Advancements in human capital management systems, more strategic and data-driven human resource and talent management practices, and increased attention to bias are all factors that are changing how people are hired, developed, promoted and fired.
I teach and work in talent management and leadership development. I’ve used these programs and practices in the real world and continue to learn and research how these practices are changing. Artificial intelligence and systems are already big business, grossing over US$38 billion in 2021. Without a doubt, AI-driven software has the potential to advance quickly and change how companies make strategic decisions about their employees.
Here’s what that acceleration may mean to you.
Imagine you apply for a job in the very near future. You upload your carefully written résumé through the company website, noting that the platform looks eerily similar to other platforms you’ve used to apply for other jobs. After your résumé is saved, you provide demographic information and complete countless fields with the same data from your résumé. You then hit “submit” and hope for a follow-up email from a person.
Your data now lives within this company’s human capital management system. Even if they collect them, very few companies are looking at résumés anymore; they’re looking at the info you type into those tiny boxes to help make comparisons between you, dozens or hundreds of other applicants, and the job requirements. Even if your résumé demonstrates that you are the most qualified applicant, it alone is unlikely to catch the eye of the recruiter, because the recruiter’s attention is elsewhere.
Let’s say you get the call, you ace the interview and the job is yours. Your information hits another stage within the company’s database, or HCM: active employee. Your performance ratings and other data about your employment will now be tied to your profile, adding more data for the HCM and human resources to monitor and assess.
Enhancements in AI, technology and HCMs enable HR to look at employee data on deeper levels. The insights gleaned help identify talented employees who could fill key leadership roles when people quit and guide decisions about who should be promoted. The data can also identify favoritism and bias in hiring and promotion.
As you continue in your role, data on your performance is tracked and analyzed. This may include your performance ratings, supervisor’s feedback, professional development activity – or lack thereof. Having this large amount of data about you and others over time now helps HR think about how employees can better support the growth of the organization.
For example, HR may use data to identify how likely specific employees are to quit and evaluate the impact of that loss.
Platforms that many people already use every day aggregate productivity data from sign-in to signoff. Widely available Microsoft tools including Teams, Outlook and SharePoint can help provide insight to managers via their workplace analytics tool. The Microsoft productivity score tracks overall usage within the platform.
Even the metrics and behaviors defining “good” or “bad” performance may change, relying less on the perception of the manager. As data grows, even the work of professionals like consultants, doctors and marketers will be quantitatively and objectively measured. A 2022 New York Times investigation found that these systems, designed to improve worker productivity and accountability, had the effect of damaging morale and instilling fear.
It’s clear that American employees should begin to think about how our data is being used, what story that data is telling, and how it may dictate our futures.
Not every company has an HCM or is advanced in using talent data to make decisions. But many companies are becoming savvier and some are incredibly advanced. At a recent Microsoft Viva summit I attended, chief human resources officers from companies like PayPal and Rio Tinto outlined ways they are using these advancements.
Some researchers claim that AI could promote equity by removing implicit bias from hiring and promoting, but many more see a danger that AI built by humans will just repackage old issues in a new box. Amazon learned this lesson the hard way back in 2018 when a résumé-sorting AI it built had to be abandoned when it favored men for programming roles.
What’s more, the increase of data collection and analysis can leave employees unclear on where they stand while the organization is very clear. It’s best if you understand how AI is changing the workplace and demand transparency from your employer. These are data points that employees should consider asking about during their next review:
- Do you see me as a high-potential employee?
- How does my performance compare with others’?
- Do you see me as a successor to your role or others’?
Just as you need to master traditional aspects of workplace culture, politics and relationships, you should learn to navigate these platforms, understand how you are being assessed, and take ownership of your career in a new and more data-driven way.
Want to know more about AI, chatbots, and the future of machine learning? Check out our full coverage of artificial intelligence, or browse our guides to The Best Free AI Art Generators and Everything We Know About OpenAI’s ChatGPT.