Recruiters have more technology than ever, yet candidates are still wondering if anyone has even seen their applications. If AI is supposed to make hiring easier, why does the process still feel so hard for the people going through it?
That question came up when I sat down with Aaron Tittsworth, also known as the Superhero Recruiter, for an episode of TA Talk, part of The JD Fix.
Aaron has spent about 16 years in recruiting, from employer branding and recruiting strategy to working with hiring managers and setting up applicant tracking systems (ATS).
We talked about AI in talent acquisition, job descriptions, the candidate experience, and what recruiters should focus on next.
I left the conversation thinking about something pretty simple: The tools can move faster, but people still need to know what’s going on.
Why the Superhero Recruiter?
Aaron grew up loving comic books. Over time, he saw a link between superheroes and recruiters.
Both meet people at important moments and help guide them through a journey.
Recruiters don’t have superpowers, of course. But they can help someone get from a stressful job search to an exciting next step.
I loved that comparison.
Recruiting can look like a long list of tasks from the outside: review resumes, book interviews, update managers, send offers.
For a candidate, though, it can be a life-changing moment. They might be trying to support their family, find a healthier workplace, or take the next step in their career.
That human side is easy to forget when your inbox is full and another tool promises to cut your workload in half.
AI in Talent Acquisition Is Helping. But What Happens to the Human Side?
Aaron remembers recruiting when applications were on paper.
Today, recruiters have software to help with scheduling, outreach, meeting notes, and moving information to hiring managers.
Those are real wins.
I told him that AI meeting notes have changed my life. I still use a paper notebook sometimes, but being able to revisit a transcript and find what someone said is incredibly useful.
Aaron agrees that automation can free recruiters to spend more time on work that matters.
But he also offered a warning:
“You still have to protect the relationship.”
That’s the part I keep coming back to.
A scheduling tool can find an open time. It can’t make a nervous candidate feel heard.
A summary can capture what happened in an interview. It can’t replace a thoughtful conversation about why a person wants to make a change.
So how should teams use AI without losing that connection?
I’d start with three questions:
- Which tasks take time but don’t need a personal conversation?
- Where are candidates waiting without knowing what comes next?
- What could recruiters do with the hours they get back?
Use AI to help with the first two, then give recruiters room to do the third.
Faster admin work only helps if the saved time turns into a better hiring experience.
Stop Adding Tools to a Broken Recruiting Process
One thing Aaron said really stood out to me.
He described teams adding new software in layers instead of looking at how the whole process works.
It’s familiar, isn’t it?
A tool solves one problem, another tool fills a gap, and soon nobody is sure which system owns what.
In the episode, I mentioned a recent conversation with Julia Levy about going back to the basics.
What does the team actually need? Which steps help? Which ones only exist because we’ve always done them that way?
Before rolling out another AI feature, I’d map the candidate journey from start to finish.
Follow one job from the hiring manager’s request through the job description, application, screening, interview, offer, and final update.
Look for places where information gets lost or people have to repeat work.
Then ask the people using the process what frustrates them.
Recruiters and hiring managers will often point to different issues. Candidates may see problems neither group has noticed.
AI can make a good process smoother. It can also help a bad process run faster.
That’s why the process needs attention first.
Recruiters Need to Be Advisors, Not Just Order-Takers
Aaron thinks recruiting teams have a chance to become more strategic as AI takes on routine work.
He also knows that change is hard.
Many recruiting steps have been around for decades, and hiring managers may still expect recruiters to take a request and fill it as quickly as possible.
But a good recruiter can do much more than post a role.
They can help a manager figure out what the job really needs.
We’ve all heard about the hiring manager looking for a unicorn.
They want ten years of experience with a tool that’s only been around for five. They want one person to handle three different jobs.
And they want that person yesterday.
A strategic recruiter can ask better questions:
- Which skills are truly required on day one?
- What can someone learn after joining?
- What would success look like six months from now?
- Which requirements might be keeping good candidates away?
These questions help shape the search before the team spends weeks chasing someone who may not exist.
They also make the job description much more useful.
Better Job Descriptions Start With Better Conversations
Aaron has seen job descriptions that made him wonder whether the company should post them at all.
He talked about going back to hiring managers, learning the real duties of the role, and rewriting the content so it made sense.
He also sees a shift from long lists of qualifications toward skills and the value someone can bring.
That’s an important change.
Candidates need to understand what they’ll do and why their work matters.
At Ongig, we talk with teams about moving the information candidates care about higher in job postings.
Think about the role’s purpose, the work someone will do each day, and what they can expect from the company.
Don’t make people dig through ten paragraphs to find the good stuff.
Here are a few ways I’d improve a job description before publishing it:
- Start with a clear explanation of the job and its impact.
- Separate must-have skills from things someone can learn.
- Use everyday words instead of internal terms and vague phrases.
- Explain what the person will actually spend time doing.
- Have a human review AI-generated text for errors and confusing claims.
Aaron made another point I found interesting: job descriptions are often a candidate’s first real look at a company.
That means the writing, structure, and tone all matter.
If the posting feels careless, candidates may wonder what the rest of the experience will be like.
Should Job Postings Disclose AI Use?
One of the more unexpected parts of our conversation was Aaron’s suggestion that employers be more open about AI.
He said he rarely sees AI disclosures in job descriptions, even as companies use more AI tools in hiring.
His concern was about transparency.
Candidates should have a better sense of how technology is being used in the process.
He also raised legal questions, which are worth reviewing with the right experts rather than assuming one disclosure covers every rule.
I liked the idea because it opens a useful conversation.
If AI helps screen applications, what should candidates know? If a tool helps write the posting, does that need the same kind of notice?
The answer may depend on the tool, the process, and the laws that apply.
We also talked about another trend I’ve noticed: some companies are telling candidates which AI tools employees will have access to once hired.
For certain roles, that information could be a real selling point.
It helps people picture how they’ll do the work.
At a minimum, I’d encourage teams to review where AI is used, what candidates are told, and whether those messages are clear and accurate.
The Candidate Experience Has a Missing Middle
This might have been my favorite part of the episode.
Aaron has recently experienced the job search from the candidate side, and it made him look at recruiting systems differently.
He described a pattern many job seekers know well.
You apply.
You get an automatic message saying the company received your application.
Then you hear nothing until a rejection arrives, sometimes much later.
Aaron asked a great question:
“Where’s the in-between?”
He has helped set up ATS systems, so he knows teams can build automated follow-ups.
Recruiters may not have time to personally update every applicant, especially when applications pile up.
But that doesn’t mean candidates should be left guessing for weeks or months.
We talked about how sales teams often plan several touchpoints for a lead.
Why wouldn’t we think about candidate communication with the same care?
A simple update could say that applications are still being reviewed, that interviews are underway, or that the timeline has changed.
It doesn’t need to promise an outcome.
It just needs to tell the truth.
That matters even for candidates who don’t get the job.
Aaron pointed out that people who feel respected may still refer others to the company.
A thoughtful candidate experience can have value long after one opening closes.
Don’t Assume Your ATS Filters Are Working Perfectly
Aaron also wondered whether some fast rejections might come from how ATS filters and AI features are set up.
He was careful to frame this as a concern based on what he’s seen and heard, rather than proof that every rejection is caused by automation.
It’s a fair reason to check your setup.
An ATS has a lot of settings, and those settings can affect how applications move through the system.
If a team adds AI tools without reviewing the workflow, it may miss problems.
I’d ask recruiters and system owners to test the application process like a candidate.
Submit a test application where appropriate.
Review the messages.
Check how screening rules are applied.
Make sure someone knows who is responsible for checking the system after changes.
The goal isn’t to blame the software.
It’s to make sure the process works the way the team thinks it works.
What Should Recruiters Measure Next? Aaron’s Case for TVR
Near the end of the conversation, Aaron brought up a metric he thinks more talent acquisition leaders should explore: Talent Value Realization, or TVR.
Most teams know metrics like time to fill, cost per hire, and offer acceptance rate.
Those numbers can be useful, but Aaron wants leaders to look further.
What happened after the person was hired?
How quickly did they become productive?
What did the hire contribute to the business?
He described TVR as a way to think about the cost of a vacancy, hiring speed, time to productivity, quality, and business contribution.
It’s a broader view of what hiring achieves.
I wouldn’t throw away existing metrics overnight.
Teams still need to know how long hiring takes and where money goes.
But I do think Aaron is asking the right question: Are we measuring how quickly we fill a seat, or how well that hire helps the organization?
That could also change the conversations recruiters have with leaders.
Instead of reporting only that a role closed in 35 days, they could talk about whether the person was set up to succeed and what the team gained by hiring them.
If We Had a Magic Wand, We’d Fix Communication
I ask every TA Talk guest what they’d fix in talent acquisition if they had a magic wand.
For someone called the Superhero Recruiter, it felt like an especially fitting question.
Aaron’s answer wasn’t another tool or a faster way to screen resumes.
He wanted better personal communication and transparency.
That says a lot.
AI can help teams do more with limited time. It can make notes easier, speed up scheduling, and support better job content.
But candidates still want clear updates, thoughtful conversations, and a sense that someone is paying attention.
Maybe that’s the real superhero move in recruiting: use the technology, but show up for the people.
Why I Wrote This
I host TA Talk because I want to hear what recruiting looks like for the people doing the work.
Aaron’s stories reminded me how closely job descriptions, hiring systems, and candidate communication are tied together.
When one part breaks, candidates feel it.
At Ongig, we help teams improve and manage job descriptions so they can create clearer, more consistent job content.
That won’t solve every candidate experience problem, but it gives teams a better starting point.
If your job descriptions are slowing people down or sending the wrong message, request a demo to see Ongig in action.
FAQs About AI in Talent Acquisition
How is AI helping recruiters today?
AI can help with scheduling, interview notes, outreach, and drafting job content. These tasks can take up a lot of a recruiter’s day. The best use is to save time while keeping humans involved in decisions and conversations that affect candidates.
Can AI replace the human side of recruiting?
AI can support recruiting work, but Aaron’s point was that relationships still matter. Candidates need someone who can listen, explain the process, answer questions, and help them make a big career decision. Automation should support that work, not push it aside.
How can employers improve candidate communication?
Start by checking what happens after someone applies. Make sure acknowledgment emails work, then add honest updates when the process takes longer than expected. Review the messages for clarity and make it easy for candidates to know what comes next.
What makes a job description more useful to candidates?
A useful job description explains the role, the work, the skills that matter, and what the candidate can expect. Put important details near the top. Avoid vague language and unrealistic wish lists. Have a person review AI-written content before it goes live.
What is Talent Value Realization in recruiting?
Talent Value Realization, or TVR, is a way of looking beyond hiring speed to the value a hire brings to the business. Aaron discussed factors such as vacancy cost, time to productivity, hire quality, and business contribution. Teams can explore these alongside their existing recruiting metrics.
