Every day there seems to be a new tool that’s intended to help sourcers and researchers go further, faster, and better in the pursuit of talent. Much of the conversation today is around artificial intelligence, or more appropriately the large language models that are being baked into everything from automating sourcing to candidate outreach.
But is this new?
How is this different from any number of alerts previously set inside of Google, or even the job boards?
Is it any different?
What Exactly is AI? LLMs? ChatGPT?
In my pursuit to answer about the newness of artificial intelligence, I want to dissect three areas: Artificial Intelligence, LLMs, and ChatGPT. They sound like three episodes of Black Mirror, right? But they’re not. They’re here. They’re now. And they’re shaking things up in ways we couldn’t have imagined just a few short years ago.
Let’s talk about AI first. Artificial Intelligence. It’s a powerful tool that’s changing every facet of our lives, from how we shop for groceries to how we diagnose diseases. AI is, in the simplest terms, the process by which machines or software mimic human intelligence—learning from experience, adjusting to new inputs, and performing tasks that traditionally required human brainpower. It’s your Siri, your Alexa, your Tesla Autopilot—it’s machines getting smart, and not in the ‘take over the world’ way Hollywood promised. At least, not yet.
Next on the docket, LLMs. Large Language Models. They’re like the Shakespeare of AI, spewing out text like they’ve got a doctorate in English Literature. But instead of centuries of human experience, they’re fed with a massive amount of text data, and they generate text that can answer queries, translate languages, summarize long documents, even generate poetry. But, bear in mind, they don’t understand what they’re saying—they don’t have feelings, desires, or consciousness. They’re just incredibly good at predicting the next word in a sentence. It’s like playing a game of Scrabble with a robot who’s eaten a dictionary.
Which brings me to our last guest of honor—ChatGPT. It’s an offshoot of the fine folks over at OpenAI, and it’s a prime example of an LLM. ChatGPT is the online cousin of the notorious GPT-3, and it’s capable of generating pretty darn convincing text based on the prompts it’s given. Think of it like a chatbot on steroids. You give it an input—like, ‘tell me a story about a cat in a spaceship’—and voila, you’ve got yourself a feline space opera. But remember, it’s not sentient—it’s just good at its job.
AI, LLMs, and ChatGPT. Three acronyms that are changing the world. But are they going to render us obsolete? Not likely. As long as there’s a need for creativity, innovation, and a human touch, we’re not going anywhere. But we’ll have some incredibly smart tools at our disposal to do great candidate research, right?
Evolution of Talent Sourcing and Candidate Research
Okay, let’s buckle up and take a ride down memory lane, starting from a decade ago, when job boards like CareerBuilder and Monster.com reigned supreme in the talent sourcing and candidate research landscape. They were the “in-thing”, the place to be, the hub where talent and employers met. But boy oh boy, how times have changed!
Flash forward to just a few years later, LinkedIn comes into the picture, turning the traditional recruitment model on its head. This platform became a virtual networking heaven, where every job seeker could put themselves on a platter for potential employers to see, and every recruiter could sift through candidates like they were shopping at the talent supermarket. LinkedIn revolutionized talent sourcing, and it wasn’t just a place to find candidates, it was a place to get to know them.
The beauty of LinkedIn lies in the layers of information available on candidates—work histories, skills, endorsements, recommendations, shared articles, and so much more. It wasn’t just about sourcing candidates, it was about sourcing the right candidates. But like every disruptive innovation, LinkedIn was just the beginning.
Enter the age of AI and data-driven tools like SeekOut, Gem, and HireEZ. Now, we’re not just talking about a game changer, we’re talking about a whole new sport. These tools took everything good about LinkedIn and cranked it up to eleven.
SeekOut, for instance, didn’t just look at candidates’ profiles, it looked at the whole Internet. Its AI could find, aggregate, and analyze candidate information from social media, public records, and more, giving recruiters a 360-degree view of potential hires.
Then you have Gem, a tool that some say made candidate relationship management a breeze. It’s like the CRM for talent sourcing, tracking every touchpoint with potential candidates, making follow-ups easy, and ensuring no promising talent falls through the cracks.
And we can’t forget HireEZ. Some could say, this platform was a goldmine for anyone hiring in tech, boasting a database of millions of IT professionals globally. Its AI could sort through this enormous pool of talent to find the best candidates for specific roles.
In essence, the past decade has seen the evolution of talent sourcing from a simple process of sifting through resumes on job boards, to utilizing the networking capabilities of platforms like LinkedIn, to leveraging advanced, AI-driven tools that can source, track, and analyze candidates like never before.
And the best part?
We’re still at the beginning.
The Rise of AI in Talent Sourcing and Candidate Research
The rise of AI in talent sourcing and candidate research isn’t tied to a single moment, but rather, it’s been a process that’s been gathering steam over the past several years, with the most significant acceleration seen in the last 5 to 7 years.
This rise was spurred by a myriad of factors. Think about the evolution of Big Data, the improvements in computational power, the refinement of machine learning algorithms, and the burgeoning necessity for more efficient talent acquisition strategies in an increasingly competitive market.
So, what did this mean for recruiters?
Quite simply, it was game–changing. It was like someone walked into their office, dumped a bucket of efficiency, precision, and data insights on their desk and said, “Good luck, you’re going to nail this!”.
Some could say, AI has given recruiters superpowers. Remember the good old days of poring over hundreds, maybe thousands, of CVs and resumes? Those days are long gone. Now, AI tools can do the heavy lifting—scanning, sorting, and ranking candidates based on specific job requirements—in a fraction of the time.
Recruiters will have more time to focus on what they do best—connecting with people and building relationships.
Let’s talk about the global reach. It used to be that recruiters were largely limited to their local talent pool, but AI has blown those geographical boundaries out of the water. Now, recruiters can source talent from just about anywhere, broadening the diversity and skills of potential candidates.
And then there’s the issue of bias. We all have them, and in recruitment, they can be a significant hindrance. But AI has the potential to minimize these biases by focusing on data, skills, and qualifications, rather than subjective factors.
However, let’s not paint an overly rosy picture here. The rise of AI in talent sourcing and candidate research also brought challenges. For one, there’s the fear of job displacement—though, in reality, AI is more likely to augment roles than replace them. There’s also the risk of relying too heavily on algorithms and losing the human touch that’s so essential in recruitment.
Moreover, AI is only as good as the data it’s trained on, and if that data reflects societal biases, the AI could perpetuate them. Therefore, while AI has undeniably been a game-changer in recruitment, it’s not a panacea. It’s a tool—one that, used responsibly and thoughtfully, can bring incredible benefits to talent sourcing and candidate research.
In Part II, we will continue the journey and dive into the impacts of AI on talent sourcing, taking a look at more of the good, as well as a look at the not so good. We’ll also touch improving speed in the workflow, efficiency, and reducing biases.