Jessica Miller-Merrell | , , , , ,| By
This is a two part series that dives into the basics for machine learning for human resources and recruitment. Click here to visit Part 1.
In a world where the demand for qualified knowledge workers is greater than ever before, highly-skilled professionals expect nothing less than the best from their employers when it comes to workplace benefits, compensation and development for themselves as well as their employees. But, in this highly competitive business world, talent management and development takes a back seat to business plans and meeting ever looming deadlines.
In 2015, talent management, specifically employee development is becoming a priority for employers as they re-evaluate the cost of recruiting, hiring and onboarding in comparison to the cost of retaining and developing internal talent within the organization. In fact, The 2014, Conference Board’s Talent Leadership Trends Forecast identified human capital development as the number one CEO’s focus when it comes to human capital. While senior leaders are increasingly focus on human capital and creating an internal talent pipeline within their organizations, human resource leaders are challenged with building internal talent development programs. Question is should they be looking at a high potential program, an organization-wide employee development plan, front line manager training, or specialty programs in high turnover or highly specialized training and development programs for knowledge workers? The answer is, all of the above. The question is how do you squeeze resources into your already overwhelmed human resource or organizational development team? The solution is accomplished by leveraging your already existing workplace technology.
What is Machine Learning?
The challenge faced for Chief Human Resource offices, is that developing multiple talent management and development programs simply takes time, effort and man or woman power. In this competitive talent and business landscape, it is often time we do not often have. There are just simply not enough hours in the day. Workplace technologies are emerging that allow you and your employees to have the best of both worlds. Your employees can have personalized and customized learning and development that fulfill employee’s very individual expectations and needs. The secret to scaling your talent development program is a combination of big data and machine learning.
Machine learning combines computer science with data to create a highly personalized and contextual experience that is unique to the individual user. Programs are focused on accelerating development, growth and especially learning.
The New Work Hack
Machine learning helps us transfer what is learned for one task and improve learning for other tasks that are related. This is the basis for utilizing machine learning in organizations as part of a personalized talent management strategy. It helps employees improve and/or accelerate their learning, growth or development based on past activities and data. It does this by recommending new resources, information or classes.
Machine learning is the new work hack that can help accelerate employee development in areas, you might not have considered and even overlooked. The data helps establish and identify the skill or topic’s importance. This is especially useful for organizations that are experiencing hyper growth and/or increased turnover as knowledge workers retire or voluntarily leave your company. The data helps identify potential knowledge gaps or opportunities for growth. This use of data can be complementary as well as supplementary to your established training, development and high potential programs you’ve identified.
Preparing for the Boomer Exodus
Beginning in 2015, 33 percent of our workforce including 48 percent of our supervisors will be eligible to retire. The Boomer mass exodus has finally begun. This simple statistic makes knowledge sharing, learning and development no longer a nicety, but an imperative for the future success of your team as well as your entire company. Our Generation X and Millennial workers and future business leaders who are filling these knowledge gaps are fiercely independent and self-directed. They want to be an active part in their own fulfillment and career path. How you choose to educate and engage them is extremely important. How do you scale the vast amount of knowledge sharing but also individual learning and development at your company due to this change of the guard? Machine learning presents a personalized and focused opportunity.
Machine learning is self-directed using your company’s already-existing learning and development resources. It allows employees to find resources based on their previous pageviews, courses completed and other activities contained within your own centralized company portal. It utilizes your existing HR and workplace technology to help develop a custom yet focused career development and self-directed learning strategy. Employees are encouraged to learn and explore organically within your gated and secure workplace technology that supports your organization’s company culture as well as more specific talent development programs for front line manager, knowledge workers and high potential employees.
The Ultimate Engagement That’s On Demand
One area that taps into machine learning methodologies and builds on them is artificial intelligence. This area is a huge opportunity to be able to provide any individual who is engaged in a human resources process. Think about candidate screening, onboarding, leave of absence, and offboarding. In it’s simplest form, AI is a the ultimate choose your own adventure book. Responses or assumptions are preplanned. The machine can learn and assess things like candidate applications to determine their qualification for a role they applied. Rules, responses and boundaries are set making the conversations and interactions human but facilitated by a machine.
The use of machine learning in human resources continues to grow. This is a two part series that dives into the basics for machine learning for human resources and recruitment. Click here to visit Part 1.