I tested 5 AI workflows this month. Here's what actually worked.

I spent the last month testing five different AI workflows in my business. Two worked, three didn't. The two that worked saved me about 10 hours per week. The three that failed wasted 15 hours trying to force them to stick. Here's what I learned about building AI workflows that actually last versus the ones you abandon after a week.

I built a workflow to automate my weekly content planning. It pulled from transcripts, analyzed themes, suggested topics, and drafted outlines. It was beautiful, complex, and sophisticated but I used it twice. Why? Because using the workflow required me to remember six different prompt variations, check three different outputs, and manually reconcile conflicts between what the AI suggested and what I actually wanted to write about. The original process was simpler.. read transcripts, jot down ideas, pick one.

The workflow that replaced my morning email triage? I use it every single day. Because it requires zero thinking. I open my inbox, run the workflow, review three categorized buckets (urgent, respond today, archive), and I'm done. The difference is that one workflow eliminated decisions. The other one added them.

If your AI workflow requires you to think harder than the original task, you won't use it. The friction needs to go down, not sideways.

Most people build workflows that automate execution. Click this, fill that, send here. That's helpful but it isn’t really all that transformative. The workflows I’ve kept have helped automate the decision-making that I’m doing manually.

For example, I built a workflow that scores incoming leads from our website forms. It pulls the form data, checks the company domain against our ICP criteria, analyzes their stated challenge against our service offerings, and assigns a priority score.

Before this workflow, I'd read every form submission and think "Is this a good fit? Should I respond today or next week? Is this a tire-kicker or a real opportunity?"

Now the workflow makes those decisions. I just review the scores and act.

The task (reading the form) took 2 minutes. The decisions (scoring the lead) took 5 minutes of context-switching and pattern matching.

Automating the 2-minute task saved me 2 minutes. Automating the 5-minute decision saved me mental energy and inconsistency. Your brain is expensive.. use AI to make the repeatable decisions, not just to click the buttons.

I tried building a workflow that required me to export data from one system, upload it to another, run the workflow, then import the results back. I used it maybe one time.

The workflow that stuck? It lives in the tools I already use. My email workflow runs in Outlook. My transcript analysis runs in the folder where I already store transcripts. My lead scoring runs automatically when a form is submitted.

I didn't have to change my behavior to use the workflow. The workflow adapted to my existing behavior. This is the difference between "I should use this" and "I can't imagine not using this."

If your workflow requires you to go somewhere new, remember a new step, or break your existing routine, it won't stick. Build the workflow into the path you're already walking.

The workflows that failed tried to automate an entire process end-to-end.

These failed because they tried to solve too much at once. Too many edge cases, too many dependencies, and oo many places where the AI guessed wrong and I had to intervene. The workflows that worked started with one painful decision inside a process.

One decision with clear inputs, clear outputs, and is easy to validate. Once that worked, I expanded but I started pretty narrow. If you're building your first AI workflow, don't try to automate the whole job. Automate the one decision that's burning the most mental/cognitive load.

I tried building a workflow to prioritize my weekly tasks. It failed spectacularly. Why? Because I couldn't articulate the decision rule I use to prioritize tasks. "Is this urgent? Important? Depends on what's happening this week. Depends on client needs. Depends on how I'm feeling about momentum."

The inputs were too fuzzy and the logic behind decisions were too dependent on me giving AI context around too many things. AI couldn't replicate what I couldn't explain (this is true for us humans too..).

The workflows that worked had clear decision rules:

I could write those rules down so AI could execute them. If you can't clearly define the way decisions are made, AI is guessing and the workflow won’t work. Guessing burns trust and burned trust means you stop using the workflow.

Before you build the workflow write down how decisions are made. If you can't you're not ready to automate it yet.

Start there and build one workflow and use it for a week. If it sticks, then keep going and expand upon it. If it doesn't, figure out why the friction is higher than the benefit and fix that before you build the next one. The goal isn't to automate everything, it should be to automate the decisions that drain your energy so you can spend that energy on the work that matters.

Most AI workflows fail because they add complexity instead of removing it. The workflows that stick eliminate decisions, fit your existing behavior, start narrow/specific/focused, and execute based on clear rules/decisions. You don't need sophisticated workflows. You need workflows that make your day easier without requiring you to think about them.

Start with one painful decision, automate that, then build from there.

Onward & upward,
Drew

P.s. If we haven't met yet, hello. I'm Drew Burdick, Founder and Managing Partner at StealthX. We work with brands to design and build great customer experiences that win. I share ideas weekly through this newsletter and over on the Building Great Experiences podcast. Have a question? Feel free to contact us. I'd love to hear from you.