How AI is Reshaping the Future of Reference Checking

A thorough reference checking process is critical to the integrity and quality of the workforce at any modern organization. But the task of locating, vetting, and onboarding top talent is an ever-escalating challenge to global hiring teams operating in today’s competitive market. Traditional methods just aren’t as effective as they used to be, a reality which is driving companies to level up their reference checking tactics with technological solutions.

Why Traditional Reference Checks Fall Short

Smaller businesses may continue to find that traditional reference checking methods meet their needs. But for enterprise-level companies, or start-ups hitting their growth stride, manual reference checking just isn’t going to cut it. 

Relying on phone calls or email communication to reach references can cause expensive bottle necks as recruiters log countless hours locating references, playing phone tag, or waiting for surveys to be returned. For companies making large-scale hiring decisions on a regular basis, it is also easy to lose track of references, and by extension valuable insight into your candidates. 

But traditional reference checks offer only limited candidate insights in any case. Difficult to verify, subject to human error and bias, the growing demand for faster, data-driven hiring decisions necessitates a modern approach to reference checks that will deliver reliable, actionable, and deeply insightful candidate information every time. That’s why more companies are adopting AI as a strategic tool in their greater hiring strategy.

What Are AI Reference Checks?

Implementing AI within your reference checking process works to automate a number of individual tasks, including outreach, candidate evaluation, and even reference survey design to facilitate a more efficient, more effective outcome. 

An AI tool can compose a questionnaire to deliver to references, based on which questions historically deliver impactful candidate insights, then send that survey to the listed reference to be filled out. Once the response makes it back, AI can read the survey, evaluate the candidate, and deliver that evaluation to a human at the other end to make the next decision.

AI transforms reference data into actionable insights for your team by reading between the lines of more complex survey responses, delivering only the key points you need to make an informed decision. AI can also extract this same information from multiple reference sources, defining the throughline in all reference responses so you get the clearest picture of your candidate’s true quality.

The Benefits of AI in Reference Checking

In addition to faster, more efficient insights that leave your recruiters free to tackle other tasks, using AI for reference checking comes with a number of key benefits.

• AI eliminates bias from reference check surveys, reading reference responses based on predetermined criteria that you set. Objective evaluation ensures more accurate and consistent insight into the actual merit of your candidate, leading to stronger hiring decisions.
  
  
• AI yields faster results, as recruiters no longer need to play phone-tag with references or send out reminder emails every few days if they haven’t heard back from a reference. AI can make the initial outreach, send regular reminders until the survey is completed, and deliver insights to your inbox the moment that survey is returned, allowing for more efficient recruiting timelines.
  
  
• AI analyzes reference sentiment and performance metrics in real-time, giving you a clearer look into your candidate’s secondary qualities such as work ethic and cultural fit. These insights will allow you to make longer-lasting hiring decisions for your company.

How AI Detects Hiring Trends

Using returned reference surveys, AI can help your team to stay on top of hiring trends and make better decisions for the oncoming year.

AI uses Natural Language Processing (NLP) to understand human language on returned surveys. Advanced NLPs allow AI to comprehend slang, colloquialisms, and other intricacies of written communication, which in turn enables them to accurately assess reference feedback and deliver actionable insights to your desk. This capability means AI can track trends and find commonalities between references, regardless of who the response is coming from or their particular writing style. 

Using aggregated reference data, as well as past trends relating to effective hiring decisions, AI can deliver predictive insights that tell you which qualities to look for in your candidates for a more impactful hire. 

Ethical Considerations in AI Reference Checking

When adopting or updating your AI reference checking tool kit, it is important to acknowledge and accommodate its limitations.

AI is not infallible: the insights it delivers can help you to make decisions faster and with greater ease, but you should take care to avoid letting AI make the decisions for you. Some AI reference checking tools may make hiring recommendations based on the analyses of reference feedback, but a human professional should make their choice based on the insights, and not the final recommendation of the machine. 

One reason for this is that AI, if over-relied upon, can create biases unique to an algorithmic analysis model. Mitigating this possibility requires hiring professionals to monitor this programming to ensure it is not replicating discriminatory practices. This can also be circumvented by taking care in giving your AI tool highly specific parameters to work within that disallow biases and focus solely on the quality of the candidate’s work. 

Critically, the way candidate data is used must also meet compliance laws and respect the privacy and autonomy of the individual. 

It is important to evaluate your AI programming regularly to ensure fairness and objectivity – doing so supports stronger hiring decisions within your company.

The Future of AI in Reference Checks

As organizations continue to use AI to support their reference checking processes, new possibilities for AI application will continue to emerge as part of broader talent intelligence strategies. 

The more insight AI can generate from reference feedback, the more it will be able to empower better and longer-lasting hiring decisions for your company. Over time, hiring trend predictions will become increasingly accurate, allowing you to close staffing gaps before they emerge. New insights will also demonstrate which questions are the most important to ask on reference surveys, allowing you to craft reference checks that lead you to the most advantageous hires for your team.

In addition to communicating with references, emerging trends in AI have intelligent tools handling other aspects of the hiring and recruiting process, including fielding candidate inquiries, and creating personalized update and outreach messages for candidates and new hires. And integration capabilities with external hiring platforms mean AI can help you handle end-to-end recruiting processes, from initial outreach, to onboarding paperwork.

As we continue to discover new applications for AI in hiring and recruiting, human oversight remains key to ensure that its use remains ethical, and effective.

Why AI Reference Checks Are the Next Step in Smarter Hiring

If your company is just getting started, AI is easy to implement as an automated administrative assistant that can handle reference outreach and reception of feedback. Once you’re there, your AI tool can start taking on larger assignments according to your needs and the capability of the program. 

Consider starting with an assessment of your hiring process now, focusing on the areas AI could help you to gain greater efficiency, efficacy, and a data-driven strategy. Next, look for tools that are purpose-built to help you address those needs. 

Crosschq is an AI-powered hiring intelligence platform that helps you make the right hiring decision, every time. Sign up for a free demo today.