AI and the Future of Talent Sourcing

The sourcing process in executive search has changed more in the last three years than in the previous ten.

When I started at Majhi Group, building a candidate list for a VP search involved significant manual research: LinkedIn, proprietary databases, industry contacts, referrals from existing relationships. A good list of thirty to forty qualified candidates for a senior role took a small research team the better part of a week to compile.

Today, with the right AI tooling, the same list is available in hours. The efficiency gain is real and it has changed the economics of the search process in ways that haven't fully settled yet.

But there's a confusion I see in the market — both among clients hiring for TA roles and among recruiters themselves — about what this efficiency gain actually changes. The assumption is often: AI is making sourcing faster, therefore AI is making recruiting better. The efficiency gain is real. The conclusion does not follow.

What sourcing actually was

The sourcing problem in executive search was never primarily a finding problem. It was a reaching problem.

At senior levels, the people you want to talk to are not actively looking. They are not on job boards, they are not scrolling LinkedIn with notifications turned on, and they are not responding to generic outreach. They are occupied with consequential work. They have seen hundreds of recruiter messages. They have developed an efficient filtering mechanism that routes most of those messages to unread.

The finding problem — identifying who has the right background, at the right company, with the right tenure — was already close to solved before AI came along. Databases like LinkedIn were comprehensive enough that a diligent researcher could find the right candidates. The problem was never that they didn't exist in the database. It was that getting them to engage required something the database couldn't provide: a reason to respond.

AI has made finding faster. It has not solved the reaching problem.

The outreach quality problem

The irony of AI in sourcing is that the same technology that makes candidate identification easier has also been used to flood candidates with worse outreach.

The average senior executive now receives more recruiter messages than ever before. More messages means more noise to filter. More noise means a higher quality bar for what gets through. And a higher quality bar means that the generic, templated AI-generated outreach that is produced at scale is getting ignored at higher rates than the specific, researched human-written outreach it replaced.

I have written about outreach craft in depth elsewhere. The core principle — that effective outreach is specific to the individual, demonstrates genuine research, and leads with the recipient's situation rather than the sender's pitch — is more important now than it was before AI sourcing became common. Because now everyone has a list of the right candidates. The differentiator is whether your message is worth responding to.

This is a genuine opportunity for search professionals who invest in outreach quality. The market has moved toward volume. Volume has degraded the average quality of the message candidates receive. Which means a well-crafted, specific, genuinely thoughtful message stands out more, not less, than it did five years ago.

Where AI genuinely improves the search

I don't want to be read as arguing against AI in search. I use it and I believe in it for the right applications.

First-pass screening at volume is genuinely better with AI than without it. When a mandate produces 200 candidate profiles to review, AI-assisted screening that filters for the key criteria before human review allows the human attention to be focused on the candidates most likely to be relevant. That's a good use of the technology.

Scheduling and logistics are better automated. The coordination involved in arranging interviews across multiple candidates, hiring managers, and panel members is pure process, and process is better done by a system than a person.

Communication consistency can be improved with AI tooling — making sure candidates are updated on their status, that the client has the right documentation at the right time, that nothing falls through the cracks in the administrative layer of a search. These are areas where human error and inconsistency are the main risk, and where good tooling reduces that risk.

Within Majhi OS, we've built exactly this kind of infrastructure: observability into what's happening inside a search in real time, alerts when something is degrading (response times, pipeline velocity, candidate engagement), and automated workflows for the parts of the process that are genuinely repeatable. The system handles the process. The humans handle the judgment.

What the future actually looks like

The sourcing landscape will continue to evolve in ways that are hard to predict precisely. What I believe with some confidence:

The value of broad database access will commoditize further. If AI can identify the same candidates for any search firm, then having access to the database is a table stake, not a differentiator. Differentiation will come from what you do with the candidates once you've found them.

Outreach quality will matter more, not less, as volume increases. The firms and professionals who invest in the craft of human-level communication will outperform the ones who automate communication at the cost of quality.

The judgment layer — assessment, fit, candidate management through a search — will become the primary locus of value creation in search, because it is the part AI cannot replicate at the level that determines outcomes.

I built Majhi OS to own the process and observability layer so that Majhi Group could focus its human attention on the judgment layer. That's the model I believe in. Technology makes the process better. Humans make the call.

Manas Majhi is the founder of Majhi Group, a retained executive search firm, and Majhi OS, a hiring operations platform. He thinks carefully about what technology should and shouldn't own in the search process.