L&D Evolution: Is the Tail (Still) Wagging the Dog?

I recently attended a Society of Industrial-Organizational Psychology (SIOP) Leading Edge Conference, which focused on People Analytics. Having been in the field of I-O Psychology for thirty-two years, one of the things I really appreciate in attending these conferences is meeting people who are new to the field of I-O and their careers, and having discussions about what they do in their roles and about the field in general.

At this recent conference, I met a young man who is working for an international consulting firm in an assessment development role. We began talking about the field of Learning and Development and he asked if I thought the field had changed very much over time. My response was that in terms of the content of the field, the understanding of things such as best practices, etc., I did not think there was very much change at all. Rather, that the changes over time have been more about technology. And, I believe a similar statement can be made about the other areas of I-O expertise, including selection, performance management, succession management, etc. When attending I-O conferences year after year, when there is a conversation about what is new in the field it is almost always about technology.

To some extent that should not be unexpected as I-O is a science and the scientific concepts behind selection, performance management, learning and development, etc., should not change very much over time. For example, we know that selection procedures need to be reliable and valid, no matter what technology is incorporated in the procedure.

As a field though we could push innovation and best practice more beyond the innovation that comes with technology. And, we should not let the changing technology be the thing that drives the evolution or direction of the field. That is the tail wagging the dog.

An Example of Skills-Based Taxonomies

An example of this was when skills and skills taxonomies became the focus in many organizations. Josh Bersin, a renowned expert primarily in the arena of HR technology, talks about this time in The Pragmatic Approach To Skills - Bonus Episode – JOSH BERSIN. He discusses his belief that the concept of skills became popular initially in the learning space when companies like Degreed created skills-based learning search tools in their LXPs. From there, the wildfire spread with skills-based recruiting systems, skills-based talent mobility systems, the Skills Cloud introduced by Workday, and the list goes on. Companies gravitated to what was viewed as a very easy way to accomplish things like identifying skills important to a company and to specific roles, matching candidates to skill profiles, or assisting people in career path planning. HR technology platforms were generating so much buzz and people enthusiastically jumped on the skills train.

Bersin comments:

“This particular topic has driven me crazy for the last two or three years because we have this enormous interest in skills-based hiring, skills-based internal mobility, skills-based development, skills-based pay… but in reality, if we are not focused on the business problem, we’re not going to be successful.”

I echo the sentiment. This is an area similar to what happens over and over again when a technology platform drives the HR field to move in a certain direction without a strong understanding of if it makes sense or not, if it will drive business success or ultimately frustrate the company because the systems or platforms are driving what is done and how it is accomplished from a talent perspective. Then, when the next new HR system is rolled out, everyone is scrambling to figure out how to integrate what they’ve been doing with the previous platform with the new platform capabilities. The technology is the tail wagging the dog.

Bersin also comments that in most companies nobody really knows what the word skill means. This continues to be true despite the years of companies applying things like Workday’s Skills Cloud. Bersin says that he prefers the word capabilities because it is a word that a business person understands.

I believe that part of the reason that the skills-based “frenzy” happened is likely because companies and leaders never liked the term “competencies.” Now, all of the skills-based platforms made it easy to forget about competencies and move on to what was thought to be much more understandable. In their zest, companies “threw out the baby with the bath water.”

Drilled into the head of every I-O psychology practitioner is the definition of competencies as the “knowledge, skills, abilities, and other characteristics” (KSAOs) important to success in a role. Competencies are more encompassing than skills by themselves. Defining a refined set of competencies that are important to certain roles versus a laundry list of 100 skills is a much broader and simpler way to look at talent assessment, fit, or various other dimensions.

For example, a hospital may have a leadership framework for its physicians that includes competency areas such as Develops Coalitions or Systems Transformation. These areas are defined behaviorally of course and when the hospital is hiring new physicians these are two areas that they want to assess. These are also two areas that they want to develop in their existing staff. These are two examples of the broad competencies that are important to physicians at this hospital. In addition, however, there is a place to go deeper. This is related to technical or functional skills. So, for example, if a hospital is hiring an oncology specialist, there are specific technical skills that this person needs to have in addition to these broader competencies.

The Current Example of Artificial Intelligence

Now, we have a new example of AI technology impacting every field, HR and otherwise, and every arena within HR, including recruiting, selection, and learning and development.

Josh Bersin recently published a paper call It’s Time for an L&D Revolution: The AI Era Arrives – JOSH BERSIN. In it, he makes the key points that corporate L&D is stagnating and that AI is set to revolutionize the learning environment with a significant shift toward “AI-first learning” (what he refers to as the new model of learning). He notes:

“Our research reveals that AI fundamentally alters traditional methods, technologies, operating models and outcomes. By enabling personalized and adaptive learning experiences, AI enhances engagement and effectiveness. It streamlines content creation and delivery, reducing the time and resources needed to develop training programs.”

I believe it true that corporate L&D is stagnating. Back to the conversation at the recent SIOP conference, from my vantage point, L&D programs and content look much the same as they did 30 years ago. It is the technology that exists related to L&D that changes more over time.

I also believe that AI provides wonderful benefits to L&D such as enabling more personalized learning. But, just as with implementing AI in all other areas, human oversight and governance need to lead how AI best impacts the field.

Conversely, I don’t believe that the field needs to yet again allow the changes in technology to drive the evolution of the field. Rather, we need to do things such as define how adult learning has evolved, and consider where and how AI fits into this change from previous learning theory. How are adult learners learning differently than they did 30 years ago? How are the brains of children who grew up in a technology era different as adults from those who did not? What do these things mean to how adults today actually do learn and grow?

As I-O practitioners, we need to offer the governance and oversight of how to use AI in L&D most effectively, similarly to how we offer governance related to selection assessments which incorporate AI. In that recent SIOP LEC conference on People Analytics, there was much discussion about the opportunities that the field of I-O has to govern and appropriately evolve the use of AI in all talent practices. There were presentations that included:

• Generative AI at Work: Building Readiness and Systems for Strategic Impact by Evan Sinar, PhD, Senior Research Scientist, Global Hiring Science at Amazon
• GenAI Adoption: Improving the Speed and Depth of Analytic Insights by Pat Caputo, Head of People Analytics at Meta
• Employee AI Adoption and Connections to Thriving and Productivity by Jon Peterson, Senior Director, HRBI Data Science & Applied Research at Microsoft.
• Increasing the Trustworthiness of AI by Alexandra Dmytriw, Director Global People AI at ServiceNow

There were also numerous calls to action for the field of I-O and the particular expertise of People Analytics to define and drive the future of organizations. After all, the science of Industrial-Organizational Psychology is the science equipped to do this.

Conclusion

The evolution of L&D should not be guided by technology innovation, as it has been so often in the past. Rather, it should be guided and governed by the real science behind it. The science too has to evolve as the environments in which we operate have evolved so dramatically over time. 

Follow us on LinkedIn to get immediate access to the next article and all of our other resources.