Artificial intelligence is increasingly being used to perform jobs that once required somebody to sit in front of a computer and complete them manually. AI can read customer enquiries, prepare responses, organise information, categorise products, help produce quotations, update business systems and decide what should happen next within an automated workflow. When everything works correctly, the benefits are easy to see. Work happens faster, repetitive administration is reduced and employees have more time for the jobs that genuinely require their attention. But there is another question businesses need to consider before handing more responsibility to AI: what happens when it gets something wrong?
It is an important question because AI isn't perfect. Even an automation that performs extremely well can eventually encounter something unusual, misunderstand information or make a decision that isn't appropriate. That doesn't necessarily mean businesses shouldn't use AI. Human employees make mistakes too. What matters is what happens after a mistake occurs and, more importantly, whether the system has been designed to prevent a small mistake becoming a much larger problem.
Imagine employing somebody new and immediately giving them complete control of your customer emails, website, pricing, accounting software and sales systems. You probably wouldn't do it. They would initially have certain responsibilities, processes to follow and limits on what they could change. Important decisions might require approval from somebody more experienced. As they demonstrate that they can perform particular tasks reliably, their responsibilities might increase. There is a strong argument that businesses should think about AI in much the same way.
AI Doesn't Need Permission to Do Everything
One of the biggest differences between using AI casually and connecting it to a business is that connected AI can potentially take actions. If you're using AI to draft an email, you can read the response before sending it. If an automated system is connected directly to your email account, ecommerce platform, CRM or another business application, the consequences of a bad decision can be greater because the AI's output may trigger something else.
For example, imagine an AI system that receives product information and prepares it for an ecommerce website. If it misunderstands a specification while creating the description, that information could potentially reach the customer. If an AI sales assistant misunderstands a customer's request, it might prepare an inappropriate response. If an appointment-setting system misunderstands a date, it could attempt to create the wrong booking.
The answer isn't necessarily to avoid connecting AI to anything. Instead, the workflow can be designed so the AI only has the authority it actually needs.
An AI system might be allowed to prepare information but not publish it. It might be able to draft a quotation but require approval before sending it. It might handle ordinary customer questions automatically while unusual conversations are passed to somebody in the business. The level of automation can depend on the risk involved in the action.
The Mistake Isn't Always the Biggest Problem
Suppose an AI system makes one incorrect decision out of 1,000. The mistake itself may be easy to correct. The bigger question is whether the automation notices anything unusual or simply continues.
This is where designing the workflow around the AI becomes important.
If every AI output is automatically accepted as correct, one incorrect decision can potentially travel through several connected systems. Incorrect information could be entered into a spreadsheet, passed into another application, used to generate something else and eventually reach a customer.
A better workflow can introduce checks at important points. Information might need to meet certain requirements before continuing. Particular actions might always require approval. Unusual results can be held for review. Missing information can stop the workflow rather than being guessed.
The objective isn't to inspect every single action manually because that would remove much of the benefit of automation. The objective is to decide where a mistake matters enough to justify a check.
Some Mistakes Matter More Than Others
Not every AI mistake carries the same risk.
If AI suggests a slightly different wording for a social media post, the consequences are probably relatively small. If it changes the price of a £10,000 product, sends incorrect financial information or changes an important customer booking, the consequences could be much greater.
That means businesses don't necessarily need one set of rules for every AI automation.
Low-risk tasks can often be allowed to operate with greater independence. Higher-risk tasks can have tighter controls. A system might automatically organise information while requiring approval before that information is sent externally. Another might automatically respond to common questions but immediately hand over complaints, unusual requests or sensitive conversations.
This is similar to the way responsibilities work inside a normal business. Different jobs have different levels of authority.
AI should be no different.
Sometimes the Best Action Is No Action
One of the most useful behaviours an AI automation can have is the ability to stop.
There can be pressure when designing automation to make sure every input produces an output. A customer email arrives, so something must be sent. A product enters the workflow, so a category must be chosen. A quotation request arrives, so a quotation must be produced.
But forcing AI to make a decision when the information isn't clear can create unnecessary risk.
If a product doesn't clearly match an existing category, perhaps it should be flagged for review. If a customer request could be interpreted in two different ways, perhaps the system should ask a question. If essential quotation information is missing, perhaps nothing should be generated until the missing details have been supplied.
An automation that occasionally stops isn't necessarily broken. Sometimes stopping is exactly what a reliable system should do.
Human Approval Doesn't Defeat the Point of Automation
There is sometimes an assumption that successful AI automation should completely remove people from a process. In reality, some of the most useful workflows can deliberately keep people involved at specific points.
Imagine a process that previously required an employee to spend 20 minutes gathering information, entering data, performing repetitive checks and preparing a document. AI and automation might complete the first 18 minutes of that process and then present the finished result to somebody for a two-minute review.
The process isn't fully automated, but the majority of the repetitive work has disappeared.
That can be considerably more practical than attempting to automate the final decision simply to claim the process is 100% automated.
The aim should be to remove unnecessary work, not necessary judgement.
What Happens After Something Goes Wrong?
Another useful question is whether the business can understand what happened after an error occurs.
If an employee makes a mistake, you can normally investigate it. You can look at what information they received, what action they took and why the process failed. AI automation should be designed with similar visibility where appropriate.
Keeping useful records of what entered a workflow, what decisions were made and what actions followed can make problems much easier to diagnose. If something unexpected happens, the business can investigate the individual case rather than simply knowing that “the AI got it wrong.”
That information can also help improve the automation. Perhaps the AI needed clearer instructions. Maybe an unusual customer request hadn't been considered. Perhaps the workflow was missing an important validation rule. Sometimes the problem isn't the AI at all; it's the process surrounding it.
A mistake can therefore become useful information about how the system should be improved.
Don't Just Test the Perfect Examples
AI demonstrations often use ideal inputs. The product information is complete, the customer asks a clear question and every field contains exactly what the system expects.
Real businesses aren't like that.
Customers make spelling mistakes. Suppliers provide incomplete information. Someone changes their mind halfway through a conversation. A spreadsheet contains an unexpected value. An email contains information in a completely different format. Two products have almost identical names. A customer replies with three words that only make sense when you read the previous six emails.
Those are the situations where an automation really gets tested.
Before relying on an AI workflow, it can therefore be useful to deliberately give it awkward situations. What happens when information is missing? What happens when two answers appear possible? What happens when a customer contradicts something they said earlier? What happens when the AI cannot find what it needs?
Testing what happens when things go wrong can be just as important as testing what happens when everything goes right.
Build the Safety Net Before You Need It
One of the most useful questions to ask when designing an AI automation is surprisingly simple: “What is the worst reasonable mistake this system could make?”
Once you know the answer, you can think about what should prevent it.
Perhaps certain actions require approval. Perhaps values outside an expected range are stopped. Perhaps the AI must use information from an approved source rather than inventing missing details. Perhaps unusual customer conversations are transferred to a person. Perhaps the system records its actions so somebody can investigate them afterwards.
The right safeguards will be different for every business and every workflow.
What matters is that they are considered before the automation is relied upon.
Treat AI Like a New Employee
There is a useful way to think about all of this. Imagine your AI automation as a new employee.
Give it a clearly defined job. Give it access to the information it needs. Explain the rules it needs to follow. Decide which actions it can take independently. Decide which actions require approval. Give it somewhere to send work it cannot confidently complete. Keep enough visibility to understand what it has done.
Then improve the process as you learn where the problems occur.
This approach is very different from connecting AI to every system and hoping it behaves perfectly. It recognises that AI can be extremely useful without pretending it is incapable of making mistakes.
At Byron AI, we believe the value of business AI isn't simply about how much work you can automate. It's about creating automation that businesses can actually rely on. That means thinking about the successful outcomes, but also designing for the unusual requests, missing information, misunderstandings and mistakes that will eventually happen in the real world.
Because the important question isn't:
“Will AI ever make a mistake?”
Eventually, it probably will.
The much better question is:
“What have we designed the system to do when it does?”
Contact Us:
Call 07905 967307 for details
Email: Byronai.uk@gmail.com
