General ledger agent can pull a list of unposted journal entries in seconds, helping you close the books faster
The common thread? These aren’t just AI tools plugged into disconnected systems. They’re built on a foundation of clean, unified data (what we call grounded data).
That’s the difference between AI that delivers results and AI that delivers disappointment.
Ready to see how AI can work for your finance team? Watch the webinar and check out AI Accounting Agents by Accounting Seed.
FAQ
Q: How do you handle the fear that AI will replace jobs?
AI is not here to replace accountants. It’s here to assist them. There are far too many tasks that require human judgment for AI to fully replace the role of an accountant. What AI does is take those repetitive, time-consuming tasks off your plate so you can focus on work that requires critical thinking and expertise.
As one expert put it: “AI won’t replace accountants, but accountants who use AI will.”
The key is approaching AI as a lifelong learner. Just like when Excel first came out, there was resistance. But now look how far we’ve come, and accountants are still here, more valuable than ever.
Q: Can I use AI with QuickBooks Desktop or Sage?
If you’re using QuickBooks Desktop or Sage, it will be difficult to implement effective AI without significant customization. The challenge is that AI tools need access to unified, grounded data across your systems—your CRM, accounting software, and other platforms.
When your accounting system is disconnected from your other business systems, AI tools can’t easily pull the contextual data they need to provide accurate insights. That’s why many companies are moving to unified platforms like Accounting Seed on Salesforce, where AI agents work day one because all your data lives in a single place.
Q: What’s a realistic timeline for seeing ROI after implementing AI in finance?
With pre-built AI agents designed specifically for finance workflows, ROI can be immediate. The key is choosing a solution where the development work has already been done for you—where agents are trained, tested, and ready to go.
The real ROI isn’t just faster task completion. It’s also about freeing up your team to provide more strategic value to the company while maintaining human oversight and control.
Q: How much customization is typically needed to get AI agents working in finance workflows?
If you choose a solution with pre-built AI agents designed for finance, you should need little to no customization to get started. The heavy lifting—training the agents, building the workflows, understanding accounting language and processes—should already be done.
If you try to build agents from scratch, expect significant development work, training time, and ongoing maintenance. That’s why starting with purpose-built solutions makes sense for most finance teams.
Q: Our organization is already using AI in some areas. Where should finance teams focus their AI efforts first?
Start with the manual, repetitive tasks that consume the most time and don’t require complex judgment. Common starting points include:
- Identifying unposted journal entries
- Sending collection notices for past-due invoices
- Detecting duplicate invoices in accounts payable
- Predicting payment timing for cash flow planning
The goal is quick wins that build confidence in AI adoption while delivering measurable time savings.
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.
