Why Automating Broken Processes Creates Faster Failure

Here’s a painful pattern that plays out in practices across the UK. A practice identifies a time-consuming task and gets excited about AI. They buy a tool to automate it, implement it, and six months later it’s disabled. The team went back to doing the task manually. The tool was never used properly and the licence was wasted. Why does this happen? Because they tried to automate a broken process.

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

Why does automation lock in problems instead of fixing them?

Automation amplifies what’s there. Broken data sources, unclear rules, and inefficient handoffs all get embedded into the AI system. When you automate something that’s already broken, you don’t fix it, you just lock it in. You make it faster to fail and harder to debug. You haven’t solved anything, just introduced a new layer of complexity.

What does a real example of automation failure look like?

Let’s say you’re working on Self Assessment. Your approach to work in busy season is that clients email figures, a team member re-enters them into a spreadsheet (because the email format is inconsistent), and another team member verifies the entries (because they don’t trust the first entry). Someone else checks the software output (because you need to verify the input was correct) and finally someone files and emails the client. That’s five manual steps, each step an opportunity for data to get lost or mistyped.

Now, one of your managers comes across an AI solution that offers to automate the re-entry step. They get excited and think it’ll fix the whole process. However, bad data sources, handoffs and verification still exist. They’ve just added complexity without fixing the root problem. The AI gets blamed for a bad workflow that’s been designed to fail.

So, make sure that you fix the workflow first, then add AI to handle complex analytical work. AI works on good data that’s already been validated. The output is reliable, your team can trust it, and most of all, the AI solution you’ve paid for actually works. For further information take a read of How to Audit Your Accounting Workflows Before Deploying AI.

How does document processing show the same problem?

A practice gets an AI tool to automatically extract data from client documents. It’s a great idea in theory but the documents are inconsistent. Sometimes it’s a PDF, sometimes it’s a scan, sometimes the information is in a different order, or sometimes it’s missing entirely. The AI has to guess, let’s say that it gets it wrong 15% of the time. The team has to review every output anyway. All you’ve done is just added a review step to a manual process and called it progress.

How do you standardise before automating?

Get clients to use templates, train your team on required information and placement, and create consistent documents. Then AI will produce reliable outputs from good inputs, saving you a bunch of time.

Why does process order matter so much?

AI doesn’t solve every problem, it only amplifies wheat’s already there. If you have existing problems, then you’ll only be amplifying the problems. If you’ve got great processes and standardisation, then you’ll have better automation. Remember, design first and then automate.

The practices that win with AI start with discipline. They document their workflows, remove any waste, and clarify the rules. They make the process repeatable and consistent, then test it. once it gets the tick of approval, then they add tools such as AI to amplify what’s already working. This takes longer upfront and is a lot less glamorous than buying the latest AI tool that claims to save 10 hours a week, but it’s the approach that actually works. For further reading, check out our blog on Why Workflow Strategy Beats AI Tools for Accountants.

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.