Part 3 of 3 in our series on decoding AI agents in accounting

AI is only as good as the data it’s built on.

That sounds obvious but in accounting, most “AI agents” are deployed without a serious data strategy. And that’s why they fail.

The State of Data in Accounting

The numbers tell the story:

• 79% of professionals believe AI will transform their work.

• Only 14% say they have an AI strategy.

In other words: lots of optimism, very little foundation. Without a structured approach to data - cleaning it, connecting it, and learning from it - AI agents in accounting are just guessing.

The Silo Problem

Most finance data lives in silos:

• ERP systems
  
  

• Bank feeds
  
  

• Spreadsheets
  
  

• CRMs
  
  

• Billing platforms

Agents that only see one silo can’t understand the full picture. An invoice that looks wrong in isolation might make perfect sense when compared to bank data or historical vendor behaviour. Without that integration, agents misclassify, drop, or escalate unnecessarily.

Why Data Quality and Feedback Loops Matter

Even with access to multiple systems, agents need more than raw data. They need structured feedback loops.

• Garbage in, garbage out. If the input is messy, the output will be worse.
  
  

• Exceptions as training data. Every time an accountant resolves an exception, the system should learn.
  
  

• Auditability. Finance leaders need to see why the agent made a decision - not just the decision itself.

Without these elements, agents can’t improve. They’ll remain stuck at “demo-ready” rather than “real-world reliable.”

What a Data-Driven Agent Looks Like

A true accounting agent treats data as its foundation:

1. Unified view. It connects ERPs, banks, spreadsheets, and CRMs into a single picture.
   
   

2. Learning loops. Every resolved exception strengthens the system.
   
   

3. Transparency. The agent explains why it made a choice, so accountants can trust (and audit) its reasoning.

This is how agents earn credibility: by showing their work, not hiding behind percentages.

Why This Matters for Finance Teams

At the end of the day, finance teams don’t need toys. They need tools they can trust. Data strategy is what turns an AI demo into a dependable partner.

When data is integrated, clean, and reinforced by human feedback, agents stop being black boxes. They become collaborators - handling repetitive tasks while accountants focus on judgment, strategy, and insight.

The Path Forward

This is the final piece of the puzzle. Context, workflows, and data - together - are what make real accounting agents possible.

At Stacks, we’re building on these three pillars to deliver agents that finance leaders can actually trust. Not hype. Not inflated numbers. Just automation that works in the real world.

👉 This wraps up our 3-part series on Decoding AI Agents in Accounting. If you’d like to see Agentic in action, join our waitlist or attend our London launch event.