How AI Can Help and Hinder the Recruitment Process

We’re no longer talking about emerging trends or the future of AI in recruitment. AI is here.    

Did you know 93% of Fortune 500 HR teams have started using AI tools to streamline recruitment tasks? Yet fewer than 1% say they are fully AI-ready. At best, this gap means most HR teams aren’t maximizing value from AI. At worst, it’s like driving a car without a license: increasing risk and the chance of costly mistakes.

This shift ignited in late 2022, when OpenAI’s ChatGPT became a household name out of nowhere. While that initial version was rough around the edges, especially compared to today’s suite of GenAI tools, it showed immediate promise to solve several longstanding recruitment challenges.

But even today, beneath the surface of this rapid adoption lies widespread inconsistency. Many organizations have tested the waters, but this will only get you so far without a cohesive, organization-wide strategy.     

At the other end of the spectrum, some organizations have banned AI altogether, citing security, legal, or ethical concerns. This is because, despite the buzz, we are still in the early adoption stages. So much so, that fewer than 1% of leaders consider their organizations fully AI-ready. This highlights a significant gap between perception and reality.

So what’s the next step for tech-savvy recruiters and HR leaders who don’t want to be left behind? It’s shifting from reactive adoption to intentional, strategic use. Organizations are either using AI by default or by design. This means that you can passively follow where the market leads, or you can actively shape your hiring strategy to get the most from new technology. 

You should see AI as both an efficiency boost and an extra set of hands: use it to manage high volumes, keep candidates warm and understand where ROI and fairness are leaking. Start with your real pain points, measure what changes, and keep human judgement firmly in the loop.”

AI reflects the biases in its training data, unless those biases are actively managed.

This is one of the most dangerous assumptions. While AI can process more data, it is not inherently neutral. AI ultimately relies on human input. If the input data includes bias, the algorithm will learn and base decisions on that. A high-profile example is Amazon’s internal resume-screening AI, which was trained on ten years of historical hiring data. Since the company had predominantly hired men for technical roles, the AI learned to downgrade resumes that included the word “women”, even in phrases like “women’s chess club captain”.

The project was eventually scrapped after it became clear that the tool was discriminating against female candidates. While this happened over ten years ago, it still serves as a cautionary tale that reminds us human bias can easily be transferred into software, unless measures like diverse training datasets and regular audits are built into the system.

AI can support decision-making, but it can’t replace it.

While AI can help shortlist candidates or match skills to roles, it lacks the nuance to assess culture fit, motivation and interpersonal dynamics. These are the factors that often determine long-term success, best evaluated by people, guided by science. That’s why the most forward-thinking organizations use AI to augment a science-led hiring process, not override it. Relying solely on “automated fairness” or chasing “total efficiency” risks undermining the very outcomes you’re aiming to improve.

How top hiring teams are using AI in recruitment – the Good, the Bad, and the Blended.

·         Job description generation Writing job ads at scale can overwhelm teams. Generative AI can quickly create consistent, brand-aligned postings. This allows recruiters to focus on rectifying any biased language that might discourage strong candidates and ensure the description aligns with their goals. 5 Many recruitment teams see real, measurable value from AI, especially when it’s applied thoughtfully.

·          Interview Support:  AI is saving time in the interview process, particularly around scheduling and documentation. Intelligent scheduling tools like Calendly automatically manage availability and coordinate interview logistics, eliminating the back-and-forth that usually eats into recruiters’ time.

Ø  Recording and AI note taking tools transcribe conversations, highlight key moments, and generate structured summaries.

Ø  Recruiters THEN SHOULD check the AI summary for key takeaways for each interview question, providing a quick, easy, and accurate reference when comparing candidates..

·         Workflow Automation Beyond these tasks, AI is also beginning to automate broader interview workflows. For instance, platforms can automatically: Trigger post-interview feedback forms once a session concludes • Generate preliminary candidate scorecards based on structured input • Flag next steps such as scheduling another round or declining a candidate based on scoring logic or hiring criteria - are the good ones getting away? 

Ø  This type of workflow automation reduces admin overhead, ensures timely follow- up, and frees recruiters to focus on evaluation quality and candidate experience.

Ø  A great candidate and recruiter experience comes from having frictionless processes that don’t compromise on rigor.

HR Insights by Ellie will design blended solutions to integrate seamlessly with your current platforms.  If you are in need of updates or enhancements, or don’t know where to start… I can provide options for ATS platforms based on experience, recommendations, and statistics to best support your workflow.

AI should actively support, not substitute, insight backed by clear evidence. In practice, you firstly need objective, job‑relevant indicators of future success, then AI helps you act on those indicators faster, drafting role profiles, keeping candidates warm, sequencing next steps and highlighting drop‑offs.

As the number of data breaches increases globally, HR and recruitment platforms have become attractive targets for cyberattacks. At the same time, internal misuse can also occur when organizations lack clear access controls, anonymization standards, or audit trails.

With regulations like GDPR and CCPA setting strict requirements around consent, transparency, and data purpose, mishandling candidate data can lead to significant legal and financial consequences. Additionally, many AI systems function as “black boxes,” providing results without explaining how they were reached. This lack of transparency creates problems, especially in hiring.

Candidates and regulators demand accountability. If an applicant is rejected based on an AI recommendation, employers must be able to explain the legitimate, job-related reason behind that decision. Without it, they risk entering ethically and legally uncertain territory.

As best practice, use AI systems that offer interpretable outputs or simplified reasoning (such as “Candidate’s assessment score was below the required threshold for X competency”). Having a human involved in the decision-making process ensures oversight, providing someone who can question or override AI recommendations when necessary.   Keeping the “HUMAN” in Human Resources. 

HR Insights by Ellie can help you:

·         Manage the risks of AI while capturing its most valuable opportunities. 

·         See what AI can and cannot do in recruitment, based on real-world scenarios. 

·         Explore how leading teams are applying AI to power their recruitment processes

·         How to lead a blended HI with AI adoption in YOUR recruitment team 

How balanced is your AI and HI?

For your free consultation which includes a personalized AI – Assessment, send me a message, a text, an email,  or give me a call to connect.