Every Monday morning, Sarah spends 3 hours copying data between HubSpot, Monday.com, and Google Sheets. Export CSV files. Fix format mismatches. Check for errors. Compile a report.
Should be quick. Instead it eats up half her morning. By the time she's done, she's already behind on actual marketing work.
Most businesses have work like this. Tasks that feel necessary but eat up time. The hard part isn't automating them. It's recognizing which ones are worth the effort.
Here's how I figure that out.
Step 1: Find the Pattern
Repetitive work usually looks like one of these:
- Scheduled tasks. Things you do every Monday, every month, every quarter.
- Data copying. Information that lives in one system but needs to exist in another.
- Manual entry. Typing the same information multiple times or comparing data across platforms.
I worked with someone who did this every Monday: log into HubSpot, export lead data, open Monday.com for campaign budgets, then consolidate everything in Google Sheets for a report.
Simple task. Took hours. Why? CSV files had different field formats. Campaign names didn't match across platforms. By the time she fixed everything, the marketing team had already missed opportunities.
The pattern was obvious. Scheduled repetition. Three disconnected systems. Manual fixing.
Try this: Notice when you think "here we go again." That sigh before you start a task? That's usually something worth automating.
Step 2: Count the Hours
Not everything repetitive is worth automating. You need to know the actual cost.
I look at three things:
- Time spent. How many hours per week does this take?
- Missed opportunities. What important work isn't getting done because of this?
- Error fixing. How much time goes to fixing mistakes?
In one accounts payable project, finance teams spent 85% of their time on work a computer could do. Extracting data from invoices. Chasing approvals. Fixing errors. Retyping information.
The direct cost was clear. Skilled people doing data entry. But the bigger cost was what they weren't doing. They missed early payment discounts. Vendors got paid late. The finance team was a bottleneck instead of a strategic partner.
Some people on that team spent 15+ hours per week on invoice processing. That's nearly two full workdays.
Try this: Track one repetitive task for a week. Use a timer. Include the time spent fixing errors. Then multiply by how often you do it. Ask yourself: "What could I do with this time instead?"
Step 3: Find the Starting Point
Every manual process starts somewhere. Find that spot and you can stop the manual work before it begins.
I ask:
- What kicks this off? An email? A spreadsheet entry? A specific time?
- Where's the data? Which system has the information?
- Where does it need to go? What's the end result?
In one project, the trigger was simple. Someone typed a campaign name in a Google Sheet. That one action started a chain: export from HubSpot, pull from Monday.com, fix format issues, generate a report.
We made the system watch for that Google Sheets entry. Now when someone types a campaign name, the system automatically pulls data from both platforms, runs analysis, and delivers the report. No manual work.
The trigger is usually the easiest part. Everything after is where the work piles up.
Try this: Pick a repetitive task. What's the first thing you do? That's your trigger. Now list everything that comes after. Each step is a place you could automate.
Step 4: Check if the Systems Can Connect
Some systems work together easily. Others don't. Before you commit to automating something, you need to know if it's even possible.
I check:
- APIs. Do the systems have ways to connect? Is the documentation good?
- Data formats. How different are the formats between systems?
- How many systems. More systems means more things that can break.
In one project, we connected Google Sheets, Monday.com, and HubSpot. Each had different data structures. Different field names. Different API formats. It wasn't impossible, but it took work.
Sometimes the complexity is worth it. Sometimes it's not. I've walked away from projects where connecting the systems would take longer than just doing the work manually.
But usually, if the systems have decent APIs, you can make it work.
Try this: List every system in your repetitive task. For each one, search "[system name] API documentation." If most have APIs, you can probably automate it. If none do, you might need a different approach.
Step 5: Do the Math
The last step is figuring out if automation is actually worth it. Not just in time saved, but in real value.
I look at:
- Time saved vs. build cost. How long until this pays for itself?
- Error reduction. What do mistakes cost you?
- What becomes possible. What important work can you do with the freed-up time?
In one project, we turned a 5 to 10 hour process into something that takes under 5 minutes. The time savings paid for development in weeks. But the real value was eliminating errors and letting the team focus on growth instead of spreadsheets.
In an accounts payable project, the math included time saved plus money recovered from early-payment discounts, prevented duplicate payments, and better vendor relationships.
Not everything passes this test. I've walked away from projects where the build cost was more than the savings. But when all five steps line up, automation is obvious.
Try this: Take hours per week, multiply by your hourly cost, multiply by 52 weeks. Compare that to what it would cost to build. If you break even in 6 to 12 months, it's probably worth it.
Patterns That Show Up Again and Again
After doing this for a while, I see the same things over and over:
- Data stuck in one place. CRM data that needs to be in project management. Accounting data that needs to be in dashboards. Inventory that needs to be in marketing systems.
- Comparing data manually. Checking two systems to find differences. Usually happens when systems don't connect, so you're stuck staring and comparing.
- Scheduled tasks. Weekly reports. Monthly exports. Recurring status updates. These are easy wins because they're predictable.
- Processing documents. Invoices, forms, contracts where you pull out data and type it in somewhere else.
If you're doing repetitive work on a schedule, moving data between systems, or fixing the same errors over and over, you know what to do.
Where This Leaves You
Spotting what to automate comes first. Building it is the technical part. But you have to see it before you can fix it.
These five steps help you see the patterns. If a task checks all five boxes, it's worth exploring.
Every business has different systems and constraints. But now you know what's worth your time.