Why AI is the Future of HR


Meet Amy. Amy is the future of human resources. I met Amy because she works for Abby.

I met Abby through HR UndergroundX. Abby told me about a new technology she was using to recruit candidates for her startup clients. To find out more, I sent Abby this email:

“Abby – It was nice meeting you the other night. I’d love to get together and have coffee. -Greg”

Abby responded:

“…Amy CC’d has my schedule and will reach out to coordinate with you. She’ll also send you a calendar invite once confirmed. Amy, please block this as a 30-minute coffee meeting. If Crema is not a good location for Greg, please let him select our meeting spot. Thanks! – Abby”

Twenty-one minutes later, I received an email from Amy Ingram.

“Hi Greg,

Happy to get something on Abby’s calendar.

Does Monday, Jul 18 at 4:00 PM work? Alternatively, Abby is available Tuesday, Jul 19 at 11:00 AM or 4:00 PM.

Abby likes Crema Coffee House, 2862 Larimer St, Denver, CO 80205, USA, for coffee.

Amy Ingram | Personal Assistant to Abby”

I replied

“Amy – Unfortunately, those times don’t work for me. I am open at 11AM on July 22nd. Let me know if that would work. –Greg”

Twenty-eight minutes later, Amy responded.

“Hi Greg,

Abby is available Friday, Jul 22 at 11:00 AM.

I’ll send out an invite.


Not long thereafter, I later received the meeting invite

The day before meeting with Abby, I sent an email to Amy to confirm the appointment

“Amy – Just want to confirm that Abby is still available for our appointment tomorrow at 11AM. –Greg”

Amy replied:

“Hi Greg,

This meeting is scheduled for Friday, Jul 22 at 11:00 AM and will be at Crema Coffee House, 2862 Larimer St, Denver, CO 80205, USA. I haven’t heard anything otherwise from Abby.


From this exchange, you might conclude that Amy is the model Personal Assistant. She provides timely, accurate responses, blended with a human touch.

And you would be correct, except for the part about being human.

Amy Ingram, as her initials imply, is not human.

She’s AI.

Artificial Intelligence.

Amy is a virtual Personal Assistant created by a company called x.ai.

As of this article, Amy, and assistants like her, are in beta (and there’s a long wait list for them).

Amy is an example of how close AI is to replicating human interaction.

Amy will one day replace Abby’s Recruiting Coordinator (if she had one) (she’s smart not to), often relegated to scheduling interviews and managing Abby’s calendar.

When I heard about Amy, I asked myself, “What other HR functions would AI one day replace?”

What Does AI Look Like?

HAL-9000. The Terminator. Agent Smith.

Hollywood has shown us the ways that AI could go bad.

It makes for great cinema – human beings creating intelligent machines creating chaos for their human creators.

When we think of AI, the idea of a computer obtaining consciousness is what we envision.

The world’s most brilliant minds – Stephen Hawking, Elon Musk, Bill Gates (to name a few) – aren’t just debating when this consciousness will take place, but whether it spells the end of humanity as we know it.

I won’t get into what will happen when AI ultimately achieves consciousness (it will). Way smarter people have written plenty on this topic already.

If you’re interested in the future of AI, and the ethical conversations around it, read this article at Wait But Why. Set aside some time because it is a deep-diving, comprehensive summary of AI and its implications for our future. It will blow your mind.

When I started contemplating AI’s impact on the future of human resources, I thought about Artificial Narrow Intelligence (ANI), the lowest caliber of AI, created for one specific purpose.

Believe it or not, you’re already interacting with this kind of AI – when you use your phone, access your Amazon account or browse your Netflix recommendations. These are the programs that determine what you like based on what you’ve read or what you’ve watched.

When I searched “AI in HR” to find out what was already being done, many of the articles I read were about algorithms that could identify talented candidates or identify trends in unwanted turnover.

While these programs are plenty useful, I had something else in mind.

When we ask Siri to find something for us, it feels like we’re talking to a machine. Siri doesn’t feel human.

When you communicate with an AI that has generated specific deliverables for a specific person, it feels less like an interface and more like an interaction. It feels human.

Like Amy.

As AI becomes more human, my entirely unscientific estimate is that it will assume 80% of the work that HR professionals are doing today. With that said, go ahead and ask yourself…

Should HR Fear AI?

When I think about HR, I think of it in two parts:

  1. Strategic ideas that optimize the people and processes of the organization, and
  2. Administration and compliance to mitigate risk.

Part one is where HR professionals wish they could spend their time.

Part two is where HR professionals actually spend their time, and where AI will displace us practitioners.

To demonstrate, let’s take a look at four HR functions where “someone” like Amy will likely replace today’s practitioners.


Amy already demonstrates what can be done in terms of supporting a recruitment function, but that’s just the beginning.

Soon, a hiring manager will be able to send this email to Phil, an AI Recruiter:

“Phil, I need to open a req for a new Business Analyst.”

Phil will respond, almost immediately:

“Certainly! Let’s start by figuring out what you’re looking for. Can you tell me what skills or experience this person will need to have?”

The dialogue continues until Phil has all of the information needed to build a candidate profile.

Once Phil has the profile, he can search all available online sources (candidate databases, social media, etc.) until he identifies someone who closely matches it.

In short order, Phil generates a list of candidates, complete with résumés and online profiles. He then sends them to the hiring manager for review.

Each time the hiring manager responds to the candidates Phil submits, Phil uses the feedback to refine his search and identify candidates that more closely match the hiring manager’s perception of the ideal candidate.

At the same time that Phil is searching for additional candidates, he’s simultaneously contacting a few of those initial candidates that the hiring manager expressed an interest in, asking them if they might be interested in the open position.

Phil works 24 hours a day, 7 days a week, and he can do all of this for every hiring manager in your organization at the same time.

You might be asking yourself, “Why wouldn’t hiring managers just enter the candidate profile into an ATS and just let the system turn up the talent?” Well, if that was the solution, one of the myriad ATS vendors would have figured it out by now and conquered the industry.

The issue isn’t applicant tracking systems, or databases. It’s that hiring managers don’t go through the hiring process enough to remember how to login to the ATS, let alone create a requisition.

It’s the interface via a channel they use every day, in this case email, which makes the interaction between Phil and the hiring manager work.

It’s the Socratic method of inquiry an AI can replicate that leads to actionable data.

The same back-and-forth today’s recruiters have with hiring managers can be done by Phil.

Recruiters are probably reading this, shaking their head, and exclaiming, “There’s more to recruiting than that!”

I agree.

If the hiring manager and Phil can’t get to a point where they agree on the candidate profile, or if the hiring manager becomes frustrated because his or her needs aren’t being met, that’s where the Recruitment Coach (Mentor, Lead, etc.) can step in and sort it out.

Recruiters, be honest with the amount of time you spend finding candidates and getting basic information on the req from hiring managers, and rejoice at the prospect of having to do less – much less – of both of those things.

Instead of being an administrator in talent acquisition, recruiters can become strategists and coaches who work alongside hiring managers, educating them on how to hire and retain the best talent for their organizations.


Compensation Analysts in many organizations are inundated by the same daily request, “Can you look up Sarah’s pay and tell me if it’s within the range?”

In a tech-enabled, open organization, the manager that sent that request could login to a compensation system and locate a graphical depiction of a range with Sarah’s placement in it.

I would bet that more than 97% of you reading this article don’t work in such an organization.

Even if you had the system to enable this, your manager can’t remember how to access the system, and if she can login, she can’t remember how to find the information she’s looking for.

Compensation data is not her job.

She runs Sales, or IT, or Finance. We can’t expect her to be proficient in HR systems as well.

So, what happens next? The Comp Analyst looks Sarah up in the HRIS, finds her current salary, and determines her percentile within the positional range. He might even pull some external market data. If he’s a truly high performer, he’ll do an internal comparison of Sarah against her peers within the organization or department.

With experience, the Compensation Analyst learns what certain managers like. He anticipates the questions they might ask, and he does his best to incorporate answers to those questions in his analysis.

Now, meet Buck.

Buck is like Amy or Phil. He’s an AI that takes that initial request and runs through the progression listed above.

The difference? Buck can do this in a matter of seconds, and for 10, 20, 100 managers at the same time.

Buck emails his analysis and, much like his high-performing, human counterpart, Buck learns what questions managers will ask next and what they might do with that information.

After hitting send on the salary data, Buck promptly asks, “Are you thinking about giving Sarah a raise or promoting her to a new role? Please let me know and I can run both scenarios for you.”

While Buck seamlessly handles all of this, what’s left for the compensation team?

Buck can recommend that market value for any given position, but only the Compensation Lead can work across the organization to ensure that managers are making the best decisions when it comes to their pay practices.

Only the Compensation Lead can leverage data and behavioral psychology to develop incentive programs that will drive true business value.

Long story short, Buck takes the reins on repetitive, administrative tasks, enabling your compensation department to set the strategy and execute it.


When a new employee starts with your company, what does their first day look like?

At the very least, your infrastructure team sets that new hire up with the technology they will be using from here on out. In a larger organization, your new hire might spend hours completing e-learning modules or reviewing exasperating decks on why the company exists, how the company makes money, and what is expected of the people who work there.

Basically, they complete the standard-issue new hire checklist – the onboarding gauntlet.

Companies like Tasytt are trying to change this. They’ve created Obie, an onboarding coach to help you find all of the information you need to ramp up within your new organization.

According to Tasytt, “Obie offers a familiar, conversational user experience you’ll actually enjoy. He can answer questions and send [out] bite-sized knowledge to the team. Obie is a quick-study – the more you use him, the more he delivers relevant and accurate content.”

Poof. No more checklist administration for your HR Generalist.


The world of employee benefits mirrors the world of HR – strategic and administrative.

The strategic component involves reviewing plan designs and claims trends to determine how to build a benefits offering that is market competitive, yet manages the financial risk of the organization.

The administrative component comes into play when employees ask about their benefits effective date, or how to navigate the enrollment portal, or if a claim isn’t processed just as they expected it would have been.

Experienced Benefits Specialists have accumulated the knowledge to answer these questions and just about every other question that comes up.

They answer these questions over and over. And over.

This knowledge takes time to build, and it’s difficult to replace when one of your Benefits Specialists gets promoted or leaves the company

Imagine something (or “someone”) similar to Obie, but with a knowledge base in benefits.

This AI Benefits Specialist – let’s call him Benny – will answer employees’ questions about any of the benefits available to them.

As Benny answers question after question, he begins to improve.

At first, he’ll need some help from your benefits team, but as time passes and Benny learns how to properly apply your organization’s policies, Benny will need less and less help from his human counterparts.

What happens to the Benefits Specialist then?

They can stop spending their time answering the same questions over and over. And over.

They can identify the root causes of the problems that lead to those questions in the first place.

They can help educate employees on the state of the market, and what it will take for them to retire comfortably.

They will do all of those things that they have always talked about doing, but never quite got around to because they were answering the questions that Benny will now answer for them.

Are these the only roles AI can fill in HR?

If you are an HR practitioner that is specialized in employee relations, you might think that AI has no placed in your world.

Until you meet Ellie.

Ellie is an AI currently being developed by the U.S. Military to diagnose psychological ills borne by those in uniform.

Ellie asks questions and then visually scans the faces of service members as they respond to detect expressions that help evaluate the truthfulness of the response.

If an AI can talk to veterans and active service members about their experience in war, the technology can certainly be leveraged by employers to resolve issues employees are having with their colleagues and ultimately determine whether an investigation is warranted.

Why will AI succeed where other technologies have failed?

Each one of the roles I describe above uses technology which was sold on the promise that it would free up resources or require fewer headcount to deliver data to the people who need it.

The pitch was, with the right technology, managers would be able to open their own requisitions, get their own pay data, and provide onboarding and benefits information in ways that employees would understand.

That promise was not fulfilled.

The reason for this, is that the interface is not human enough. Human managers need a human link between themselves and the data that is housed in an organization’s various systems.

Today, that human link is the Recruiter, the Comp Analyst, the HR Coordinator, and the Benefits Specialist. Tomorrow, that link will be artificial intelligence.

Even though AI is not human, it will be human enough to bridge the gap between the manager and the data.

What does this mean for HR?

You should be concerned, and you should start looking ahead.

Let’s be clear, there is nothing so special about the humanness you deliver to your organization that cannot (eventually) be replaced by a machine. Email – where you spend the majority of your day in human resources – can be managed by artificial intelligence that feels no less human than the service you would have provided.

My advice? Embrace this industrial evolution and invest in leveraging your newfound freedom from mundane tasks to operate at a more strategic level.

In their book, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, Tom Davenport and Julia Kirby explain 5 things employees can do in response to the automation of their positions. These “steps” were nicely summarized in this article by Bernard Marr:

Stepping Aside means leaving the machines to do what they do best, and picking a career requiring skills such as creativity or empathy.

Stepping Narrowly means developing a specialty, in a field where there is little demand or no business case for implementing automation (a local tour guide, or a wine expert specializing in a particular region, being possibilities here).

Stepping Up means taking oversight of and responsibility for the work carried out by computers – essentially becoming their boss, and considering the big picture strategy of implementing technology across an organization.

Stepping In means to become involved with the work being carried out by machines, to fine-tune and provide human oversight in areas where it is still needed. Real world examples here could be an Accountant trained to spot errors caused by an automated system, or an Ad Buyer who can spot when a brand could be damaged by a particular placement, for reasons a robot might not comprehend.

Lastly, Stepping Forward is to work on developing the next generation of robotic and AI-driven technology. Robots can solve problems for us, but we still need to tell them what problems need solving. It still takes a human to understand that automation will be of benefit to a particular area of business, and a human to put together a strategy for automating that section.”

Remember how I said that there are two parts to HR?

The good news is that AI is going to take care of that second part, so that HR practitioners can spend their time on the first. Thinking in these terms, and through the lens of Davenport and Kirby’s 5 Steps, should give practitioners hope, rather than fear, in the face of the impending automation of their function.

What can HR do today to prepare for the HR of tomorrow?

There are two things that will make HR practitioners successful in the future: (1) an ability to understand and speak the language of data, and (2) focusing on the “human” in human resources.

Recruiters should hone their coaching skills. Learn how to spot and explain hiring biases to managers. Understand how candidates can become high-performing employees.

Compensation teams and Benefits Specialists should hone their skills in data science and behavioral psychology.

Now, I’m not suggesting you need a Ph.D. in data science, but explore free or inexpensive resources like Data Science Central or Data Camp, or download two of the more common data science languages, R and Python.

Put yourself through a behavioral psych boot camp – the study of why people behave the way that they do. Start with Freakonomics, then read anything by Dan and Chip Heath. Read Nudge and, if you’re ready for something really deep, read Thinking, Fast and Slow.

As you learn more about human behavior, you will realize that most of the compensation and incentive programs out there today run contrary to what motivates us humans.

Use these skills to identify trends and opportunities within your compensation structure or your claims trends.

Use this knowledge to design more effective compensation programs.

The AI End Game

Think about how many times you have wished you could elevate your position to focus on more strategic deliverables, but have instead been dragged down to pure task administration.

Envision a future where you had the time to produce those deliverables that would provide true business value.

When AI takes on HR, we can finally focus on the ideas that will make our organizations, well, human.

AI won’t eliminate HR. It will liberate it.

Leave a Comment