A Five-Step Framework for AI Quality Control

You’ve built a skill, it’s deployed, and your team is using it. But how do you know it’s working? How do you catch problems before they become client-facing disasters? Most practices deploy a skill, assume it works, and move on. Six months later, they discover it’s been producing mediocre output the whole time. Or worse, a client gets a bad analysis because the skill went wrong and nobody noticed until the complaint came in.

The practices that win with AI have a systematic approach to quality control. Here’s a five-step framework for AI quality control that if done consistently can make the difference between a skill that’s used and trusted, and a skill that sits unused because nobody believes in it.

This blog is part of our series, How Accountants Get Real Value from AI.

Step One: How do you test on historical data?

Before your team uses a new skill on live client work, run it on past work you’ve already completed and verified. Does the skill produce the same output you did? If not, why not? What’s the difference? Is it because the AI interprets something differently or is it because your historical output was inconsistent?

This tells you where the skill is strong and where it needs guardrails. Maybe it nails the numerical analysis but oversimplifies the narrative. Maybe it gets the numbers right but misses context. Maybe it handles standard situations well but falls apart on unusual ones. Use this data to refine the skill before it touches live work.

Step Two: How do you spot-check live work?

Once you’ve deployed the skill, don’t assume it’s working. For the first month, have someone review a random sample of outputs. You’re looking for systematic errors, so places where the skill is consistently missing something or misinterpreting something. You’re not looking for every error, you’re trying to catch patterns.

Maybe you check 20% of outputs in month one. If you find no problems, you might drop it to 10% in month two. If you find a systematic issue, you pull the skill, fix it, test it, and restart the monitoring.

Step Three: How do you measure against success criteria?

Data beats intuition, so don’t guess whether the skill is working, measure it. If you don’t, you fall into the AI hype trap that’s infiltrating the accounting industry. Remember those explicit success criteria you defined? Track them. How often does the skill hit all the criteria? How often does it need adjustment? Is it right 95% of the time? 70%? 60%? Is that acceptable for your practice? For many practices, you need 95%+ because the work goes directly to clients.

Step Four: What do you do with edge cases?

Accounting has edge cases like intercompany transactions, unusual structures and connected parties. The skill will sometimes encounter a situation it wasn’t trained on. When it does, document it. Is it a one-off? Or is it a class of similar cases that comes up regularly?

If it’s a class, update the skill to handle it. If it’s a one-off, is it something your team should handle manually instead? Document the decision. That decision becomes institutional knowledge. The next person doesn’t have to wonder why the skill failed here.

Step Five: How often should you review performance?

Quarterly, pull up the skill metrics. How is it performing? Is your initial success criteria still relevant? Have new issues emerged? Are team members actually using it the way you intended or are they working around it in ways that suggest it’s not quite right?

Use these reviews to refine. A skill that improves 5% every quarter becomes significantly better over a year. That’s the difference between something your team tolerates and something they love.

Quality is the difference between a skill that your team trusts and uses consistently, and a tool that sits unused because it produces unreliable output. It’s the difference between a failed initiative and a transformational one. For further information, check out our blog, How to Audit Your Accounting Workflows Before Deploying AI.

This content may have been developed with the assistance of AI tools. Content is reviewed by a member of the Bright team before publication. Guidance on legislation can be obtained from your industry body.