Think of it as Yoga for the brain, we are going to stretch a bit.
AI in the Office:
The New Wild West
This is the world’s most sophisticated AI system. It is secure, tested, foolproof, and nothing can possibly go wrong… go wrong… go wrong… wrong… wrong… Daisy, Daisy, give me your answer do…  I’m half crazy, All for the love of you.-- HAL9000
There is a danger I am seeing with AI.
People inside companies are now using AI to build applications. At first, that sounds great. They are solving problems. They are moving fast. They are automating work. They are connecting to databases. They are building dashboards, reports, forms, scripts, and internal tools.
But here is the problem: some of them are doing it with confidential data, weak security, no standards, no testing, and no independent review.
In some companies, it is the Wild West.
People are building software that touches payroll, accounting, customer records, vendor data, pricing, contracts, and internal systems — and nobody is checking whether the application is secure, accurate, documented, or maintainable.
And then comes the second problem: what happens when the person who built it leaves?
Who inherits it?
Who understands the code?
Who knows which database tables it touches?
Who knows whether it is safe?
Who knows whether the numbers are right?
There is also a darker issue nobody likes to talk about: not every employee is honest. Most people may be trying to help, but it only takes one bad actor to create a serious problem. An employee could intentionally build in a weakness, break something on the way out, hide logic nobody understands, or quietly copy the tool, the database structure, the customer information, the pricing formulas, or the business process and take it to a future employer or competitor. When AI makes it easy for one person to build powerful internal software quickly, companies also have to think about trust, access control, audit trails, and ownership. A company should never be in a position where one employee secretly owns the keys to a system the business depends on.
This is happening right now, but it is not really new.
I remember when Lotus 1-2-3 first came out. Lotus was the spreadsheet before Excel became king, back when WordPerfect ruled word processing. People thought spreadsheets were magic. Anyone could make one, send it to someone else, and suddenly they had built a “system.”
Payroll was on spreadsheets. Pricing was on spreadsheets. Job costing was on spreadsheets. Financial projections were on spreadsheets.
The problem was that many people did not really test the math. They checked whether the answer looked about right, and that was good enough.
Until it wasn’t.
Companies lost real money, some millions, because of bad formulas, hidden cells, wrong assumptions, or broken links between sheets. Sometimes people sent spreadsheets to customers or competitors and accidentally included formulas that exposed their pricing strategy.
People often learn through pain.
The same thing is going to happen with AI unless companies put rules in place now.
13 Things Companies Should Do Before AI-Built Applications Become a Disaster
1. Create an AI application policy.
Make it clear who is allowed to build internal AI tools, what systems they can touch, and what data they are allowed to use.
2. Require review before deployment.
No AI-generated application should be connected to live company data without someone qualified reviewing the code, security, and business logic.
3. Separate test data from real data.
People should not be experimenting with live customer records, payroll, accounting, banking, or confidential business data.
4. Require documentation.
Every internal tool should have a basic explanation: what it does, who built it, what data it uses, where it runs, and who owns it.
5. Use version control.
Code should be stored in a proper repository, not sitting on someone’s desktop in a folder called “Final Version 3 New.”
6. Control database access.
AI-built tools should not use administrator database accounts. They should use limited-access accounts with only the permissions needed.
7. Test the math and business rules.
Just because the screen shows an answer does not mean the answer is right. Reports, calculations, payroll logic, billing logic, and pricing logic must be tested.
8. Protect confidential data.
Do not paste private company data, customer records, employee information, contracts, pricing, or credentials into random AI tools without understanding where that data goes.
9. Plan for inheritance.
Every tool needs an owner, a backup owner, and enough documentation so someone else can maintain it if the original builder leaves.
10. Get periodic third-party review.
For anything important — payroll, accounting, security, customer data, financial reporting, or operational systems — bring in an outside reviewer once in a while. Fresh eyes find mistakes insiders miss.
11. Use generational backups.
Backups are more important than ever. Companies need daily, weekly, monthly, and historical backups so they can recover from bad code, accidental deletion, ransomware, corrupted data, or an employee who breaks something intentionally or unintentionally. A backup from last night may not be enough if the problem started three months ago.
12. Test before going live, not after.
Do not put software into production and then start testing it, securing it, and figuring out whether it works. Do the work first. Test it. Review it. Secure it. Then put it live. Think of it like building an airplane: you do not put passengers on it first and then see if it flies. You test it many times, under controlled conditions, before trusting lives to it. Business software may not carry passengers, but it can carry payroll, money, customer records, and the company’s reputation.
13. Keep internal software internal.
If the software is only for internal company use, then design it that way. Not every tool needs to be available from the public internet. Many internal applications should run only on an internal server, behind the firewall, accessible only from the company network or through a secure VPN. There is no reason to expose a private company tool to the whole world just because someone knew how to make a web page.
AI is powerful. It can help small teams do work that used to require entire departments.
But power without discipline becomes danger.
The lesson from spreadsheets was simple: when anyone can build a system, eventually someone builds a dangerous one.
AI is giving every employee a software factory. Anyone is now a software engineer, they have to act like it, or someone will pay the price.
Now companies need rules before one of those factories starts producing landmines.
