Are Training and Professional Development Leaders Being Replaced by Machines?
Computers rarely make mistakes, they don’t let emotions and biases affect their decisions, and they are generally faster and definitely more logical than humans. So why not let them run training and professional development organizations?
This seems to be a popular topic as of late but it’s rarely applied to the corporate learning environment. Regardless of where you fall on the human versus machine spectrum, it is important to acknowledge the role of data analytics in learning and development training.
Picture this. You are given a list of 35 people by a computer and told to fire them in the next month because doing so will help the company’s bottom line. Some of those individuals have great performance reviews and some have been employed by the company for over 20 years, do you listen to the computer and fire them?
Maybe the computer is wrong, maybe it didn’t account for human behaviors like tenacity and kindness. Maybe a few people on the list are your friends and are good at their jobs so you don’t want to fire them. After all, you have experience in this area, you probably have better judgment than a computer and the computer has never met those people. Plus, the algorithm doesn’t have the ability to empathize, and it lacks intuition so relying on its outputs would devalue the importance of human interaction.
You would think organizational decisions should be made by you, not the predictive model. The only problem is, this is an old, outdated way of thinking. If we continue to value our own conclusions over the results of the data, we will be left behind.
As a data scientist, I am biased and often find myself thinking organizations should clean house and make way for the data geeks and their algorithms. Of course, this would be a bit drastic, but it’s equally extreme to run training and professional development programs without tracking and analyzing data. We must find a middle ground.
If we continue to ignore the data and distrust the models, we are doing our organizations and our people a disservice. If we continue to run our learning and development training programs in the same way and assume they are working, we are limiting our organization’s potential. We are missing opportunities to create the best leadership development programs possible.
Advanced Learning Intelligence (ALI), developed by The Regis Company to address this challenge, uses learning data in addition to field and market data to minimize the cost of learning and maximize the learning impact and transfer. To properly accomplish these objectives, the following factors must be compared and analyzed to identify correlations, both positive and negative:
- learning data (test scores, core competencies, behavioral tendencies, skill gaps, etc.)
- demographic data (job title, work history, gender, etc.)
- performance data (performance reviews, productivity, work quality, job satisfaction, etc.)
These predictive models and machine learning techniques answer questions like:
- What was the impact of the professional development training program?
- What skills/behaviors do our people still lack and how is this affecting our organization?
- How should the curriculum or delivery be adjusted so that the learning program is more effective?
So does it make sense for learning and development leaders to be replaced by machines? Of course not. But could learning programs, such as corporate leadership training be improved by incorporating data and predictive analytics such as those provided by ALI? Absolutely.
Computers that use models like the ones above make training and professional development programs more effective. These machines are fully informed and make statistically logical decisions to reduce the cost of training, accurately predict the type of individuals that need training, and optimize learning impact to drive company-wide results. In times of hard decisions, we revert to what we know and often lean towards our instinct to guide us rather than listen to the numbers. We have biases and immediately reject model outputs that go against our beliefs because we think we know better. But in doing this we are hindering our potential.
Computers can provide insights into what’s working, and making data-driven decisions based on these insights makes people more qualified and able to do their jobs. Managers would be well served to analyze data, make data-driven decisions and use computers to make learning solutions that drive real results.