Views on the News: Why the AI and ML trends of today remind Laura Hume of the 90s

Every month at the Skillcentrix blog, we share a recent article from the HR press, and ask our Advisory and Workday Technology experts to share their “views” on why this particular news is relevant to People and talent leaders throughout the ecosystem.   

This month, we’re tackling two articles, focused on how Artificial Intelligence and Machine Learning will impact the Human Resources profession. (There have been quite a few articles out there, as you can imagine – we picked two from last March as they seem to have the most comprehensive overviews.)   

Let’s dive into this month’s review and see how (and why) the AI world is going to change ours!   

 Article One: How AI and ML Are Powering the Future of Work (by Sayan Chakraborty, published on the Workday blog March 14, 2023)  

Workday’s Unique Approach to AI and ML   

We think about and implement AI and ML differently than any other enterprise software company in the world. From a capabilities perspective, Workday takes a platform-first approach that embeds AI and ML into the very core of our technology platform.   

Why does this matter? It matters because it allows us to rapidly deliver and sustain new ML-infused capabilities into our applications. ML gets better the more you use it, and by having millions of users constantly using dozens of applications on the same platform, it improves at a faster rate.  

At Workday, we’ve embedded AI and ML into the very core of our platform—delivering unrivaled business adaptability and competitive advantage to our customers. In ML, practitioners talk about the “3 Vs” of data needed to drive positive outcomes: sufficient volume, velocity, and variety. Workday has all three.   

Article 2: The Role Of Generative AI And Large Language Models in HR (By Josh Bersin, published on the Josh Bersin Blog March 10, 2023.)     

How Generative AI and Large Language Models Can Help 

Given the complex, important, and messy business we are in, how can Generative AI and Large Language Models help? Well while it’s still early days, let me venture the idea that this new branch of AI has the potential to totally reinvent how much of HR works. And in this disruptive change, we will see new platforms, new vendors, and new ways of running our companies.  

• Create content for job descriptions, competency guides, learning outlines, and onboarding/transition tools  
• Create skills models, experience models, and candidate profiles for recruiting  
• Analyze and improve pay, salary benchmarks, and rewards 
• Performance management and feedback 
• Coaching and leadership development 
• Individual Coaching, Mental Health, and Wellbeing 
• HR self-service and knowledge management  

The Underlying Data Set in HR Is Textual  

If you want to really do a “big data” analysis of your workforce’s skills, experience, and suitability for different work, you’re dealing with mountains of “anecdotal data,” much of which is encoded in biographies, work output, company leadership frameworks, assessments, and lots of communications. And of course, there are performance appraisals, business results, and more.   

I would remind you to consider Generative AI as a tool, not a living person. Just as Microsoft Excel was groundbreaking in the early 1980s (and there were fears of it putting accountants out of business), this system will become an essential business tool that we all must learn how to utilize.  

The Expert Opinion  

This month, we asked Strategic Talent Advisor, Laura Hume, to add her thoughts based on her personal experience with Artificial Intelligence and Machine Learning in the Human Resources profession.  

Let’s see what she had to say…    

“When I joined a large consultancy over 25 years ago, they had just developed a robust, stand-alone skills tool. Every role at every career level had articulated specific skills with required proficiencies as defined via well-written behavior statements. I was an experienced hire in their client-facing learning and change group and thought the tool was an absolute gold mine. It showed me where I was right on target, where I had gaps, and where I was rocking it.  

I thought it would help me more quickly integrate into my new and demanding company, but gold mine or not, there were two major issues with the tool:  

Challenge One: The tool could not overcome the culture. In the late 90s, this company did not hire many folks with experience. After presenting my self-assessment, my manager was quick to agree with my self-identified deficiencies but totally balked in places where I had rated myself above my career level. Even when I was able to point towards the specific behaviors bulleted in the tool — for instance, I ranked myself highly on written communication due to my composition-focused teaching degree and published articles and book on learning — my manager didn’t buy it. I was sent to their three-week training course for new hires, where I did learn some content, however, ended up mostly hanging out with the instructors and coaching the folks fresh from college. It was the complete opposite of adaptive learning, and in turn, I had now spent 19 days (or two and a half weeks) on-site and away from client work. 

Challenge Two: There was no AI or machine learning at the time. Although the tool was incredibly powerful, keeping it up to date was a completely manual process that proved impossible to maintain. After a year in production, it fizzled away, and the pendulum swung in the opposite direction where skills were only mentioned in the broadest and most sweeping terms.   

I am immensely excited to continue in this field where now, both issues are being addressed. Having a mobile workforce with “new” employees carrying variable skill and experience levels is the norm now, not the exception.  

Companies need to improve on prioritizing which skills are the key must-hire skills for each role versus which skills can be built on the job. I think we will see more of a focus on professional/leadership/people skills as companies figure out that it is much easier to train an employee in Python 3 than to teach them to be strong communicators.     

Finally, AI, ML, and Big Data give leaders the information they need to make strategic decisions about hiring, development, internal mobility, and retention of high performers. It’s apparent that the volume and velocity of data will continue to increase, but the variety of data is the secret sauce. Pulling skills data from multiple sources will allow leadership to peek at skills hiding within their workforce. They won’t only see the slice of skills contained within a resume that was tailored to a role or the output of an annual review, which is often focused on a point-of-time project and centered on shoring up skills deficits.   

My long-ago company was ahead of its time, carrying the right strategy but supplementing it with the wrong tools. AI will now allow leaders to automate the maintenance process, and when skills are tied into Workday with a flow across all components of the employee lifecycle, companies can become much more strategic with their talent decisions.”