We've Made Hiring Faster. But Are We Missing the Best People?

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 As AI transforms recruiting, new research and real experiences raise questions about efficiency, human judgment, and recognizing talent.



A couple of weeks ago, I published a blog about new Stanford research examining how automated hiring systems may be overlooking qualified candidates. I called it "The Bot Loop Is Real," a phrase I also explored in my book, Redefined.

What happened next truly caught me by surprise.

The article became my most-read blog to date. But what stayed with me wasn't the number of views or shares. It was the stories people shared and how many said they finally felt validated.

One professional with 30 years of experience described how transferable skills can be overlooked when a career doesn't follow a traditional path. Another shared how an application was rejected almost immediately, only to discover that no human had reviewed the resume. When someone finally did, the reaction was positive.

Then there was a parent watching her 18-year-old struggle to land an entry-level job, getting automated responses from places where teenagers once had a relatively straightforward path to start working.

Different experiences, but a common frustration: People feel invisible in a system designed to connect talent with opportunity.

Their stories made me think about something we don't discuss enough. We spend so much time talking about what job seekers need to do differently. But what about employers? Are we asking enough questions about how hiring systems evaluate the people organizations want to attract?

When Efficiency Doesn't Tell the Whole Story

Recruiting has become increasingly complex.

Employers can receive hundreds, sometimes thousands, of applications for a single position. Recruiters are managing heavy workloads, hiring managers need positions filled, and organizations face pressure to control costs.

Technology can help. AI can streamline administrative tasks, identify potential candidates, and give recruiters more time to focus on work that requires their expertise.

Those are real benefits. But efficiency and effectiveness aren't necessarily the same thing.

A system can process applications quickly while still overlooking people who could make valuable contributions.

In their May 2026 study, Algorithmic Monocultures in Hiring, Stanford researchers analyzed about four million applications and found that hiring assessments could look acceptable in aggregate, yet reveal troubling disparities at the individual-job level.

The Talent That Doesn't Fit Neatly Into a Box

Consider the professional who commented on my previous article. With approximately 30 years of experience across different business sectors, she described how easily transferable skills can be missed when recruiters or automated systems don't recognize the connections between industries.

She makes an excellent point.

Leadership, judgment, adaptability, and the ability to navigate uncertainty aren't always easily captured by keywords or standardized assessments. These capabilities develop through experience, sometimes across industries and unconventional career paths.

And ironically, they're precisely the qualities organizations say they value.

Companies routinely talk about innovation, agility, and bringing fresh perspectives into their organizations. But are their hiring processes designed to recognize those qualities? Or are they unintentionally favoring candidates whose experience looks most familiar?

This doesn't mean technology cannot identify transferable skills. It means organizations should understand what their tools measure and where human evaluation can add value. A candidate who doesn't look like the obvious choice on paper may be exactly the person who brings a fresh perspective.

Better Tools, Better Hiring Decisions

The talent acquisition profession is already discussing how recruiting can evolve.

In a September 2026 SHRM article, How AI Will Transform the Talent Acquisition Function, industry leaders explored how automation could give recruiters more time to understand candidates' career goals, evaluate their capabilities, and advise hiring managers.

I like that perspective. Rather than using AI simply to process more applications, organizations have an opportunity to give recruiters more time to understand the people behind the resumes and recognize skills that may not be immediately obvious.

But that raises another question: How do we know whether these tools are helping us make better hiring decisions?

Organizations have traditionally measured recruiting performance through metrics such as time-to-fill and cost-per-hire. Those measures matter, but they don't necessarily tell us whether an organization is finding the talent it needs.

SHRM also discusses time to capability, which considers how quickly organizations obtain necessary skills through hiring, developing existing employees, or other workforce strategies.

It's an interesting shift. Rather than focusing only on how quickly positions are filled, organizations can examine whether they're identifying strong candidates, recognizing transferable skills, and understanding why people are screened out.

That also means giving recruiters the authority to question automated recommendations when something doesn't seem right.

Ultimately, these are business questions, not just recruiting questions. And they deserve leadership's attention.

Your Hiring Process Is Also Your Reputation

There's another part of this conversation I want to address: organizational reputation.

Throughout my communications career, I've helped numerous organizations build trust, strengthen relationships, and communicate their values.

And one thing I've learned is that reputation isn't shaped solely by what an organization says about itself. It's shaped by how people experience it. Every interaction is an opportunity to build or erode trust, and the hiring process is no exception.

Consider the candidate who spends hours researching an organization, tailoring a resume, and completing an application, only to receive an automated rejection within minutes.

Or the experienced professional who advances through several rounds of interviews and never hears back.

Not every rejection reflects a flawed process, and employers cannot reasonably provide personal feedback to every applicant.

But those interactions leave an impression.

A candidate may never become an employee, but that doesn't mean their experience won't influence how they see the organization or what they tell others about it. People talk. They share their experiences with friends, colleagues, and professional networks. Sometimes they share them publicly.

And for companies investing heavily in attracting talent and building their employer brand, remember that first impressions begin long before someone's first day on the job.

The Opportunity to Get It Right

None of this means organizations should abandon AI or return to entirely manual hiring processes.

Technology offers tremendous opportunities to improve recruiting. But as these tools become more sophisticated, oversight should keep pace.

In a Stanford interview about the research, researcher Sarah Bana suggested that employers regularly examine whom their algorithms screen in and out, including allowing a sample of applicants to advance beyond initial screening to assess how they perform. It's a practical reminder that organizations need to evaluate their systems' results rather than assume they're working as intended. That means reviewing outcomes, understanding limitations, and ensuring recruiters have opportunities to exercise professional judgment.

The people who shared their stories after my last article weren't asking employers to abandon technology. They wanted their experience, capabilities, and potential to be recognized.

And isn't that what hiring should be about? Finding people who can bring something valuable to an organization, even when their experience or career path doesn't fit neatly into a box.

As we continue to embrace AI in hiring, let's not lose sight of the people behind the applications.


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