How To Leverage AI And Automation For Strategic Hiring

Key trends and challenges in AI-driven recruitment for 2023 and beyond

It’s no secret that talent acquisition is more competitive, and more cutthroat than ever, the result of the ever increasing imbalance between employer demand and candidate supply. Across markets, industries and company sizes, organizations are having a historically hard time attracting, converting and retaining the skilled workers required to survive - and thrive - in today’s dynamic business landscape.

Hiring success has emerged as the ultimate competitive advantage, a critical core corporate competency when it comes to both the bigger business and the bottom line. This has led many employers to turn to artificial intelligence (or AI) and automation to make their hiring process more efficient and effective.
 

By removing the requisite recruiting roadblocks and minimizing labor market challenges, AI and automation capabilities enable employers to stay a step ahead of both external competition and internal demand for an increasingly finite supply of qualified talent.

While HR technology has matured into a fully entrenched and established category of enterprise software, the rise of AI and automation is transforming our traditional understanding of what “talent tech” can do, as well as our understanding of what’s possible when it comes to workforce planning, training & development, and employee retention.

The promise and possibility of these emerging technologies remain unfulfilled, but there’s no doubt that AI and automation have become inextricably intertwined with how hiring works, no matter where in the world of work you happen to work.

Even though “AI” and “automation” might already come across as meaningless buzzwords or a trite trending topic, the fact of the matter is they represent among the most important developments, and top TA trends, in recruitment and hiring for 2023 - and beyond.

If you haven’t been sentient or paying attention for the past year or so, here’s a brief recap of how companies are already using AI to transform their talent acquisition function.

Yeah, we know, these use cases are all pretty much table stakes at this point, and most people leaders already understand and utilize AI functions and features, but for those remedial recruiters out there, AI can be used for a bunch of helpful stuff, like creating the first drafts of all those “how AI is changing recruiting” articles that are everywhere, all the time. Not to mention:

Sourcing: AI-powered tools can instantly crawl and index vast amounts of existing information from publicly available online sources like company directories or contact databases and professional networks (by which we actually just mean LinkedIn, really), providing a great way to quickly identify qualified candidates, no Boolean strings attached.

Resume Screening: Using AI-powered resume screening systems (actually, these instances combine machine learning and stack ranking, but AI sounds sexier), recruiters don’t have to waste the 8 seconds they used to spend reviewing the average resume, which gives them enough time back in their day to keep not calling candidates back. Remember, subconscious bias should be systematically eliminated from recruitment, unless it’s automation bias. Algorithms can always be trusted, after all - particularly when it involves shortlisting.

Predictive Analytics: What’s better than one buzzword? With AI-powered predictive analytics, the answer is two buzzwords and one amorphous concept that sounds pretty legit. And while companies still can’t tell you how much they’re paying per hire, or what the true source of those hires actually is, AI is here to help - because unlike math, it’s basically idiot-proof. So, theoretically, organizations can use AI to perform pattern recognition on large talent datasets and forecast future headcount needs or market/competitive intelligence, transforming data into actionable insights to drive more informed strategic decisions and improved TA outcomes. Let the machines do the math because if you could do advanced analytics, you probably would have found a better job than TA.

Chatbots and Virtual Assistants: Again, this isn’t technically AI, but semantics are for search. When that search ends up identifying a potential candidate or driving qualified traffic to your career site, chatbots and virtual assistants provide an ideal way to answer any candidate questions and hiring process support at scale - except if those questions are stuff like, ‘why do I need a username and password to apply for this job’ or ‘can you give any feedback on why I wasn’t selected.’ Still, it’s kind of a flex for your employer brand to show how cutting-edge your company is having capabilities like chatbots…right there on your career site.

A recent article that we would cite, but no one clicks anchor links so what’s the point, highlighted that 48% of organizations (or nearly half, for all you predictive analytics experts out there) heavily use AI to enhance talent acquisition.

The most common tech tools in recruitment, such as applicant tracking systems, background and reference checks, employee referral solutions and assessment tools, are also the most common solutions leveraging AI or automation capabilities as part of their core product offerings, a trend that will only accelerate as more recruiting startups have to work even harder to raise additional capital and justify their overvaluation to the market. Just saying “AI” can transform a technology provider’s multiple from 10x to 15x or more, which will come in handy once the inevitable AI-related employment lawsuits start to hit.

Companies in India have been early adopters of AI-enabled technologies and tools to automate and streamline their hiring processes, enabling cost and time savings and improved hiring outcomes while reducing bias in the hiring process.

For example, companies such as Byju’s or the Adani Group might be facing massive layoffs as the result of corporate fraud, but they do use a tool that offers AI-powered intelligent hiring, improved candidate experience and people analytics to accelerate and smoothen their hiring processes, while also enabling better decision-making capabilities than their executive, ops or finance counterparts, apparently. Too bad there’s not a chatbot for the Securities and Exchange Board of India…yet.

The Challenges and Risks of Leveraging AI in Recruiting and Hiring

As AI continues to evolve, we can expect case law and legislation to eventually keep up, and some sort of consumer protections or regulations to protect against the unwanted side effects of AI, but for now, the only thing rarer than legal precedent in AI and automation is enforcement of those rules and regulations. So, while it might seem like AI has no downside, the truth is that for all its obvious benefits, there are some objectively attendant challenges associated with AI usage.

In addition to mediocre copy for B2B content marketing, here are some tough AI and automation-related recruiting realities that organizations need to be aware of before going all in on artificial intelligence for hiring and TA:

AI is not bias free: Like all algorithms, AI systems are ‘trained’ using existing databases or historic outcome or performance data, which can amplify or accelerate historical biases and create unintentionally unethical hiring practices if not carefully controlled. For instance, Amazon’s attempt at developing an AI recruiting tool ultimately ended because it very clearly and systematically discriminated against female candidates; similarly, it took an early Microsoft conversational AI experiment less than a day to turn user inputs into profanity-laced hate speech, which would have made Clippy way more interesting, but is an outcome that every hiring team should avoid.

Context Counts as Much as Content: AI is great at generating content, or performing tasks such as filtering massive talent pools into shortlists based on historical hiring data and natural language search producing more targeted results through object clustering and instance-based ontology (long story). It’s really bad, however, at understanding nuance, context or crucial factors like candidate personality, culture fit or learning agility - all of which are crucial for making informed, strategic hiring decisions and can be easily lost when replacing intuition with automation and real experience with “artificial intelligence.”

AI Actively Misses Passive Candidates: Not everyone recruiters look for when looking for candidates is actively looking for a job (especially for hard-to-fill experienced hires, where for most recruiters, it seems no one is ever on the market, and if they are, they’re damaged goods). And sure, AI is great at answering basic questions for inbound, active applicants at scale, or creating a ranked shortlist from profiles or resumes that are already in your system, but it still has significant shortcomings when it comes to attracting and converting passive candidates.

If you’re trying to find that purple squirrel, unicorn, needle in a haystack or whatever metaphor for hard-to-find talent you prefer, then high touch still beats high tech every single time. Automation is great, but personalization is still critical for engaging and converting highly skilled, highly experienced talent - something that inherently cannot be replicated at scale.

What’s Next for AI and Automation in Recruiting and Hiring?

Some researchers and academics have expressed concern about the lack of controls, oversight or regulation when it comes to developing AI products and solutions - which makes sense, since they’re largely on tenure and therefore can question the intentions behind the latest shameless capitalist cash grab (AI is in a place similar to dot coms in the early 2000s just before the bubble burst, but with shakier business models and go to market strategies that make Pets.com look genius by comparison).

These ivory tower types are advocating the need for external and neutral auditing of AI technologies, particularly when it comes to leveraging multi-agent software architecture to support algorithmic auditing throughout the talent acquisition process and across the hiring cycle.

Of course, how the AI sausage is actually made is largely proprietary, representing billions of dollars in potential revenue that would be at risk if they were open to third-party evaluation, but that doesn’t seem to be stopping AI providers from promising that their systems and algorithms are fair and bias free - and for now, we have to take their word for it. But let’s agree that it’s crucial for companies going forward to ensure that they have the tools and training to monitor and refine their algorithms themselves, instead of relying on a third party.

Ethical AI is the ultimate company value, and responsible use & equality of access need to be a part of your company culture moving forward, because recruiting, retention and referrals rely almost exclusively on building and maintaining trusted relationships, and those can only be maintained through responsible, transparent AI adoption and utilization going forward.

Finally, it’s important to remember that even the most sophisticated artificial intelligence or automation technologies can’t replicate the human judgment that’s ultimately the most critical influence on any hiring process. Therefore, AI should be designed to augment and extend existing TA resources and processes, not replace them - and that starts with the understanding that recruiters can’t be replaced by algorithms. That said, recruiters should be focused on building relationships, nurturing talent and aligning individual hiring efforts with bigger business initiatives and bottom-line outcomes.

Only by combining human judgment and expertise with AI's analytical capabilities and automation's efficiencies can organizations effectively compete for, and win, the talent they need to succeed - today, and tomorrow. It’s important that recruiters get with the system, but even more important that the system gets recruiters.

For now, that remains very much a work in progress. Like everything else in TA.

Author Bio

	Dr. Sanghamitra Bhattacharyya is the Director of Center of Excellence for Sustainable Development and a Professor, and Dr. Poornima Gupta is the Director of PGDM and a Professor at Great Lakes Institute of Management, Gurgaon, India.