How I’d Use AI Auto-Match
The power of combining cascading automations with quality data
How I’d Use AI Auto-Match
AI Auto-Match is powerful.
But like any tool, how you use it determines how much value you get.
Too often, teams flip a switch and let AI match from the entire candidate pool at once.
It’s fast … but typically doesn’t produce the results you’re looking for.
Here’s a more strategic, layered approach that uses cascading automations to prioritize the best candidates first, while still moving quickly.
Start with a Filtered Candidate List
The first step is to define the eligible pool.
Instead of matching from your entire database, create a filtered list of candidates based on prior outcomes or performance signals.
This lets you rank and match with precision.
The Cascading Automation Model
Think of this like a waterfall.
Each step widens the net but only after the previous pools have had a chance to deliver.
Here’s how I’d structure it:
Automation 1: Match from Top-Performing Placements
Start with candidates who have been placed before and had positive outcomes.
You’ve seen them succeed.
They’re already vetted.
This is your highest-confidence pool.
Automation 2: Match from Strong Submission History
If Automation 1 doesn’t generate enough quality submissions, we go to Automation 2.
This pulls from candidates who have previously been submitted and received positive client feedback.
Still a strong signal. Still worth prioritizing.
Automation 3: Match Based on High AI Match Scores (85%+)
If you still need more candidates, widen the pool again.
This time, target candidates who score 85%+ in AI match relevance but may not have a submission or placement history yet.
Good fit. Less context. Still valuable.
Automation 4 and Beyond: Keep expanding until you have what you need
If you still need more candidates, keep widening the pool.
Do this until you have all the matches you need.
Rule-Based Triggers to Control Flow
The key is what controls when the next automation kicks in.
Each automation runs only after the job has completed the previous automation AND only if the number of total submissions is below your threshold.
For example:
If Automation 1 yields 20 great matches, maybe that’s enough
If not, then and only then, Automation 2 triggers
And if you still need more, then Automation 3 runs
You can set limits like:
Stop once 25 total matches are found
Or keep going until 50 or 75
Or pause all matching after 72 hours
Ways to Extend and Optimize This Model
Here are some ways to go even further:
Prioritize Recent Engagement
Add an automation layer that prioritizes candidates who:
Have updated their profile recently
Engaged with an email
Clicked on a job
Responded to outreach
Or maybe you added that field every company should have …. the Date of Last Meaningful engagement 😉
You can even build in candidate messaging like:
“View or click on a job within 7 days to be prioritized for new roles”
This drives marketing engagement while reinforcing a positive feedback loop for candidates.
Use 'Date Available' Dates to Time Matches
Avoid matching candidates who aren’t available for another 60 days.
Filter by “Date Available” dates to keep matches timely and relevant.
This can also reduce recruiter churn on unresponsive or unavailable candidates.
Track KPIs Across Each Layer
By tracking performance at each automation layer, you’ll start to understand:
Which pools convert to placements faster
Where your top matches really come from
How many total candidates you need per role by job type or client
Where your best placements really come from
That data feeds your optimization loop and helps you tune each layer over time.
And in time get even better matches!
Final Thought
AI Auto-Match isn’t just about speed.
It’s about control and quality.
Cascading automations let you scale smart
They protect recruiter time
They improve candidate experience
And they ensure that every job gets the best possible match
Most importantly done this way it unlocks the hidden super power of Staffing & Recruiting, the gold mine of outcome data you possess.
Happy Automating and AI-ing!
- Billy