The most common thing people do when they discover AI tools is pile them onto their existing workflows:
- They automate the outreach sequence they’ve been running.
- They use agents to produce the reports they’ve always produced.
- They build AI into the content process they’ve had in place for two years.
Then they wonder why things aren’t improving—or in some cases, actively getting worse, and faster than before.
The answer is AI doesn’t fix a broken process; it merely accelerates it. And if the process was producing mistakes before, it’ll produce more of them now, more quickly, at greater volume, with less human oversight to catch them.
The AI acceleration problem
Think about what AI actually does at a mechanical level. It takes a defined input, applies a set of instructions, and produces an output at a speed and scale no human team can match. People are excited about this, for good reason.
But speed and scale are neutral forces that amplify whatever they’re pointed at. If you point them at a well-designed, clearly reasoned workflow, you get compounding returns.
If you point them at a process full of unclear thinking, poor targeting, or bad assumptions, you get compounding errors shipped faster.
This is why the first question to ask before introducing any AI tool into your work isn’t “what can this automate?” but “is this process worth automating?”
What “accelerating the wrong thing” looks like
Here are the most common patterns:
Sales outreach that wasn’t working
A GTM engineer builds an AI sequence to send fifty personalised cold emails a day. I get a lot of these daily. The problem is the positioning was already weak, the target list was already imprecise, and the message/offer was already off. AI doesn’t fix any of that. It just delivers the wrong message to the wrong people at fifty times the previous rate. So you tank your reputation faster.
Content that wasn’t resonating
A founder uses an AI writing tool to produce three articles and 5 LinkedIn posts a week instead of one. But the one article they were producing before wasn’t driving traffic, building authority, or converting readers into anything useful. Now they have three versions of that non-result, every week, indefinitely.
Reporting that wasn’t informing decisions
A team automates its weekly performance report. The report now arrives faster and looks cleaner, but the metrics it tracks were always the wrong ones, and nobody was acting on it anyway. Automation just makes irrelevance more efficient.
In each case, the AI delivered exactly what it was asked to deliver. The problem was upstream, in the thinking that designed the workflow.
| Before AI | After AI (broken process) | After AI (sound process) |
| Slow output, low volume | Fast output, high volume | Fast output, high volume |
| Errors caught manually | Errors scaled before anyone notices | Quality maintained at scale |
| Weak results over time | Weak results, faster and at greater cost | Compounding returns over time |
| One person’s capacity | Still one person’s thinking, amplified | Genuine leverage on good decisions |
Start simple, and fix the process first
The advice I give most often is to start simple. This is because most people and teams don’t yet have a clear enough picture of where their real inefficiencies are.
Before you automate anything, map out what you’re actually doing and why:
- Where are the decisions?
- Where are the bottlenecks?
- Where does quality break down?
- Where do things take longer than they should, and what’s the reason?
That last question matters most, because a long task and a broken task look identical on a to-do list, but they need completely different responses.
A long task that’s well-designed is a candidate for AI assistance. A broken task needs to be fixed by a human first, then considered for automation.
Fix the thinking first. Then document the process. Then introduce AI at the specific points where human effort is genuinely the bottleneck.
The goal isn’t to do more things faster. It’s to do the right things better, and then let AI handle the volume.