How do I keep AI from making mistakes in my business?
Guided by Otto, written by Something Clever Co., updated
You cannot make AI perfect, but you can make its mistakes small, visible and easy to undo. Put a human approval step in front of anything customers see or money touches, limit what each automation can access, keep a record of what it did, set spending limits and alerts, and only let it do more on its own after it has a clean track record.
Why this worry is reasonable
If you have used ChatGPT or Claude for a while, you have seen it get something wrong with total confidence. A made up detail in a summary. A wrong date. A polite email that promises something you do not offer. In a chat window, that is a small annoyance. Once AI is connected to your inbox, CRM, billing or website, the same mistake can reach a customer.
The goal is not zero mistakes. People make mistakes too. The goal is to design the work so AI errors are caught early, contained when they slip through, and easy to reverse.
Layer 1: Match oversight to the stakes
Start by asking a simple question for each task: what happens if this is wrong? A bad internal summary costs a few minutes. A wrong invoice or a promise to a customer costs more.
A simple way to think about it: AI can suggest while a person acts, AI can do the work while a person approves it, or AI can act on its own on routine work while a person checks in on a schedule. The higher the stakes, the more a person should be involved.
Layer 2: A person approves what matters
An approval step is a point where the work stops and waits for a person. It is the single most effective way to keep mistakes away from customers. Put one in front of anything customers see, involves money, changes records others rely on, or is hard to undo.
The trick is to make approvals fast. A clear preview with an approve or reject button gets real attention. A wall of text gets rubber stamped.
Layer 3: Limit what each workflow can touch
Give every AI workflow only the access it needs for its one job. A workflow that summarizes calls into your CRM does not need access to Stripe. If something goes wrong, limited permissions keep the damage small.
Layer 4: Keep a record
Write down what the AI did, when, with what input, and who approved it. When something odd happens, you can trace the cause instead of guessing. The record is also how you decide whether the AI can safely do more on its own.
Layer 5: Spending limits and alerts
Some AI mistakes are not wrong answers but runaway behavior: a loop that retries all night, or a workflow that processes far more than expected. Most AI tools let you set a spending limit. Add alerts that tell a person when activity looks unusual.
Layer 6: Give it better inputs
Many errors start with vague instructions or missing context. Give the AI written rules, examples of good output, and access to the right reference material (your price list, policies, FAQ). Tell it what to do when it is unsure, which is usually “ask a person.”
A quick comparison
| Safeguard | What it prevents | When to use it |
|---|---|---|
| Approval steps | Wrong output reaching customers | Work that customers see, involves money or is hard to undo |
| Limited permissions | Small errors becoming big ones | Every workflow |
| A record of what happened | Not knowing what happened | Every workflow |
| Spending limits and alerts | Runaway loops and surprise bills | Anything billed by usage or running unattended |
| Clear instructions and reference data | Made up answers, or answers that break policy | Every workflow, updated over time |
| Undo and version history | Mistakes that stick | Websites, documents, records |
When AI is the wrong tool
If a task has no clear right answer, depends on reading a person’s mood, or carries legal weight, keep it human. AI can help prepare, but a person should decide. And if a process is messy or undocumented, AI will make it messier. Fix the process first.
Common mistakes
- Trusting confident answers. Confidence is not accuracy.
- Approvals nobody actually reads. If reviewers click approve without looking, the step is decoration.
- Giving AI broad access “to be safe.” Broad access is the opposite of safe.
- No record. Without one, every mistake is a mystery.
- No spending limit. A small bug can become a large bill overnight.
- Letting AI run on its own because a demo looked good. Earn it with real results, over time.
