- May 26
- 3 min read
I didn't expect to be having the same conversation three times in one week, but there I was, watching another founder's enthusiasm deflate as they realized their expensive AI tool wasn't the solution they'd hoped for.
"But everyone said this would save me 10 hours a week," they told me, gesturing at the dashboard of their new AI writing assistant. The cursor blinked back at us, waiting for a prompt that there was no good way for either of us to write.
The AI gold rush has created this strange paradox where we're simultaneously over-automating and under-utilizing. Tools get purchased, accounts get created, and then... not much happens. Or worse - we add another layer of disjointed workflows that actually slow us down.
The truth is that most small businesses aren't failing with AI because they chose the wrong tools. They're failing because they're trying to automate processes that don't actually exist yet.
I see it constantly - founders who haven't documented their customer onboarding trying to automate it. Teams who haven't agreed on their content strategy trying to delegate it to AI. Business owners who haven't defined what a qualified lead looks like trying to build an AI-powered lead scoring system.
We're asking AI to execute against invisible standards that exist only in our heads. And then we're surprised when it can't read our minds.
Last month I worked with a service-based business that had spent nearly $6,000 on various AI subscriptions. When we mapped their actual workflows we discovered they were only using about 8% of the functionality they were paying for. Not because the tools were bad, but because no one had taken the time to define what success looked like before the implementation.
There's this quiet pressure among small business owners to appear technologically advanced. To be "data-driven" and "AI-enhanced" even when those things might not serve the actual work. I've watched founders agonize over automating processes that only happen once a quarter. Or building complex systems for decisions that would be better served by human judgment.
Not everything needs to be automated, and almost nothing should be automated first.
When I work with clients now, we follow a simple rule: Document before you delegate, delegate before you automate. It's not sexy. It doesn't promise 10x growth or overnight transformation. But it works.
What does a broken process versus a working one actually look like? The difference is subtle but significant.
A broken process is vague and reactive. It lives in someone's head and changes with their mood. There's no documentation beyond maybe a few scattered notes. Decisions feel inconsistent. Work is done differently each time.
A working process has touchpoints and boundaries. It's been written down, even if imperfectly. There are clear moments when decisions happen - Tuesdays for proposal approvals, the first week of the month for content planning, after the third client call for upgrade conversations. These moments create containers where work can flow.
These defined moments are where AI actually thrives:
In sales, it's the follow-up email sequence that always happens 3 days after a discovery call
In operations, it's the Thursday afternoon client status report that combines the same 5 data points
In marketing, it's the monthly content refresh where old blog posts get updated with new information
If you're wondering where AI could actually serve your business right now, here's a simple 3-step process:
Track your week's repetitive tasks. For 5 days, note work that feels like "I've done this before." Don't analyze yet—just log what it is, how long it takes, and how often you do it.
Look for emotional friction. Mark the tasks that make you sigh or procrastinate. These pinch points often signal prime automation candidates—not because they're the most time-consuming, but because improving them creates disproportionate relief.
Document one task's current reality. What information do you need to complete it? What decisions do you make? What does success look like? This becomes your automation blueprint.
The goal isn't to find what AI can do. It's to find what you're already doing consistently enough that AI could meaningfully support it.
So before you buy another AI tool or abandon the ones you've already invested in, pause. Map the process you're trying to improve. Document your standards and decision-making criteria. Get clear on what success looks like.
Then give the AI something concrete to work with. Not your hopes or your ambitions or your anxieties about keeping up - but your reality, as it exists today.
The tools aren't magic. They're mirrors, reflecting back the clarity or confusion we bring to them. And no amount of technological sophistication can compensate for a lack of foundational understanding.
I think there's something quietly revolutionary about this approach. Not because it's innovative, but because it's grounded in a truth we keep trying to automate our way around: real transformation isn't about finding the right tool; it's about getting clear on what we're actually building.
