Practical AI

AI For Recruiters: What Works, And What Backfires.

Recruiting is the most heavily marketed AI category and one of the easiest to get wrong. Where it genuinely helps, where it damages your brand, and the line worth never crossing.

Practical AI9 min readBy Chris Sund

Every talent team in the country is being sold something right now. Source faster, screen automatically, personalize at scale, rank your pipeline. Some of it is genuinely useful. A meaningful share of it solves a problem recruiting never actually had, while quietly making the problem it does have considerably worse.

The bottleneck was never volume

Recruiting has never been limited by how many messages a team could send. Anybody with a list and a template could always send more.

What limits recruiting is that most of the people you want are not looking, hear from six recruiters a month, and have learned to ignore all of it. Sending three times as many messages that read like the ones they already ignore does not improve the outcome. It accelerates the reason they stopped reading.

So the useful question is not how to do more outreach. It is how to be the one message somebody actually answers, and then how to respond fast enough when they do.

Recruiting was never limited by how many messages you could send. It was limited by how many people wanted to hear from you.

Six uses that genuinely help

  • Job descriptions that sound like a real place. The single biggest win available. Most postings are interchangeable requirement lists. Feed in what the team is actually like, what the work involves, what you pay, and who has thrived there, and you get something specific. Candidates evaluate postings in seconds and specificity is what stops the scroll.
  • A first draft of outreach you then make real. Useful as a starting structure. It is not useful as the final message, and the distinction is the entire subject of the next section.
  • Summarizing your own notes. Interview notes into a clean summary for a hiring manager. Six conversations condensed into a comparison. Low risk, because you can check it against what you wrote in seconds.
  • Interview question preparation. Better questions for a specific role, and follow-ups that get past rehearsed answers. Preparation quality is the most underrated variable in hiring and the easiest to improve.
  • Market and company research before a call. Ten minutes of context on an industry, a company, or a role before a conversation. It makes you sound like somebody worth talking to, which is most of the battle in a first call.
  • The administrative layer. Scheduling messages, status updates, rejection notes that are actually humane rather than a template from 2011. Nobody enjoys this work and all of it has to happen.

Where it backfires

Personalization that is not personal

Generated messages that reference somebody’s job title and company and call it customized are recognizable now. Candidates compare notes, they receive dozens of these, and the pattern is obvious. A message that is transparently automated while pretending to be personal is worse than an honest short one, because it tells the recipient exactly how much thought they warrant.

The honest version works better anyway: say plainly why you are reaching out, what the role is, what it pays, and why you thought of them specifically. One real sentence about why them beats three paragraphs of manufactured warmth.

Automated scoring and ranking of people

This is where the genuine risk sits, and it is worth being direct. Tools that rank, score, or filter candidates are making employment decisions, and that carries legal exposure that varies by jurisdiction and has been changing quickly. Requirements around notice, auditing, and human review are not uniform, and this is a conversation for your employment counsel rather than a vendor’s compliance page.

Set that aside and there is still a practical problem: these systems learn from patterns in prior hiring, and prior hiring reflects prior preferences. Whatever your organization has historically favored is what gets reinforced, faster and less visibly than a person would do it.

Removing the human from the part that closes

Candidates do not accept offers because of a workflow. They accept because somebody they trust told them the truth about the job, including the hard parts, and they believed it. That is not automatable, and teams that automate everything around it often find they have optimized the parts that never decided anything.

Anything that decides about a person needs a person deciding. Use it to prepare the decision, brief the decision, and document the decision. Never to make it.

The metric that actually moves

If you fix one thing this quarter, make it response time.

Candidates who reach out or apply are comparing you to every other employer they contacted that week, and the first substantive human response wins a disproportionate share of attention. Most organizations lose good people not to a better offer but to a faster one.

One

Measure it honestly

Not time to first automated acknowledgment. Time until a human said something specific about them. Most teams have never measured this and are surprised by the number.

Two

Use the tools on the queue, not the outreach

Drafting responses, summarizing applications, and preparing notes so a recruiter can reply to twelve people in the time it used to take for four. This is where the technology earns its cost in recruiting.

Three

Give someone the number

Response time improves when a specific person owns it and reports on it. It does not improve because a tool was purchased.

Three rules for a talent team

  • Candidate data stays out of general tools. Resumes, applications, notes, and anything from your ATS. Use whatever your organization has approved for confidential information, and if nothing is approved yet, that is the project before any of this.
  • Every message goes out having been read by the person sending it. If your name is on it, you own it. The moment that stops being true is the moment your employer brand starts eroding without anybody noticing.
  • Disclose where it matters. Requirements differ by location and are still moving. Ask counsel, decide a policy, and write it down rather than deciding case by case.

What this frees you up to do

The recruiters who do best with these tools use them to protect the part of the job that was always the actual work: knowing people, understanding what they want, and telling them the truth about a role.

The hours saved on drafting should go into more conversations, better preparation, and faster responses. If they go into more outreach volume instead, you have used a new tool to do more of the thing that stopped working.

Related reading: what actually keeps people, since the cheapest hire you will ever make is the person you did not lose.

Who this is for

Different pipelines, same fundamentals.

Talent Acquisition Teams

Corporate recruiting functions carrying requisition loads that keep growing.

Healthcare Recruiting

Clinical hiring in the tightest market in the country. His home industry.

Driver Recruiting

Where speed of response decides who gets the driver, more than any other factor.

Manufacturing & Industrial

High-volume hourly hiring where the process itself is the candidate experience.

Trades Recruiting

Where the career story is strong and almost nobody writes it down well.

Higher Education HR

Faculty and staff searches inside a committee process that is slow by design.

Public Sector HR

Hiring against fixed pay bands and a formal process, where speed is the lever.

HR Associations

Chapter meetings and conferences where this is the topic members ask for.

The Full Session

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Questions

Questions talent teams ask.

Does AI actually help recruiting, or just increase volume?
It helps where recruiting is genuinely constrained, which is not volume. Recruiting has never been limited by how many messages a team could send. It is limited by how many people want to hear from you, and sending three times as many messages that read like the ones candidates already ignore makes that worse rather than better.
What are the best uses for a talent team?
Job descriptions that sound like a real place, a first draft of outreach you then make real, summarizing your own interview notes, preparing better interview questions, market research before a call, and the administrative layer of scheduling and status updates.
Can candidates tell when outreach is automated?
Yes, and they compare notes. A message that references a job title and company and calls itself personalized is recognizable now. A transparently automated message pretending to be personal is worse than an honest short one, because it tells the recipient exactly how much thought they warrant.
Is it safe to use AI to rank or score candidates?
This carries legal exposure that varies by jurisdiction and has been changing quickly, and requirements around notice, auditing, and human review are not uniform. That is a conversation for your employment counsel rather than a vendor’s compliance page. Separately, these systems learn from prior hiring patterns, which reinforces whatever your organization has historically favored.
What metric should a talent team actually fix?
Response time, measured honestly as time until a human said something specific about that candidate, not time to an automated acknowledgment. Most organizations lose good people to a faster employer rather than a better offer.
What rules should we set for candidate data?
Resumes, applications, notes, and ATS records stay out of general tools. Use whatever your organization has approved for confidential information, and if nothing is approved yet, that is the project before any of this.
Anything that decides about a person needs a person deciding.
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Chris Sund

Chris Sund

President and COO of Uniti Med, GQR Healthcare and Nebula, an AI-powered talent sourcing platform, and Vice President of the NATHO Board of Directors. He has spent a decade in healthcare staffing and speaks on it nationally. Nothing here is legal advice, and employment law questions belong with your counsel.

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