If you work in talent acquisition, you already know the ground is moving under your feet. AI is showing up in your tools, your workflows, and your client conversations…but so is something more basic and immediate: a massive spike in applicant volume.
In this conversation with Michael Yinger, a talent acquisition leader with 24 years in RPO, we talked about what’s changing in TA right now, where AI is actually helping, and why better data and better process discipline matter more than ever.
If you’ve been wondering how TA teams can stay effective without losing the human side of hiring, this is for you.
AI in Talent Acquisition Is Here — But Not in the Same Way Everywhere
The biggest change in talent acquisition right now is obvious: AI.
But what’s interesting is that AI isn’t showing up as one neat, universal solution. It’s entering TA through a mix of client tech stacks, vendor tools, and internal team adoption.
That means the real question is no longer, “Should TA use AI?” It’s “Where can AI actually help — and where does it create risk?”
Michael explained that many TA teams are already using AI for sourcing, communication, and decision support. In some environments, the client provides the technology. In others, the RPO or TA team brings its own tools and training.
That creates a practical tension: you may want to use AI in a certain way, but you still have to work inside the client’s environment and governance rules.
That’s why the conversation is shifting from hype to implementation. Teams are no longer asking whether AI exists. They’re asking how it fits into the systems they already use.
Why Some Teams Are Moving Faster Than Others
Michael noted that there’s broad support for AI in his organization, including training and clear ground rules. But across the market, client reactions vary.
Some clients have FOMO — they don’t want to fall behind. Others are more cautious, especially when AI sits inside opaque systems that affect how candidates are treated.
That concern is especially strong when applicant tracking systems update their features or when an AI-enabled process could unintentionally create adverse impact.
The result is a split screen:
- Generative AI is gaining more acceptance because it helps people write, summarize, and present information faster.
- Agentic AI and automated decision-making still face more skepticism because of compliance, fairness, and transparency concerns.
That difference matters. In TA, the most adoptable tools are often the ones that support recruiters without making decisions for them.
The Real Question: Help or Harm?
The best AI conversations in TA are about reducing friction.
If a recruiter spends too much time drafting emails, summarizing status updates, or creating reports, AI can help remove that repetitive work.
But if a tool starts making decisions that affect candidate experience or selection outcomes, the conversation changes fast.
That’s why many teams are still in the “assistive AI” phase. They’re using it to make recruiters more efficient and consistent, not to remove human judgment.
How AI Is Giving Recruiters Time Back
One of the most practical benefits of AI in TA is time.
Michael said his teams are already seeing some of the mundane work get pulled off recruiters’ plates. That may not sound dramatic, but in a high-volume environment, it matters.
When recruiters spend less time on repetitive admin, they get more time back for the work that actually changes outcomes: talking to hiring managers, speaking with candidates, and improving the quality of the process.
This is one of the clearest signs that AI is working in TA: not by cutting headcount overnight, but by improving output.
What That Time Gets Used For
The most immediate gain isn’t necessarily a shorter workday. The bigger win is better recruiter focus.
Instead of spending time on low-value tasks, teams can:
- Spend more time with hiring managers
- Improve candidate communication
- Create cleaner, more useful status updates
- Package information in a way that helps decision-making
- Focus on higher-value collaboration
Michael gave a simple example: instead of a recruiter sending a plain status email, AI can help package that update in a better format with more useful information.
That saves time and improves the product TA delivers.
And that’s an important distinction. Right now, AI often helps teams do the same work better — not necessarily do radically more work with fewer people.
That’s why the ROI conversation in TA can’t be just about labor reduction. It also has to include quality, consistency, and the recruiter experience.
Why This Is Still Early
Michael believes we’ll see even bigger efficiency gains over time, but not immediately. The reason is simple: most TA teams are still constrained by the technology they live inside.
Who owns the system? What can be modified? What’s locked down by the client? What’s approved by compliance?
Those questions shape what AI can do in practice.
The strategic conversation is about whether the surrounding tech stack lets you use it well.
That’s a useful reminder for anyone buying or implementing new TA tech: the best tool in the world still has to fit the environment you’re operating in.
The Biggest Daily Challenge in TA Is Applicant Volume
Outside of AI, Michael pointed to a more familiar problem: too many applicants.
This is one of the biggest shifts happening in talent acquisition right now. In the past, many teams were trying to generate more applicants. Now, for a growing number of roles, the problem is the opposite.
A single posting can attract hundreds or even thousands of candidates in a very short period of time.
The Volume Problem Has Changed the Job
Michael shared examples that make the scale feel real:
- A req with 100 to 150 applicants is no longer unusual.
- One entry-level developer role pulled in 1,500 applicants in two hours.
- Senior roles can rack up hundreds of applications almost immediately.
That volume creates a new kind of work. Recruiters still have to comply with posting requirements, review candidates appropriately, and keep the process moving — but now they’re doing it at a much higher speed and scale.
And the speed is part of the issue. Candidates can react faster than ever, which means recruiters are dealing with more applications, more quickly, and often with less signal.
Why AI Helps — But Doesn’t Solve Everything
Yes, AI can help sort, summarize, and prioritize applications. But it doesn’t erase the underlying problem of volume.
It can make the review process more manageable, but recruiters still need to make good judgments. And in a high-volume environment, that means process matters even more.
Michael also pointed out a classic TA reality: not every applicant is actually qualified.
When a technology project manager role attracts someone whose background is installing ice cream machines, the challenge isn’t just volume — it’s relevance.
That’s where job descriptions, screening logic, and workflow design become critical. If the front end of the process is weak, the back end becomes overwhelmed.
Better Data and Better Process Are Still the Real Fixes
If Michael had one magic wand for TA, it wouldn’t be another AI feature.
It would be better access to data.
That answer tells you a lot. In talent acquisition, data is often the missing piece behind good decisions.
Teams may know something is happening, but they can’t always see it clearly enough to explain why, prove it, or fix it.
Why Data Access Is Such a Persistent Issue
Michael said TA teams often struggle to get the data they need from the system. Sometimes that’s because the client controls what they can see. Sometimes it’s because the ATS doesn’t surface the right information in the right way.
That creates a recurring challenge:
- You need data to make decisions.
- You need data to tell the story of your work.
- You need data to prove where the process is breaking down.
- But you may not have access to the data that would answer those questions.
So teams end up spending time working around the system instead of learning from it.
Why Process Breaks When Teams Are Stretched
Michael also made a point that anyone in operations will recognize: process work is never done, and it is usually not current.
That becomes a real problem when companies cut staff or move people around without documenting how the work should actually happen.
Suddenly, fewer people are doing the same work, but nobody is fully sure what the complete process is anymore.
That’s when broken workflows, missed handoffs, and unnecessary escalations show up.
And Michael called out one especially frustrating symptom: the Friday 4 p.m. escalation.
Most TA leaders know this pattern. Something appears urgent at the end of the week, and everyone reacts as if it’s a full-blown trend.
But often it’s not a trend at all. It might be one mistake, one process issue, or one interpretation problem.
The better response is to ask:
- What actually happened?
- Is this a one-off or a pattern?
- Is the process broken, or is someone interpreting it incorrectly?
- What data do we need before reacting?
That kind of discipline saves time, energy, and credibility.
What TA Will Look Like in Three Years
When asked what talent acquisition might look like three years from now, Michael didn’t predict a future without recruiters.
Instead, he sees more automation, more AI-assisted interaction, and a more strategic role for humans.
That’s a useful counterpoint to the idea that AI will simply replace TA. Michael’s view is more nuanced: automation will keep growing, but the human part of hiring will still matter.
The Future Is Likely to Be Hybrid
Michael framed the future as a mix of three modes:
- Human interaction
- AI interaction
- AI-assisted interaction
That last one may become the most common. Recruiters won’t disappear. They’ll be supported by tools that help them move faster, stay organized, and focus on the conversations that matter.
This is not a new cycle. TA has heard versions of this before.
Years ago, job boards were supposed to eliminate the need for recruiters. But candidates still wanted to talk to someone. So recruiters adapted — and in some cases had to learn old-school communication skills again.
The pattern is familiar: technology changes the workflow, but it doesn’t eliminate the need for human connection.
The Enduring Truth: TA Is About People
Michael ended with the reminder that talent acquisition is fundamentally a people function.
That may sound obvious, but it’s easy to forget when you’re buried in workflow, tools, metrics, and escalations.
The work is important because it connects companies to the people who make everything else possible.
As Michael put it, TA may not be curing cancer — but it helps find the people who could.
That perspective matters. In a world where tools are getting faster and processes are getting more automated, it’s still human judgment, communication, and care that make talent acquisition effective.
Why I Wrote This
AI is changing talent acquisition, but this conversation with Michael reinforced something we see all the time: better technology only gets you so far if your data, job content, and processes are working against you.
That’s part of why we built Ongig. Ongig helps talent acquisition teams improve and manage job descriptions at scale, making job content clearer, more consistent, and easier to manage.
If applicant quality, job content, or the growing complexity of your TA workflow is becoming a problem, request a demo to see Ongig in action.
Frequently Asked Questions
How is AI being used in talent acquisition today?
AI is being used for sourcing, communication, job description support, reporting, and decision assistance. Many teams are starting with assistive uses that improve speed and consistency without removing human oversight.
Why are TA teams struggling with applicant volume?
Job boards, easy-apply features, and faster candidate response times have made it much easier for people to apply quickly. That can create huge applicant pools that overwhelm recruiters if the process isn’t designed to handle them.
What is one of the biggest process problems in talent acquisition?
According to Michael Yinger, access to useful data is a major problem. Without good data, it’s hard to diagnose problems, prove impact, or improve the hiring process.
Will AI replace recruiters?
The future Michael described is more likely to be hybrid. AI can handle repetitive work and support recruiters, while people remain responsible for the human conversations, judgment, and relationships that hiring requires.
How can better job descriptions help with high applicant volume?
Clearer job descriptions can help candidates better understand the role, its requirements, and whether they’re a reasonable fit before applying. That gives TA teams a stronger front end to the recruiting process.
