This is a SasaDaily hypothetical business case.
A small study abroad consulting studio with 4 staff members faces the same time-consuming issue every year before overseas university semesters start:
students and their families constantly ask about flight tickets.
“Is it too expensive to buy now?”
“Will prices drop next week?”
“This flight is cheap but does it require two transfers?”
“I have two large suitcases; is this ticket suitable?”
The problem isn’t that tickets can’t be found.
It’s that prices change every day, requiring consultants to continuously recheck.
The biggest time waste isn’t the first search
The initial flight options compiled for students are usually reasonable.
The real time drain is afterward:
Search once today.
Search again three days later.
Check again when parents ask if prices have dropped.
Search again when students change departure dates.
With 20 to 30 families preparing to go abroad simultaneously, these scattered tasks add up and consume a lot of time.
So this hypothetical studio does not ask AI to “automatically buy the cheapest ticket for clients.”
It only changes the most repetitive step:
using AI to monitor prices.
Step 1: Consultants clarify actual needs first
Consultants don’t just search for:
“Cheapest flight from Taipei to Los Angeles.”
Because the cheapest isn’t always the best fit.
Students might also have requirements like:
Latest arrival date.
Maximum number of transfers allowed.
Whether they need to check two large pieces of luggage.
If early morning arrivals are acceptable.
Whether they want to use specific airline miles.
How much buffer time they need before the school registration date.
These conditions are confirmed by consultants and students first.
AI handles the search and pricing info.
Humans decide what counts as "appropriate."
Step 2: Once reasonable flights are found, stop checking prices daily manually
Google AI Mode now allows direct flight price tracking setup.
So once consultants find a few flights meeting conditions, they don’t have to search from scratch every day.
They can create price trackers.
Only check back when prices change.
This small change is very important for small service businesses.
Because AI doesn’t replace professional judgment.
It simply removes the low-value repetitive task of "checking if prices changed every day."
Step 3: Even after a price drop alert, consultants don’t immediately tell clients to “buy now”
This is the most crucial step of the entire process.
Suppose a flight seen yesterday has dropped in price.
Consultants still need to reconfirm:
Is it the same flight?
Are the fare conditions the same?
Are checked luggage fees included?
Is the layover time reasonable?
Are cancellation and change policies different?
Is the total price still within budget?
A price drop only answers one question:
It’s cheaper now.
It doesn’t answer:
Is this still the best ticket for this particular student?
Step 4: Before actual payment, clients confirm details themselves
This studio divides work into two layers.
Tasks given to AI:
Search.
Compare.
Price tracking.
Organize candidate flights.
Alert price changes.
Tasks handled by people:
Confirm passenger names.
Confirm passport details.
Confirm luggage needs.
Confirm refund and change policies.
Confirm final price.
Confirm payment.
Consultants are not selling "click search for you."
They are reducing the risk of clients making mistakes in their choice.
How much time can this actually save?
All numbers below are hypothetical for understanding business value by SasaDaily—not official Google results or any real company data.
Assuming the studio has 30 student families each month waiting for suitable tickets.
Previously, each client:
Had prices manually rechecked 4 times.
Spent 10 minutes each time.
So:
30 clients × 4 times × 10 minutes.
About 20 hours per month spent on repetitive price checks.
After implementing price tracking, assuming 5 hours total per month to set up trackers, check important conditions after alerts, and confirm with clients.
That means:
About 15 fewer hours of repetitive work per month.
If hourly labor cost is estimated at NT$400:
15 hours × NT$400.
Equivalent to about NT$6,000 worth of work time saved per month.
Again:
This is an assumed calculation by SasaDaily for conceptual understanding.
Real results depend on customer numbers, route complexity, number of alerts, and service processes.
The real business value isn’t just about cutting staff
Many small companies immediately think:
Can AI reduce the number of employees?
But this case’s valuable insight isn’t about layoffs.
If consultants save 15 hours a month of repeated price checking, they can use that time for:
Discussing what first-time travelers need to prepare.
Checking registration dates and accommodation coordination.
Handling truly complex flight issues.
Serving more families.
Even providing pre-departure consultations they previously lacked time for.
This is where small service businesses gain value from AI:
Not by automating the most important work first, but by removing the most repetitive work first.
What’s the key takeaway from this case worth replicating?
It’s not about being a study abroad consultant.
It’s not about selling flight tickets.
It’s about identifying tasks in your work where:
Data changes constantly.
You have to repeatedly check it.
But what you really need only happens when there’s a change requiring your judgment.
If so, this is ideal for AI to watch first.
Humans stay in the second layer to make decisions.
This is one way AI gives time back to people.
Today, improve a little with AI.
Learn one AI skill every day.
Save a little time every day.
Enhance your ability bit by bit daily.
SasaDaily, growing alongside you.
