Prioritizing Tasks for Automation
Not every task is worth automating. The ones that drain your team's energy and repeat themselves daily are your starting point. Here's how to find them and rank them.
Why Prioritization Matters
Trying to automate everything at once is a fast route to failure. Teams that scatter their energy across dozens of AI projects end up with nothing production-ready and a lot of skepticism about whether AI works at all.
The teams that win start with a short, ranked list. They pick one painful, repetitive task, solve it completely, and show the result. That single win creates trust, momentum, and a blueprint for everything that follows.
Identify the Right Tasks
The best automation candidates share three traits: they are repetitive, rules-based, and time-consuming. Use these questions to surface them fast.
Ask your team
What's the task your team dreads most? What would they automate tomorrow if they could? Frustration is your best discovery signal.
Look for copy-paste patterns
If someone is copying data from one spreadsheet to another, or reformatting the same report every week, that is a strong automation candidate.
Time how long it takes
Tasks that take more than 30 minutes per week per person are worth investigating. An hour a day across a five-person team is 25 hours of recoverable time every week.
Check for clear rules
Can you write down exactly how to do this task in a step-by-step list? If yes, a machine can probably do it. If the answer depends entirely on judgment, save it for later.
Start with Quick Wins
Prioritize tasks where you can show a result within two weeks. Quick wins do more than save time — they build the trust your team needs to take bigger steps.
Summarizing meeting notes
Paste your raw notes into an AI tool with a simple prompt. Get a clean summary with action items in under a minute. This one works every time.
Drafting follow-up emails
Give the AI context about the conversation and ask it to draft a follow-up. Edit, send. Saves 10–15 minutes per email, every single time.
Generating weekly status reports
Feed the AI a list of completed tasks and targets. It formats a professional report your manager will actually read.
Answering repetitive internal questions
If your team answers the same five questions over and over, you have a chatbot use case. Set it up once and stop answering the same question twice.
Protect What Already Works
Not everything needs to be automated. Some processes work because a human is making a judgment call. Automating those incorrectly can do real damage to customer relationships, data quality, or team morale.
Before you automate anything, document how it currently works. Understand why it works. Then, and only then, decide whether automation improves it or just introduces new ways for it to break.
- Keep humans in the loop for decisions that affect the customer experience
- Audit AI outputs regularly — especially in the first 30 days
- Build in a manual override for every automated workflow
- Document what you automated and why, so you can roll back if needed
Track Your Progress
You can't improve what you don't measure. Set a simple baseline before you automate anything, then measure again after.
Record how long the task currently takes, who does it, and how often it's done each week.
Run the automation for two weeks. Log any errors, edge cases, or places where it needs a human to step in.
Compare time saved, error rate, and team satisfaction. Use this data to justify the next automation project.