It is that time again. The start of a new year brings perfunctory prognostications from pundits everywhere. So, without further ado, here is my take on the trends that are impacting the world of predictive hiring tools and talent assessment.
The development of platforms that cover the core areas of HR, and the integration of modules that include point-solution-like functionality within these areas, is far from new. But with each passing year, it seems that the reach of these platforms and the things they are purported to do is taken to a new level.
Josh Bersin’s latest outlook on HR tech trends provides an excellent overview of the continued evolution of platforms. These days Employee Experience Platforms, Talent Intelligence Platforms, Assessment Experience Platforms, and Employee Listening Platforms are creating entirely new categories. Platform providers are offering more and more features through marketplaces of integrated vendors and/or hoovering up point solution providers via acquisition.
If you are an internal buyer of HR technology, the chances are these days that you are going to be procuring a platform (or some portion of it). Talent assessment and predictive hiring functionalities are no exception. Infusing people platforms with the ability to measure individual differences, to help align individuals with work that they are suited and motivated to pursue is a very good thing. Expect to see more of this. In fact, all the remaining trends covered in this article are all tied to the continued evolution of platforms. If you don’t believe me, read on!
The labor market dynamics continue to provide a roller coaster ride for employers. Especially those who are finding it hard to fill their funnels with qualified candidates. In reaction, companies are challenging the notion of what makes a qualified candidate and slowly abandoning their reliance on traditional (and flawed) signals such as resumes and degree attainment. At the same time companies are realizing that a bird in the hand is worth two on Indeed. Quiet Hiring is now a thing as more and more companies build programs to help their existing talent forge borderless internal career paths.
Trends in today’s labor market share the same foundation- a reliance on the identification and development of skills. The movement towards putting capabilities ahead of pedigree continues to grow and HR tech providers are offering the infrastructure needed to execute on it. An increasing number of platforms offer automation that can boil a candidate or employee down to the skills they possess and match this to relevant opportunities and developmental resources. Many of these systems rely on automated parsing of resumes and job descriptions as the foundation of skills identification. While automation does have a place, reliable and accurate assessments are the only true way to directly identify and confirm skills in a fair manner. Emerging platforms also utilize skills as the currency for internal mobility and talent planning. They do this by providing a way to use an individual’s present skills and desired future positions to determine how to best invest in a mutually rewarding future. Expect to see skills first employment approaches lead the headlines in 2023.
When was the last time you heard the term “big data”? A decade ago, it defined HR tech’s entry into the AI age and advanced analytics. Nowadays we don’t talk about data being big or small- we talk about using technology to build a foundation that supports efficient, actionable insights about people. As data continues to become the currency of organizational people insights, most big companies have formed internal HR data/analytics functions which they supply with a steady stream of data from a variety of platforms. As we learn how to connect various platforms with centralized databases, we will gain superpowers that will help both employees and employers maximize their work experiences.
Imagine using employee listening/experience platforms to collect pulse survey data about the effectiveness of new hires to front end data collected from hiring assessments. This would provide an almost real time feedback loop that would enable vision into hiring effectiveness while feeding personalized information talent management and development programs.
It will be exciting to see novel use cases like this emerge as we continue to collect and manage more data about people and their work. Perhaps the biggest challenge we will face is ensuring that our castles are built on quality data. As my friend Mike Campion put it in our recent podcast interview - when it comes to data quality we have moved beyond worrying about “garbage in, garbage out” and are now constantly managing “data landfills”. Landfill data begets nothing good and opens the door to bias and inaccurate conclusions. In 2023 we will continue to see more love put into managing and organizing quality data.
No one in the HR tech space is leading their market facing messages with AI anymore. Perhaps this is because using AI is no longer a differentiator. This is a good thing because over the past few years AI was promoted as a solution when it is truly only one component of a bigger value proposition. We have finally moved past the hype and are settling into the AI reality.
The future of AI in HR tech is all about responsibility and the management of its dark side. Regulation, public opinion, and the risk aversion of enterprise companies are factors that are driving the increased focus on responsible use cases.
When it comes to AI and talent assessments, there are two sides of the fence.
On one side is AI that interprets resumes, social profiles, and job descriptions and infers matches between a person and a job. These solutions are more susceptible to the dark side of AI because they tend to be wide open, use data that is not highly structured, and can learn their way to being biased.
On the other side of the fence AI is being used to automate the scoring of complex and open-ended assessments such as role plays, interviews and simulations. In this case, the AI is trained with data from expert raters such that it learns how they score a particular exercise. This situation puts constraints around what the AI is being asked to do and minimizes its dark side by narrowing the opportunity for bias. There are many instances where the ratings made by these AIs are highly correlated with those of experts.
While AI is not yet ready to make complex decisions about whom to hire on its own, replacing human scoring will create tremendous efficiencies and support a new generation of work simulations in which complex interactions can be reliably scored without bias. So far 2023 has kicked off with a bang in the AI department with the advent of ChatGTP we can expect to brace for a wild ride as we see it enter the hiring arena. Where do we go from here is the question of the year thus far.
The NYC algorithmic hiring law , requiring employers to commission 3rd party firms to conduct yearly bias audits on any predictive hiring algorithm, has caused quite a stir over the past year. While its heart is in the right place the proposed law is quite vague and has left employers with more questions than answers. Given the stakes, it is good to see that the debut of this law been delayed to allow for much needed review and revision. It will be interesting to see what eventually happens. But the cow is now out of the barn. While the EEOC’s regulations have been in place for over 40 years, they do not directly address the complex AI based solutions that are becoming ubiquitous. We can expect the future to bring increased regulatory oversight of hiring practices with the gap filled by supplemental regulations at the state and municipal level.
No matter what comes of it, the NYC law is exerting an indirect and positive impact on employers. The chatter has created increased scrutiny of predictive hiring tools of all types, including traditional assessments. This is a good thing if it leads employers to follow best practices for hiring and assessment. Ensuring all predictive hiring tools are job related, reliable, and fair through a process of job analyses, validation, and fairness analysis are ironclad. This methodology can and should be applied to AI based tools. Don’t be surprised to see more talk of regulations and remedies over the coming year, and beyond.
Change is slow- Despite all the excitement about these trends a year from now things probably won't look that different. As the graphic below demonstrates, when it comes to assessments the future is in sight but far from in our grasp.
Change and the adoption cycles that create it take time, especially in the world of corporate enterprise. But the motion is all in a positive direction. Things continue to become more equitable, and the realization that equity creates value for both individuals and employers will create long tailed trends of positive change.