3 Areas Where AI Loses the Trust of Accounting Teams

Hi everyone,   

Most automation projects in accounting do not fail loudly. They fail quietly. 

Most automation works well at the start. Standard transactions process cleanly, bank feeds reconcile, and journals flow through the system.

The cracks appear when real-world behaviour creeps in, like mismatched payments, partial invoices, or clients doing things differently from the month before. These are exceptions, or edge-cases. 

In accounting, exceptions are the transactions that do not follow the expected rules, and they are where automation is truly tested. When exceptions become the norm, teams stop relying on the system and revert to old habits. 

Here are three areas where automation usually breaks, and where AI can genuinely change the game if it is designed properly. 

1. Exceptions Are the Work, Not the Edge 

Most firms treat exceptions as interruptions to be cleared as quickly as possible. That is a mistake. Exceptions are where patterns surface, judgement is exercised, and real learning happens. When AI is used only for straight-through processing, it never gets exposed to the situations that actually define accounting quality.  

But when every exception is captured, reviewed, and fed back into the system, the tool and teams both improve. Over time, fewer issues escalate and the same mistakes stop repeating. That is when automation begins to feel like leverage rather than noise. 

So, what do you need to do? 

• Map your most common exception types before automating anything and design workflows around them. 

• Train AI models on real historical data, including errors, reversals, and adjustments. 

• Measure success by how many exceptions are resolved inside the system, not how fast the happy path runs.

   

2. Human Judgement Has to Stay in the Loop 

The fastest way to lose confidence in any AI system is to present results without context. Accountants often lose trust when they cannot see how a decision was made. 

If staff cannot trace why a transaction was flagged, adjusted, or left untouched, they disengage. They start double checking everything or ignoring the system altogether.  

AI only becomes useful when humans can understand its reasoning well enough to challenge it and refine it. That is how trust is built over time. 

Here are some follow-ups to action: 

• Require every AI driven exception to come with a clear reason and suggested action. 

• Keep humans responsible for final judgement on material items. 

• Use exception reviews as training moments so the system improves with every decision. 

3. Exception Handling Is Where Capability Compounds 

In many firms, AI is introduced after workflows are already broken. Legacy processes, inconsistent data, and unclear ownership all get passed down to the tool and then everyone wonders why it struggles.  

When AI is embedded early, alongside real accounting work, it learns in context. It sees how exceptions are resolved, which ones matter, and which ones are noise. That is the difference between a system that constantly needs supervision and one that quietly gets better over time. 

So, how can your team ensure AI earns its keep? 

• Track recurring exception patterns and feed them back into the model regularly. 

• Build shared playbooks so teams respond consistently to similar issues. 

• Review exception metrics monthly to see whether complexity is reducing or just shifting. 

The firms that win with AI will not be the ones with the most automation. They will be the ones that respect where automation breaks and invest properly in handling what comes next. 

How We Do it at Samera 

That is also how we think about AI at Samera. We focus on building systems that work in the real world of accounting, where exceptions are expected, and judgement still matters.  

Our approach is to design AI that learns from real transactions, adapts to unusual cases, and supports accountants rather than replacing them.  

If you are interested in how we approach AI with that mindset, you can explore more here: 

Cheers, 

Arun