AI Recruiting: A Practical Guide 2024
AI enhances efficiency but requires human oversight: AI can streamline recruiting by automating tasks like candidate sourcing and resume screening, but maintaining human involvement is crucial for building relationships and ensuring cultural fit.
AI can reduce bias, but it’s not foolproof: While AI has the potential to reduce unconscious bias in hiring, its effectiveness depends on the quality of the data it's trained on. If historical data is biased, AI could perpetuate those biases.
AI shifts the role of recruiters: Rather than replacing recruiters, AI changes their focus from administrative tasks to more strategic activities, such as engaging candidates and refining hiring strategies to account for diversity and inclusion (DEI).
AI recruiting is transforming how companies hire talent. AI recruiting solution adoption is transforming the traditional hiring process by improving candidate matching, reducing time to hire, streamlining processes, reducing administrative burden, and improving the candidate experience.
With every technological development, however, there are also challenges to consider, including maintaining the human touch and ensuring candidate quality. This guide explores the practical applications of AI in recruiting, the benefits, challenges, and how it's shaping the future of hiring.
What is AI for Recruiting?
AI for recruiting refers to the use of artificial intelligence technologies to automate and optimize various aspects of the hiring process.
This includes everything from sourcing candidates and screening resumes to conducting initial assessments and scheduling interviews.
AI recruiting tools can analyze vast amounts of data and apply algorithms to identify the best candidates for a job, making the process more efficient and data-driven.
AI tools are good at handling repetitive, time-consuming tasks that, at least in theory, prevent recruiters from focusing on more strategic activities, such as engaging with top candidates and building stronger relationships.
Difference Between AI and Machine Learning
AI (Artificial Intelligence) and machine learning are terms which are often used interchangeably but have distinct differences.
AI refers to machines designed to mimic human intelligence, performing tasks like decision-making, problem-solving, and learning from data. Machine learning, a subset of AI, involves training algorithms to improve their performance over time based on data.
In recruiting, AI might automate candidate screening, while machine learning could refine those results over time, improving the system’s accuracy based on past hiring decisions.
Benefits of Recruiting with AI
As you can probably guess, there’s a fair amount of benefits in adopting AI into your recruiting processes. As the technology evolves, recruiters become more comfortable with it, and recruiting software companies feature more AI tools, it’s likely we see AI playing an even bigger role in finding the right people, with the right skills, and helping companies create career paths that are tailored to those skills.
But for now, here are some of the most obvious benefits to implementing AI into your recruiting efforts.
Faster candidate screening
One of the biggest advantages of AI in recruiting is the speed at which it can screen candidates. Its use in applicant tracking systems (ATS) is one of the more well-known AI developments for recruiting.
Traditional methods require recruiters to manually sift through hundreds or thousands of resumes. AI-powered systems, however, can analyze resumes and applications in a fraction of the time.
Recent data from the Society for Human Resource Management (SHRM) shows that 25% of organizations are planning to implement an AI driven ATS by 2027, as it’s estimated AI can reduce the time spent screening resumes by up to 75%.
Better candidate experience
By automating communication and providing timely updates using chatbots, AI can answer candidate questions, schedule interviews, and send reminders, creating a more seamless and responsive experience.
This kind of real-time interaction helps candidates feel engaged and valued throughout the recruiting journey and ensures that people don’t fall through the cracks due to a lack of updates on the status of their application.
Improved candidate sourcing
Advanced candidate sourcing tools use algorithms to identify qualified candidates from multiple sources, including job boards, social media platforms, and internal databases, AI-powered talent acquisition systems can analyze data from a candidate's profile, skills, and previous experience to determine if they’re a good fit for a position. This helps with sourcing as it expands the reach of recruiters and increases the pool of potential candidates.
Sourcing, like screening, is a time-intensive process, but with the assistance of AI, that is changing. Almost 90% of the respondents to Workable’s 2024 AI in Hiring survey said that the technology has sped up their hiring capabilities and two thirds say they intend to increase its use in the next five years.
Sourcing is one of the areas that the earliest developers of AI-driven tools were looking to target. But, to see which tools are truly impactful on the quality of hire, we have to understand how the process is done manually and what AI can do to match it.
When you’re sourcing candidates you look at things like:
- Job titles—same or similar ones (e.g. sales manager or account executives or business development can potentially mean the same job and experience).
- Companies—similar to yours or ones you may have picked up have similar processes, products, and services.
- Keywords—skill sets, technical qualifications, education.
Natural language processing algorithms have been improving more and more, but it’s still not 100% and it may miss out on candidates you would have made part of your talent pool.
Reduction of admin tasks
You might be as sick of hearing the line as I am that AI will free us from any shade of administrative burden to complete higher order tasks. Tasks that seemingly no one has clearly defined outside of calling them “strategic”.
With that said, it is true that tasks such as scheduling interviews, sending out paperwork, and managing candidate data can be taken off recruiters' plates with AI. By automating these processes, recruiters save time and can focus on things you don’t want to lose the human touch from, such as evaluating cultural fit or developing recruiting strategies that account for DEI.
Challenges of Recruiting with AI
The thing about AI is that it raises some concerns among some folks, primarily related to ethical and compliance issues with its use of data.
Among the more than 3,000 respondent's to the Workable survey, 37% said that compromised employee and candidate data was a major concern, while more than 30% lacked confidence in their ability to remain compliant with legal standards using the technology.
Loss of human touch
As I mentioned before, one of the big risks of using AI in recruiting is losing the human element. While AI can handle repetitive tasks, human interactions are still critical for building relationships, understanding a candidate's motivations, and assessing cultural fit. Over-reliance on AI could lead to candidates feeling like they’re being treated as data points rather than individuals.
Candidate quality
AI systems are only as good as the data they are trained on, and much of the historical data that many organizations use is flawed or will end up perpetuating a cycle of hiring that looks familiar to a fault.
If the data used to develop algorithms is biased or incomplete, the system may produce flawed results, such as recommending unqualified candidates or filtering out potentially excellent hires.
To drive candidate quality you need the AI to do a few things better, including:
- Recognize alternative job titles
- Explain its recommendations
- Provide filters so that you can manually set talent search parameters
- Fully sourced data so if you contact a candidate you know where you got their information from (especially in GDPR locations).
- The ability to modify and control automated outreach and scheduling.
Price barriers
As more technology incorporates AI, this will change, but right now, implementing AI recruiting solutions can be costly, especially for small or mid-sized companies.
Many advanced AI platforms require significant investment, both in terms of the type of recruiting software itself and the resources needed to manage and maintain it. As a result, smaller businesses may find the pricing of recruiting software with AI to be prohibitive.
However, there are still some recruiting platforms available for small businesses that do include advanced features, so don't stress.
How AI is Changing Recruiting
There’s a lot of speculation around how artificial intelligence (AI) will change our jobs, our lives, and everything in between and things are changing fast. Just last year, a study was published by Computers in Human Behavior Reports that showed only 38% of respondents to their survey though AI was useful or very useful in recruiting, while 32% were neutral and 31% thought they were useless.
A year later, many of those sentiments will have undoubtedly shifted thanks to developments in the areas where things are changing pretty fast. Here are some to note.
Automation during the application process
Recruitment process automation is a favorite topic of those who enjoy HR automation conversation. Chatbots are great for automating customer service tasks, so why not use them to help candidates navigate your application process or direct them to information about your company, jobs, benefits, or interview process?
They can help improve your employer brand by making more information accessible and not stuck behind the first interview wall.
It can also be a more interactive way of asking some pre-screening questions to screen out candidates who do not pass the mark on some very specific requirements e.g. a candidate who needs visa sponsorship but who you don’t have a license for right now.
A lot of these questions can seem aggressive when positioned in a questionnaire format on one page, but interactivity has been shown to improve user experience in UI and candidate experience is no exception.
There are standalone bots like eightfold and some ATS like SmartRecruiters and iCIMS are rolling out chatbots to help with anything from job matching, introducing hiring managers to candidates, and streamlining the application process via front-loading the questions.
Personalization
AI can deliver personalized experiences to candidates by analyzing their preferences and behavior. For instance, AI-powered systems have the ability to recommend job openings tailored to a candidate’s skills and career goals, making the job search experience more engaging and relevant.
This personalization increases the likelihood of not only attracting the right talent, but reshaping the employee experience and our approach to learning and development.
Data analysis
AI recruiting tools excel at data analysis, providing recruiters with actionable insights. Whether you’re trying to track hiring trends, analyze candidate performance, or predict which candidates are most likely to succeed in a given role, advanced data analysis is a capability that will help you streamline your decision making process.
Data-driven insights can also help you refine your recruiting strategies to reach new candidates and increase the efficiency of your hiring teams.
Interviewing
The interview process can be complex as you try to create personalized experiences and develop relationships. All that before writing an interview question or analyzing the response. AI can help a bit here, but it’s complicated.
You can use some basic generative AI tools for crafting questions based on job specifications and a candidate's CV. It's helpful to create a standard set of questions for all candidates, allowing for easy comparison.
For this, tools like ChatGPT4 are useful as they can search online for new question ideas. As an experienced recruiter, I treat these as a baseline and modify them as needed since the suggestions are often basic.
After establishing a core set, focus on candidate-specific questions. Resume screening software like Kickresume and pdf.ai can analyze job descriptions and resumes to suggest tailored questions, though it's essential to review their suggestions yourself to ensure nothing is overlooked.
Generative AI tools for text analysis, including interview notes, can be difficult to integrate into recruitment workflows due to the need for multiple tools outside of an ATS. However, two products, Metaview and Screenloop, offer seamless AI-powered interview notes analysis specifically designed for recruitment.
Unlike general sentiment analysis tools, these integrate directly with ATS platforms, automating the process without the need for manual input. Screenloop, for example, highlights key moments where a candidate's experience aligns with the job description, making it particularly useful for recruiters.
Bias detection
AI can help reduce bias in the recruitment process by eliminating human prejudices that may affect hiring decisions. Some AI systems are designed to focus purely on skills, experience, and qualifications, minimizing bias related to gender, ethnicity, or age.
However, it’s important to note that AI can inadvertently perpetuate biases if the data it’s trained on contains inherent biases. Ensuring that AI systems are properly calibrated and regularly audited is key to reducing this risk.
Being able to query a specific decision is so important as human biases are likely to be baked into the algorithm.
Often AI has been touted as a weapon against unconscious bias, but the jury is still out on whether the use of AI will lead to more equitable outcomes.
DEI Reality
Many AI tools in the recruitment software market are increasingly being touted as the answer to any Diversity, Equity, and Inclusion (DEI) needs your org has.
The theory goes that by focusing on objective data points, AI can help identify diverse talent that might otherwise be overlooked.
There are concerns that AI can sometimes amplify bias if not carefully designed and monitored but the more pressing concern is simple. If you’re working with historical data to feed the AI’s decision making mechanisms and your past hiring habits were filled with bias, why would you expect AI to give you a different result?
At the end of the day, these machines are a reflection of humanity. The work of DEI and incorporating it into hiring decisions is, quite simply, still a very human task. AI alone isn’t going get DEI initiatives off the ground.
Best AI Recruiting Software
If you’re in the market for a new AI driven recruiting software, sifting through the hype and extravagant claims can be time consuming. We’ve done a lot of the leg work for you though in our review of these tools.
Changing the Role of Recruiters
There’s a lot of speculation around how artificial intelligence (AI) will change our jobs, our lives, and everything in between.
AI is useful for tasks like creating job descriptions or LinkedIn outreach messages, but it’s not yet something we can unleash unsupervised.
Recruiters aren’t out of a job anytime soon and we cannot outsource our entire recruitment process to the current wave of machine learning models.
While writing this article I made sure to consult with a few ML/AI engineers to ensure I’m not stating anything erroneous and one of them said something I’d like to leave you with:
“Many new products and technologies suffer from evangelists that fall into the trap of having discovered a cool new hammer, so suddenly everything they see is a nail. It is the same now with AI. It's a cool hammer.” - Dr Mihail Morosan, Deployed AI Engineer.
Make sure you are there to guide the hammer and see if the problem truly is a nail!
