A
B
C
Question
What's the real
problem here?
?
Start anywhere.
Observation

Notice the small things. They carry big signals. ♡

The Lab

Experiments in practice.

Bri's experimental operating practice across disciplines, problem sizes, and altitudes. Not "Bri experiments with AI" — the work itself is the laboratory.

What do you do when you don't know the answer yet?
♡

Start anywhere. Notice the small things — they carry big signals.

The Range

CraftSearchOperationsExperienceStrategySystemsAI + Agentic Experiences

Seven Specimens Moving Through Altitude

01
SPX-01

Make the Moment Better

Candidate Experience · Interaction Altitude
What I Tried
Prep-call talks, structured interview prep, communication sequencing, follow-ups + candidate care, thoughtful decline ideas.
Signal Observed
Candidates felt more prepared and confident. Stronger NPS comments around human connection and communication.
What Changed
Small experience changes became standard practice.
Learning
Care and performance aren't opposites.
Where It Went Next
Stronger candidate trust and repeatable experience moves across searches.
02
SPX-02

Change the Story

Messaging · Craft Altitude
What I Tried
Messaging variants, different subject lines, campaign variations, different positioning, different tone by audience.
Signal Observed
Some messages landed with certain audiences and not others — interest shifted with the story.
What Changed
Sharper language. Better market questions.
Learning
Run the variants. Evidence gets to change the plan.
Where It Went Next
Improved response quality and better alignment with the right markets.
03
SPX-03

Question the Search

Sourcing · Search Altitude
What I Tried
Adjacent talent pools, beyond expected titles, new markets/orgs, multiple sourcing theses, pre-kickoff candidate reuse.
Signal Observed
Initial thesis was wrong. Better talent lived in different markets.
What Changed
Search direction reversed toward consumer transformation, regulated markets.
Learning
The stated problem isn't always the real problem.
Where It Went Next
Better market intelligence and a faster kill signal for future searches.
04
SPX-04

Turn the Lens Around

Calibration / Decision Quality · Team / Process Altitude
What I Tried
Changed screen questions, different calibration methods, panel composition changes, evaluation context experiments, decision safeguards.
Signal Observed
Same candidate, different evaluation, inconsistency came from the system.
What Changed
Clearer criteria. Stronger decision habits across the org.
Learning
What if the candidate isn't the variable?
Where It Went Next
More consistent outcomes across the organization.
05
SPX-05

Change the Operating Model

Risk + Strategy Altitude
What I Tried
Code Red pivots, sequencing changes, funnel shaping, capacity modeling, defining "healthy" (4-6 band).
Signal Observed
HM screening capacity was the real constraint. More sourcing wouldn't solve it.
What Changed
Interventions aligned to the actual constraint, not volume.
Learning
Risk is cheaper to design around than to rescue from.
Where It Went Next
Recovery playbook and healthier search outcomes.
06
SPX-06

See If The Learning Repeats

Systems + Intelligence · Org Altitude
What I Tried
Brand Studio → intelligence, built with workflows, Hiring Compass, externalized hiring judgment.
Signal Observed
Faster reviews. Shared bias. Better alignment. Reduced bias.
What Changed
Knowledge became reusable infrastructure.
Learning
If learning repeats, ask whether it should become reusable.
Where It Went Next
Hiring Compass — 700+ candidates, ~25-review time from ~60 min to 7–8 min.
07
SPX-07

Let One Problem Become Another Possibility

Candidate → Organization Altitude
What I Tried
Reframed equity story, partnered with finance, tested what mattered, explored reusable capability.
Signal Observed
Candidate said yes. Actual tradeoff was a compensation reframe, not a straight ask.
What Changed
One close became an offer storytelling approach that carries forward.
Learning
Do something with what you learned.
Where It Went Next
Offer Positioning Agent experimentation and org-wide opportunity.

What The Experiments Teach

The stated problem isn't always the real problem.
Every outcome is information.
Evidence gets to change the plan. Run the variants.
Risk is cheaper to design around than to rescue from.
Care and performance aren't opposites.
If learning repeats, ask whether it should become reusable.
Technology carries load, not accountability.
Expertise doesn't require already knowing.

Build. Test. Learn. Carry forward what earns it.

stay curious.