Executive search is broken
I once watched a search firm take 6 months for a position where the right candidate had been identified in week 3.
The role was a leadership position, the kind for which enterprises typically run a retained search.
Candidate sourcing went something like this:
- Hours spent manually mapping companies
- Identifying potential candidates
- Building long lists in spreadsheets.
On the client side, the process was also as you'd expect:
- Email threads going back and forth
- Multiple versions of the same job description
- Spreadsheets with talent maps continuously updated and re-sent
- Candidate notes living in different unstructured documents
- Critical feedback scattered across inboxes
It was a chaotic mess … despite involving only 6 candidates.
Every update between client and search firm triggered another round of manual search and email ping pong.
"Did you consider this competitor?"
"Have we exhausted this geography?"
"Can you resend the latest list?"
"Which version is the current one?"
"Did we speak to this candidate already?"
"What's the candidate's availability?"
"Did we already do the psychometric assessment?"
Information everywhere.
Some in email. Some in documents. Some in handwritten call notes.
On paper, the search was progressing. Candidates were contacted. Work appeared to be getting done. Conversations happened. Interviews were scheduled.
But looking back, one thing stood out: An industry that emerged during the post-World War II period to address skilled labor shortages; operating largely unchanged decades later. The process was manual. Painfully so.
What made this realization particularly uncomfortable is that I wasn't observing from the outside. I owned and ran the search firm managing the process.
If you're a TA / HR / Line leader, you already know this story from the other side. You've lived it too - maybe even this week. And, you deserve better from your search partners.
So I started questioning every assumption about how search should work in the AI era - the process, the model, the data, the pricing. That questioning turned into an obsession to help clients find critical talent for hard-to-fill roles without the pain. It eventually became TalSource.
Funny how the problems that frustrate you the most… end up becoming the things you build.