Heads of Talent are using Al to drive efficiency within the recruiting org. Here is a guide to evaluating what's out there.

As a Talent Leader, your team will look to you to explain the risks and benefits of using generative AI in recruiting. 

AI is everywhere these days. Recent advances in AI technologies have led to some of the first publicly accessible “generative AI” tools, like Chat GPT, Bing Chat, and Google Bard. So it makes sense that AI (and the powerful things it can do) is top of mind for many people. 

With a few clicks, people can chat with these programs and begin seeing some of their huge potential. Many Talent Leaders are figuring out how these generative AI tools can help them at work. 

As a Talent Leader, your team will look to you to explain the risks and benefits of using generative AI in recruiting. We made this guide so you can be the expert at leadership meetings, provide guidance to others, and do a great job evaluating AI automation tools for your team.

Let’s look at how AI could impact the recruiting industry in the short and long term. 

What really is AI? 

AI, or artificial intelligence, is the ability of machines to do computing so complex that it seems like it’s in the realm of human intelligence. AI has been developed since the birth of computing. But recently, it has seen a boom in public awareness and popularity with advancements in computing power and, thus, greater availability to all. 

There are many types of AI, not just the generative AI that has been making headlines recently. Machine learning, neural networks, vector databases, and more are all considered types of AI. 

AI programs can process vast amounts of data and make predictions, classifications, and decisions without explicit programming. These capabilities have been integrated into many existing computing platforms and systems, enabling them to perform tasks that were previously very challenging, if not impossible, using traditional computing methods. 

For instance, Facebook uses machine learning to serve you content you may be interested in based on your browsing history. 

AI, Generative AI, LLMs - Looking past the hype

Generative AI is a set of AI tools that can generate new content, be it essays, art, poetry, analysis, or more. 

Generative AI tools use Large Language Models (LLMs) as the foundation for what they create. So, having a very large LLM matters for the quality and accuracy of what a generative AI tool will create.

A large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other forms of content based on knowledge gained from massive datasets

Large language models are among the most successful applications of transformer models. They aren’t just for teaching AI human languages, but for understanding proteins, writing software code, and much, much more.

Enterprise-grade foundational models are what power Covey’s generative AI application layer.

What’s often misunderstood about generative AI?

One big area of misunderstanding is that generative AI = ChatGPT. Generative AI is more than ChatGPT.

ChatGPT is a program that uses generative AI, but it’s not the only one. 

Generative AI is a broader concept with a wide variety of potential applications. 

Some current offerings that use generative AI include Google Bard, DALL-E, and DeepMind. 

Another area of misunderstanding is that generative AI is its computing platform. AI is not an independent computing platform; rather, it’s a powerful set of tools and techniques to be applied across various platforms.

Pre-generative AI recruiting tools: What solutions already exist? 

AI-based solutions are not brand new to the world of recruiting. Before Chat GPT and other LLMs, companies incorporated insights and tools from machine learning and AI models. AI solutions in recruiting were typically focused on completing specific tasks within the recruitment process. 

These are some of the specific tasks that AI has been used for in recruiting that you’d have encountered: 

• Resume Screening 

  Resume screening software helps automate the initial screening process by analyzing resumes through keywords that identify qualified candidates based on education, skills, experience, and the industry or position.

  
  

• Applicant Tracking Systems (ATS) 

  ATS with AI capabilities streamline the candidate management process, tracking candidates' progress and providing analytics that recruiting teams rely upon to influence critical decisions

  
  

• Candidate Sourcing 

  AI-driven tools scour various job boards, databases, and professional networks for the best candidates, typically using keyword matching.
  
  

• Scheduling interviews

  AI solutions facilitate interview scheduling by finding suitable time slots based on the availability of both job seekers and interviewers.
  
  

• Video Interviewing and Analysis

  Recruiters use AI tools to analyze video interviews, assess candidates' responses and non-verbal cues to surface insights 

  
  

• Candidate Engagement

  Automated emails and other communications are used to engage with job applicants, answer their questions, and keep them informed about the hiring process.

  
  

• Predictive Analytics

  AI solutions can utilize historical data to predict candidate success and identify the best candidates for specific roles.

  
  

• Diversity and Inclusion

  AI-driven tools help address bias in job descriptions and candidate evaluation, promoting diversity and inclusion in the hiring process.

AI has improved recruiting efficiency, reduced time-to-hire, enhanced candidate quality, and provided a better candidate experience. 

However, these tools do have some fundamental limitations and in the next section we’ll explain why.

Limitations of pre-generative AI recruiting tools

Pre-generative AI recruiting tools typically rely upon predictive analytics and machine learning. That means they can learn and make predictions based on what happened, and the data fed to it. 

For example, LinkedIn uses AI to analyze your profile and search history to provide personalized job posting recommendations matching your experience and interests.

Many sourcing platforms use keywords to search for software engineers on their database.

Such systems get smarter as they access more data.

However, if you want a truly powerful model to identify a software engineer for instance, you would want to look at all the engineers in the world, and that’s what cutting edge post generative AI recruiting tools can do. 

Post generative AI tools can amass billions of profiles, integrate hundreds of other data sources, and provide a far more robust and reliable prediction and matches than before.

Let’s dive into some of the limitations of pre-generative AI and how generative AI could address them. 

Limitations: 

Pre-generative AI recruiting tools aren’t great at predictions. These tools can only make inferences and “learn” based on prior activity and are very limited by the amount of data they can access. Thus, they can only do more simple tasks, like finding a candidate with a specific skill.

How generative AI addresses these limitations: 

Generative AI tools use deep neural networks to provide better predictions and generate content. These tools can analyze vast amounts of data to give recruiters better insights, better candidates and better content. 

Generative AI in recruiting: What can it do for you? 

Chat GPT and other similar AI tools that use large language models represent a significant advancement in AI technology. These tools can generate content, allowing people to engage in “conversation” with the tools and ask them to produce essays, poems, summaries, and more.

Society is just beginning to figure out the many applications of generative AI. In recruiting specifically, there are already some great ways to have generative AI tools help with work tasks, particularly in creating compelling written content and intelligently sourcing great candidates.

The huge potential of generative AI in sourcing

Current sourcing technology typically looks at where you worked, past job titles, and other readily available data in an online profile. That sounds like it would be all you need to source great candidates, right? 

But, as experienced recruiters know, these systems can have limitations. They only look for specific keywords that cause qualified candidates to slip through the cracks because these candidates need to fit the exact parameters being used. Or they may miss synonyms or similar job titles, leading you to lose out on finding qualified candidates.

But new AI technologies have the potential to improve that process greatly. With LLMs, great AI recruiting software can pick out the skills needed for a specific role based on data from companies. Then, they can search for candidates with the right skills and qualifications, all without a recruiter having to decide on keywords or manually search themselves.

How Rippling uses Covey to scale their team and lower cost per hire

Imagine you are screening inbound candidates coming in via your ATS. You are looking for someone who has worked at a Big 4 accounting firm for at least 3 years and then moved on to a venture-backed startup. At the same time, this person has to have proof of career growth. These are distinct attributes that pre-generative AI technologies can’t identify, but you can now filter for using LLM AI technology.

Covey Scout is a smart AI sourcing assistant that many companies rely upon to improve their sourcing and recruiting. With natural language processing, recruiters simply need to type in a chatgpt like text box to describe what they seek.

Using Covey, Rippling scaled their engineering team and ramped up hiring at approximately 80% lower cost per hire, all without having to add recruiting resources.

The Rippling team uses Covey to:

• Enable their recruiting team to source and hire top talent quickly

• Find candidates with specific characteristics, such as early at YC software companies + top venture-backed startups in FinTech between Series A and C + companies with strong engineering cultures.

• Ramp up their pipeline with candidates that are on-point with requirements without spending hours manually sifting through each profile

• Enhance employer brand with consistent messaging

“Covey Scout evaluates and finds talent for you. It’s easy to use, gets us the exact candidates we want to talk to, and if you have a change in requirements you just need to update the bot, and then sit back and wait for these candidates to respond to you in your inbox.” 

- Nevin Cook, Staff Recruiter at Rippling

The possible pitfalls of generative AI + how to handle them

Despite the many benefits of AI in talent acquisition, it's crucial to acknowledge and address its potential challenges. These include AI hallucinations, algorithmic bias, data privacy, and compliance concerns.

AI hallucinations —> This is the term for when generative AI creates something false. 

Be vigilant in monitoring the output of generative AI to detect and correct any false or misleading information. Regularly review and assess the performance of the AI system to identify and address any potential hallucinations.

How to handle it? Hallucinations are a key reason why you need a human at the helm of your AI. For example, Covey Scout is designed to take strategic input from a recruiter and then execute on it by evaluating candidates exactly the way it has been prompted to. It is not meant to replace recruiters but rather be an assistant to them, with recruiters retaining control over hiring decisions every step of the way.

Algorithm bias —> This occurs if the data used to train the AI model contain biases (e.g., gender, racial, age-related). The AI can perpetuate or even amplify these biases in what it produces.

Provide transparency in the decision-making process of the AI system. Ensure that the algorithms used are transparent and understandable, allowing for scrutiny and accountability.

How to handle it? Create systems to check datasets for bias, and be ready to take quick action to address biased content if it comes up. 

Data privacy —> An AI system must comply with data protection laws like GDPR and CCPA. Whenever a system has personal data, there’s a risk of data breaches.

Consider ethics when developing and implementing AI systems. Ensure that the AI system respects human values, adheres to ethical guidelines, and does not harm individuals or discriminate against them.

How to handle it? Choose an AI tool with strong data protections in place, and ensure your team is trained on best practices for using any tools you have.

Compliance concerns —> There's potential for lawsuits if an AI system discriminates against candidates based on protected categories.

How to handle it? Regularly monitor and assess the AI system's performance to ensure it is not discriminating against candidates based on protected categories. Implement a system for reporting and addressing any complaints or concerns regarding discrimination. Provide ongoing training and education to your team regarding compliance and ethical considerations when using AI in talent acquisition.

Covey stays current with legal requirements and regulations related to AI and talent acquisition to ensure compliance. It goes through regular audits by an independent third party auditor.

How can our recruiting team use generative AI right now?

• Generative AI can make a far-reaching impact on candidate sourcing, outreach message generation, and screening (hundreds and thousands of) inbound applications from candidates.

• It can also create business value by automating and scaling the process of talent acquisition by freeing up time spent on mundane, repetitive tasks, and allowing recruiting teams to focus on more strategic and relationship-building work.

• To determine the best use case for your talent acquisition team, identify the bottlenecks in your recruiting process taking up the most time and resources.

AI has been playing an increasingly significant role in recruiting and talent acquisition, and it is expected to continue shaping the future of HR practices. As technology advances, AI-powered tools and systems are becoming more sophisticated, enabling recruiters and organizations to streamline the hiring process, improve candidate assessment, and enhance overall efficiency. 

Here are some potential ways AI might impact the future of talent acquisition:

• Candidate Sourcing 

  As discussed above, sourcing is an area where generative AI could provide a lot of improvements to existing technologies. It could offer a way to better find a diverse array of qualified candidates instead of relying upon binary keyword searches that may not turn up the great pool of candidates you need.

  
  

• Outreach Message Generation

  Again, this is an area where existing technologies have room for improvement. Many tools can send pre-written messages to candidates, but that does not save you much time when you have to write the message yourself or substantially edit what’s there. Generative AI could revolutionize this, providing a quick way to write custom, high-quality messages to candidates at all stages of the recruiting process.

  
  

• Candidate Screening

  AI can help screen candidates from various platforms and databases. It can analyze resumes, cover letters, and online profiles to match candidates with specific job requirements. This saves recruiters a significant amount of time, allowing them to focus on building relationships and engaging with potential candidates instead of filtering through pages and pages of profiles. 

  
  

• Enhanced Candidate Experience

  AI-powered chatbots and virtual assistants can interact with candidates, answering their questions, providing feedback, and guiding them through the application process. These high-quality virtual assistants can deliver a more personalized experience than the robotic and frustrating chatbots people may have encountered in the past.

  
  

• Bias Reduction

  One of the most promising aspects of AI in recruiting, and HR as a whole, is its potential to minimize bias in the hiring process. Generative AI has the potential to change the way talent is understood fundamentally. Skills can be inferred by AI platforms and expressed with both structured data and in natural language. Previously, skills were input by users or managers and consisted only of structured data, which is limiting and susceptible to personal bias.

As you can see, there are many potential use of AI in recruiting, and we are just scratching the surface of what these tools are capable of currently. AI is entering a new and exciting phase that will change how we work. As long as recruiters stay up-to-date with current tools and trends, they will be in the driver’s seat as these changes occur

How to approach AI usage at your recruiting organization

• Take a step back and consider the implications from a specific point of view.

• If you have a specific need, it might be more beneficial to use a tool that is tailored to that need.

• Instead of relying on a broad tool that accepts input from everywhere, consider the advantages of using a specialized tool.

• AI can augment the recruitment process for recruiters responsible for those processes.

• Rather than attempting to broadly replace recruiters' efforts with AI, use AI as a supportive tool to enhance their work, taking over time-consuming workflows.

  
  

5 things to look out for when evaluating AI recruiting tools

AI recruiting tools are only as good as the people, prompts, and data driving it. To ensure the best results, recruiters should:

• Use tools that allow input of your own recruiting strategy

• Be able to train the model on your own requirements consistently so it becomes more refined

• Use tools that can incorporate your company’s brand and voice

• Be able to mitigate unconscious bias 

• Use tools with an extensive enough dataset

You can’t fine-tune general-use AI tools like Chatgpt on your company’s brand, style, and terminology guidelines, leading to inconsistent and inaccurate output that undermines brand and industry compliance.

Consider AI a tool to scale the work of your company’s most strategic and relational minds so your business can grow. AI isn’t a cost-cutting measure to replace the recruiters on your team. The best results are going to come from smart strategists driving AI.

When you equip recruiters with AI, you create the conditions where everyone gets a chance to 10X their output and productivity by leaving the mundane and repetitive pieces in the recruiting process to AI. 

This increases work fulfillment as recruiters also get to do more of relationship building and candidate experience work that often gets overshadowed by repetitive pattern matching work, such as scanning resumes—something that AI is equipped to do especially with the advent of LLMs and neural networks.

AppLovin turned to Covey to make their candidate sourcing process more efficient by equipping recruiters with an AI bot to source and evaluate candidates the way they would.

“We had previously relied on specialized agencies to fill technical roles at 20% of its salary, and we never got the speed, cost-savings and accuracy that new generation AI tools like Covey provided,” says Anand Bheeman, VP Talent Acquisition at AppLovin.