The current state of AI in recruitment reminds me of the emergence of "social recruiting" as a construct and as a mostly vacuous buzzword that we all experienced about a decade or so ago (even then, HR Tech was years behind the consumer adoption curve, a phenomenon that, if anything, has only gotten more pronounced).
Now, instead of "social recruiting platforms" or "talent communities" (barf) we now have "AI-driven talent acquisition solutions" (real tagline, by the way - super catchy), same empty promises, different generation threatening to upend the workforce as we know it (millennials having become more or less passe at this point, with "Gen Z" enjoying its twilight hours in the spotlight).
According to Gartner research, approximately 83% of HR technology vendors claim to offer AI capabilities, yet when pressed for specifics, most demonstrate functionality that would have been considered basic automation back when X was still just plain old Twitter.
The cognitive dissonance is staggering; venture capital has poured over $6.7 billion into HR tech startups since 2021, creating a market where distinguishing genuine innovation from cleverly marketed mediocrity requires the snooping skills of a SourceCon Grandmaster (is that still a thing?) combined with the technical knowledge of a Google engineer and, probably, a prominent placement somewhere on the autistic spectrum.
The Final Battle: Revenge of the Final Budget
Transform, as one of the industry's increasingly important annual gatherings for the familiar persona that has long been foundational to HR Technology marketers: the technologically challenged and fiscally imprudent ICP that generally signs off on purchasing decisions, provides the perfect laboratory for observing this phenomenon in its natural habitat, a veritable safari of solution enabled technology (read: stuff that requires a ton of consultants, but looks really good in a demo environment).
The expo floor will undoubtedly be teeming with vendors whose marketing collateral has "AI" built into every product pitch or value proposition, unlike, say, their actual platforms. That's why Recruiter.com has compiled this handy, dandy guide for Transform 2025 attendees (or anyone else in our industry) to navigate through the labyrinth of overly eager sales reps and platform pushers, all attempting to convince you that their algorithm's ability to sort resumes alphabetically represents the vanguard of the fourth industrial revolution.
More Than Meets the Eye (or way, way less)
As someone who has spent more time than any sane person should enduring vendor demonstrations where "AI-powered" apparently means "we added a search bar," I thought it might be helpful for my fellow Transform attendees to have some real intelligence to go along with whatever passes as AI these days.
Consider this a survival guide for navigating conversations with the manifold vendors in our space who, like the VCs inexplicably making it rain like the Showtime Lakers at a Spearmint Rhino, who believe adding "AI" to a product description justifies a 300% price increase. It's not real money, until it's coming out of your budget.
Artificial Intelligence
What vendors say it means:
The revolutionary technology that will transform your recruiting function, eliminate bias, and finally let you achieve that elusive work-life balance you've been promising your significant other for the last decade - and actually give you the time you need to engage with actual candidates, stakeholders and employees.
What it actually means:
Algorithms trained on data sets of questionable origin that replicate human decision-making with just enough accuracy to be dangerous. When a vendor tells you their product uses "artificial intelligence," they typically mean "we've implemented some if/then statements and a keyword matcher from 2005."
I once asked a vendor about their training data, and they looked at me as though I'd requested their firstborn child's social security number. Which was encouraging, since for a minute there I thought they actually had some sort of proprietary LLM or longitudinal training data, Then, they told me they were just out-licensing Chat-GPT, which figures.
It's like how "programmatic advertising" vendors just buy the same underperforming display ads as before, only with higher margins and minimal results. Yeah, come @ me, bruh.
Machine Learning
What vendors say it means: A sophisticated system that continuously improves itself by learning from your organization's unique data patterns, creating a bespoke solution that understands your company's DNA.
What it actually means:
Pattern recognition that works adequately on the demonstration data set but will inexplicably crash when processing your actual requisitions. The "learning" part typically requires your team to spend countless hours manually correcting its mistakes, which your leadership will assure you is "just part of the implementation phase" – a phase that somehow never ends, or at least until it's time to start talking contract renewals.
Natural Language Processing
What vendors say it means:
Technology that understands the nuances and context of human language, enabling it to parse job descriptions, candidate responses, and complex queries with near-human comprehension.
What it actually means:
A system that can recognize keywords but struggles with basic homonyms, idioms, or industry jargon. The "natural" part is aspirational at best. I once watched a demo for an NLP-powered chatbot, er, "AI agent" which confidently responded to a basic query for "Java developers" required "experience as a barista and/or coffee roaster" – a mistake that was simultaneously funny as hell, but also, emblematic of the technology's limitations when it hasn't been trained on domain-specific language.
Which, as this article should prove, is a pretty big blind spot, if we're being honest.
Predictive Analytics
What vendors say it means:
Sophisticated forecasting that anticipates hiring needs, identifies flight risks, and predicts candidate success with uncanny accuracy, all based on robust statistical models.
What it actually means:
Correlation analysis masquerading as causation, usually based on historically biased data that ensures you'll reproduce the same hiring patterns you're trying to diversify away from. The prediction accuracy hovers around what you'd achieve by asking your office manager who they think might quit soon. According to my last predictive analytics dashboard, I was a 92% flight risk despite having absolutely no desire to go anywhere, considering my other options involve, well, marketing AI for recruiting, and we all know that's almost as big of a farce as "thought leadership."
Conversational AI
What vendors say it means:
An engaging, responsive digital assistant that provides candidates with personalized communication, answers their questions, and guides them through your hiring process with the charm of your best recruiter.
What it actually means:
A glorified FAQ system with pre-programmed responses that works beautifully until someone asks something slightly outside its script. The "conversation" part is generous – it's more akin to a particularly stubborn customer service representative who keeps directing you to read the website no matter what you ask.
Just this morning, I got a text from an employer I'd audited in the past, cheerfully telling me that they just opened some entry-level retail positions which were "a great fit based on your profile." Sad thing is, they're probably not wrong.
Semantic Search
What vendors say means: A revolutionary search capability that understands context and intent, not just keywords, allowing you to find candidates based on skills and capabilities rather than just buzzword matching.
What it actually means: Slightly better keyword matching that occasionally recognizes synonyms. The "semantic" part suggests a depth of understanding that simply isn't there. I once tested a semantic search by looking for "mobile developers" and received results for candidates who provided mobile phone numbers, which technically wasn't incorrect. But, given this technology has existed in TA since at least 2012, isn't the greatest example of what happens when you believe the hype, or get high on your own supply.
Algorithmic Bias Mitigation
What vendors say it means:
Advanced technology that eliminates human biases from your hiring process, ensuring fair, objective candidate evaluation based purely on qualifications and fit, as opposed to the cognitive operational bias hiring practitioners are used to, evaluating candidates based solely on protected characteristics.
What it actually means:
A checkbox on the old compliance form that ostensibly addresses bias (because DEIB, we just don't know how to quit you), but actually just obscures it beneath layers of code no one in your organization can interpret.
These systems typically "mitigate bias" by removing obvious demographic indicators while preserving the countless proxy variables that correlate with them. I've yet to see an algorithmic bias solution that wouldn't be improved by simply having previous employers' names or education history redacted from stack ranked results, but what the hell do I know? I'm just a mediocre white guy, which is why I'm writing a blog post about AI and recruiting.
Deep Learning
What vendors say it means:
The most advanced form of AI that mimics human neural networks to solve complex problems through sophisticated pattern recognition that transcends traditional programming limitations.
What it actually means:
An expensive, opaque system that makes decisions no one can explain or defend when your CHRO inevitably asks why it rejected every candidate from a particular university. The "deep" refers less to its profound intelligence and more to the deep trouble you'll be in when you can't articulate how it works to your legal team.
My experience with deep learning tools suggests they're exceptionally good at identifying patterns that don't actually matter and ignoring ones that do. Or, as we call it in these parts, "big data."
Talent Intelligence Platform
What vendors say it means:
A comprehensive, AI-driven ecosystem that seamlessly integrates with your existing tech stack to provide unprecedented insights into your talent landscape, both internal and external.
What it actually means:
A dashboard that consolidates information you already had access to, presented with more colorful graphs and a significantly higher price tag. The "intelligence" part is aspirational, and the "platform" means you'll need three different logins and a dedicated admin just to pull basic reports.
The last talent intelligence platform I used crashed whenever there were multiple enterprise users trying to access the system simultaneously, which I suppose is one way to limit access to sensitive information and protect data privacy. Of course, the end user adoption rates of most of these turkeys is, admittedly, a pretty good tactic, too.
Optimus Primed for Disappointment: AI at Transform 25
As you navigate Transform surrounded by eager vendors wielding these terms like incantations guaranteed to open your department's wallet, remember: beneath every buzzword is a simple function that probably doesn't work quite as advertised.
The truly intelligent approach, then, to "AI" is simple: use Occam's razor, maintain healthy skepticism, demand demonstrations with actual data on a live instance, and remembering that sometimes a spreadsheet and human judgment still outperform even the most advanced agentic AI instance. For those HR leaders with decades of experience, well, the secret is, going with your gut is probably going to yield better results in less time than any algorithm out there on the market.
And if someone tells you their AI can "revolutionize your entire talent acquisition function" – well, I've got some Web3 recruiting tokens to sell you...
This article was composed entirely without any AI assistance, which might have been a mistake, since it turns out, these damned things take forever to write, and I've got other stuff to do at Transform. See you soon?
