Your AI-Written Code Is Working Fine — Until It Isn't

AI-generated code can build your entire backend in an afternoon. It can also leave you completely helpless the morning it decides to stop working — and you have no idea why.

Let's be honest about what's happening right now in small and mid-size strategy firms. Someone on your team — maybe you — has discovered that ChatGPT, Claude, or Cursor can write functional code in minutes. You asked it to build a client onboarding form that drops data into a spreadsheet. It worked. You asked it to automate a weekly report. It worked. You asked it to connect your CRM to your invoicing software. It worked. And now you have a small but growing ecosystem of AI-written scripts, automations, and integrations that run your business — and that exactly zero people on your team actually understand.

That's not a knock on you. It's a genuinely useful way to move fast. The problem isn't that you used AI to write the code. The problem is what happens the first time something breaks and you're sitting there staring at 200 lines of JavaScript like it's written in ancient Sumerian. Because it will break. APIs change. Rate limits get hit. A field gets renamed in your CRM. A library gets deprecated. And when it does, all that speed you bought upfront comes due — with interest.

The Name for This Problem Is Comprehension Debt

Technical debt is the classic term for code that works today but creates future problems because it was built hastily or without foresight. Comprehension debt is the newer, uglier cousin: it's what accumulates when you're running systems you don't understand at all. Not "sort of understand." Not "understand well enough." Zero. The code is a black box that produces outputs, and your only relationship with it is hoping it keeps doing that.

For a strategy business — consulting firms, advisory shops, boutique agencies — this is a specific kind of risk. You're not a tech company with an engineering team on call. When the automation that sends your client deliverable summaries stops firing, you find out because a client emails you asking where their report is. When the script that pulls your pipeline data into your weekly leadership meeting throws an error, someone is copy-pasting numbers from five tabs at 8:45am. The failure mode isn't dramatic. It's just quietly embarrassing and expensive.

What Makes AI-Generated Code Especially Brittle

AI models write code that works for the prompt they were given, at the moment they were given it. They don't know your broader system. They don't know that the API you're calling has a sandbox mode and a production mode and you forgot to switch the key. They don't add monitoring, alerting, or fallback logic unless you specifically ask for it — and most people don't know to ask. The result is code that is technically correct in isolation and completely fragile in the real world.

Here's a concrete example. A strategy firm we worked with had built an AI-assisted intake process: a form submission triggered a series of automations that created a project folder, sent a welcome email sequence, and logged the new client in their tracking sheet. Worked perfectly for four months. Then the email platform they used changed how it handled API authentication tokens, and the whole chain silently failed. Not loudly — silently. New clients were filling out the form. The folder was being created. But the emails weren't sending and the sheet wasn't updating. They found out three weeks later when a client mentioned they never got their onboarding materials. By then, six clients had fallen through the gap.

Three Questions to Ask About Every Automation You're Running

You don't need to become a developer to manage this risk. You need to ask better questions before you trust a system with anything important. For every automation or AI-built integration in your business, you should be able to answer these three things:

• How will I know if it breaks? If your answer is "someone will eventually notice," that's not an answer. You need a failure alert — an email, a Slack message, a dashboard that goes red. If the system doesn't have one, it needs one.
• What does it touch? Map out every system the automation connects to. If any one of those systems changes — even slightly — your automation may stop working. The more integrations, the more exposure.
• Who can fix it? This doesn't have to be an internal person. But someone needs to have the context, access, and capability to debug it when it fails. "I'll just ask AI again" is a plan, but it's a slow, unreliable one when a client is waiting.

The Strategic Answer Isn't to Stop Using AI

Nothing here is an argument for going back to doing things manually or hiring a full-time developer to babysit your automations. AI-assisted development is a legitimate competitive advantage for small strategy firms. The answer is to build with more intention — and to treat your automations the way you'd treat any other business system that clients depend on.

That means documentation. Not for the code itself, but for what each automation does, what it connects to, and what "working correctly" looks like. It means building in monitoring so failures surface immediately rather than quietly. It means periodically reviewing what you've built — not auditing the code, but asking whether each system is still doing what you need, and whether the tools it depends on have changed. And it means having a relationship with someone who can actually look under the hood when things go sideways, rather than figuring it out from scratch under pressure.

The firms that are winning with AI right now aren't the ones who moved fastest. They're the ones who moved fast and then took fifteen minutes to think about what they'd just built. That gap — between shipping it and understanding it well enough to own it — is exactly where comprehension debt lives. Close that gap, and AI becomes a durable advantage. Leave it open, and you're one API deprecation away from a very bad week.