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The best way to use AI isn’t asking it for answers. It’s giving it your worst, most repetitive work.
Think about your week. What do you do over and over that needs no real judgment? Summarizing calls, reformatting data, drafting the same email with small changes, writing first-pass copy.
That’s where AI pays off the most. Not because it’s smarter than you, but because it never gets bored. ⚡
A simple test: if you could explain the task to an intern in 2 minutes, hand it to AI. If it needs your taste, context, or a hard call, keep it.
Use AI for the boring 70%, and spend your own energy on the 30% only you can do.
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Not every repetitive business task deserves to be automated.
Before building an AI workflow, I'd ask five questions:
Frequency: How often does this task happen?
Time: How much manual effort does it consume?
Consistency: Does the process follow repeatable steps?
Risk: What happens if the automation gets it wrong?
Value: What could the team do with the time saved?
A task that takes five minutes once a month probably doesn't need an elaborate automation.
But a task that takes 20 minutes every day, follows predictable steps, and regularly gets delayed might be a good candidate.
Even then, automation isn't always the answer. Sometimes a clearer process or a simple rule is enough.
My approach is to understand the bottleneck first, then decide whether AI, automation, a simpler workflow—or no change at all—is the right solution.
What's one repetitive task in your business that you'd love to eliminate?
#WorkflowAutomation #AIAutomation #BusinessOperations
I find the point about simple rules often being enough is critical. Many teams skip that step and jump straight to complex n8n workflows, only to realize a basic if then logic or a shared checklist solved the issue without the maintenance overhead.
Picture a consultant who takes 8 sales calls a week.
After each one comes the same routine: replay the recording, write notes, update the pipeline sheet, write the follow-up email. Call it 30 minutes a call. That is 4 hours a week, and on busy days the follow-up slips to tomorrow.
I built an n8n workflow that takes that routine away:
→ Fireflies records and transcribes the call
→ As soon as the transcript is ready, AI writes a summary, the open questions, the next step and whether the prospect is ready to go ahead
→ A follow-up email that fits that outcome is saved in Gmail drafts, addressed to the person on the call
→ The call is logged in Google Sheets and the notes land in your inbox
Nothing is sent automatically. You read the draft, change a line if you want, and press send.
For that consultant, 4 hours of admin a week turns into a few minutes of reading, and every prospect hears back the same day.
I tested it on a real recorded client call and have submitted it to the n8n template library as a free template. If you want it set up for your business, send me a message.
My AI assistant runs all day. The expensive model only does the thinking.
Most requests need no AI at all. "Call me in 30 minutes" is handled by plain code. Reminders, schedules and limits are code too.
Short tasks like sorting and scoring go to fast models on a free tier.
The big model only gets planning and long conversations, where it actually has to think.
If it hits a limit, a backup model answers instead of the app going silent.
If your AI app's bill keeps growing, check what really needs the big model.
What does your app send to the most expensive model?
Same pattern holds in outbound tooling. Filtering, dedupe and enrichment lookups are plain code. The model only writes the first line once the row is already verified. Cuts cost and the weird outputs.