The Realist’s Guide to AI in Recruitment: What’s Really Happening?

Lately it feels like AI in recruitment is everywhere.

If you're on the hunt for new recruitment technology, you're likely feeling like every website and product boasts "AI capabilities."

But what does that really mean? Is it genuinely AI, or just a buzzword slapped on so that the company sounds innovative? Did they create their own AI solution, or are they just using a ChatGPT plug-in and calling it their own? 

And more importantly, for recruiters, you’re probably asking: "What do I actually need to know?"

Since we live and breathe recruitment technology and AI, we wanted to break this down a bit. So let’s take a pragmatic look at AI's role in recruitment — and where the real impact will come from.

Chapter 1: The reality of AI in recruitment technology

First off, where are AI solutions for recruitment actually going to come from?

Many assume it will be early-stage startups in Silicon Valley. The truth is, it won’t.

Why?

AI is expensive to execute and maintain. OpenAI reportedly spends around $700,000 per day to run its GPT models. The amount of server horsepower and hardware required is staggering.

That’s why unit economics don’t work well in the recruiting tech space, which traditionally operates as business process software.

HRTech requires vast amounts of compliance, change management, and infrastructure. This is a decades-long process, and AI is no shortcut. The timeline required for meaningful AI in recruitment doesn’t align with venture capital’s demand for quick returns.

Every year, new recruitment startups come out of incubators like Y Combinator — and every year they face the same problem: growth rates and adoption in the recruitment space aren’t enough to make them viable long-term.

As a result, they either fizzle out or are sold off. This pattern has been going on for over a decade, and it will likely continue as artificial intelligence becomes even more ubiquitous.

Even large recruitment organizations like Korn Ferry and Robert Half can’t fully commit to AI for the same reasons. Many of these big firms raised capital in hopes of developing their own AI platforms, only to realize the costs are unsustainable. Investors may see the potential, but these companies would have been better off sticking to their core strengths in recruitment rather than trying to pivot to tech.

The main reason for this rush into AI? Valuation.

Recruiting firms want to increase their business valuations by positioning themselves as tech companies. But with a 0.6x revenue multiple, recruiting firms have one of the lowest valuations among professional services. This low multiple drives recruitment organizations to dabble in tech, but unfortunately, they often realize too late that the model just doesn’t work.

So, where will the successful AI-driven recruitment solutions come from? Likely from the incumbent tech giants that already have the scale and resources. Companies like LinkedIn, Loxo, Greenhouse, Workday, and Indeed have the best chance of integrating AI successfully because they already have the infrastructure, capital, and business models to support it.

Chapter 2: How recruiters can prepare for AI’s impact

So, all of that is fine and good... But what should you do if you’re a recruiting professional trying to future-proof your career?

Here’s what to consider...

Agency-side recruiters:

If you work for a recruitment agency, you’re in a strong position. AI will decentralize the power held by large firms like Korn Ferry and level the playing field for smaller agencies. Large organizations, tied to their proprietary systems, will struggle with slow, clunky processes. Clients will turn to smaller, more nimble firms that can provide better experiences, faster delivery, and high-touch service.

Corporate recruiters:

On the corporate side, AI is more of a threat. If your role involves screening inbound applicants and posting jobs to job boards, AI could automate a lot of your tasks. AI will force corporate recruiters to move up the value chain, focusing more on relationship building with top talent and offering strategic guidance to hiring managers. Recruiters who excel at networking and talent advisory will thrive. Those who spend their time on repetitive tasks, like sourcing candidates through job boards, may find themselves replaced by automated systems.

Chapter 3: What AI in recruitment looks like today and tomorrow

Now let’s talk about specific use cases where AI is already playing a role — and what’s on the horizon for the next two years:

- Generative AI: This is the hottest trend right now (think ChatGPT). Generative AI will transform any text-based process in recruitment, helping recruiters write job descriptions, outreach messages, and more—faster and more efficiently.

- Automated sourcing: AI can quickly sort through large talent pools to identify the best candidates, cutting down on the time recruiters spend combing through resumes.

- Resume ranking: Tools that automatically rank inbound applicants based on job descriptions are already being deployed, and AI will make these systems smarter and faster.

- Conversational chatbots: AI-powered chatbots can handle initial candidate screenings, automating high-volume tasks like answering basic questions and collecting initial information.

- Outbound recruitment: In the near future, expect AI to automate much of the candidate outreach process, enabling more personalized, multi-step communication without the recruiter needing to manually craft each message.

For recruiters who are elite relationship builders, AI will be an asset, enabling them to focus on high-value tasks while automating repetitive processes. Recruiters who thrive in high-touch, strategic roles will be rewarded financially. However, those who lean on outdated sourcing methods will likely need to pivot or risk being left behind.

Chapter 4: The overlooked use case – knowledge graphs

A final, less-talked-about use case for AI in recruitment is the development of knowledge graphs.

Knowledge representation is essential for AI to truly understand and make sense of data. For example, a knowledge graph in recruitment could link entities like candidates, jobs, skills, and locations in a way that allows AI to draw insights from complex relationships.

Building a robust knowledge graph is a massive undertaking and requires significant investment. Few companies outside of giants like Google, Microsoft, and Amazon have successfully developed one for commercial use. This level of AI requires structured data and intricate taxonomies that allow machines to learn and identify patterns. Without it, AI in recruitment will never be as effective as a top-level human recruiter.

Wrapping up: The evolution of AI in recruitment

The recruitment industry is poised for a shift, but we don't anticipate that it will be the sudden AI revolution that some expect.

Instead, we’re looking at a gradual, decades-long evolution (one that's been building for quite some time) where AI augments and automates certain tasks, but the human touch remains essential.

Moral of the story? The best recruiters will be those who embrace AI as a tool to enhance their capabilities rather than fearing it will replace them. Recruiters who are able to leverage AI to automate manual and administrative tasks (and become more organized) while spending more of their time and energy on highly human or strategic activities will be the recruiters who come out on top.

Not-so-subtle plug for Loxo: our Talent Intelligence Platform was designed to support your ideal workflow — and with the perfect balance between technology and human strategy in mind.