The purpose of this publication is to explain the need of a strong AI based learning algorithm that can enable training and enablement for specific teams.
A study by Pew Research Center states that there are about 56 million people between the ages of 21 to 36 who enter as new workforce every year. They need to be trained on multiple new technologies each year. The current ways of learning that we adopt needs to evolve to enable associate motivation and also to ensure optimal engagement given that the new generation possesses a distinct mindset.
Gartner suggests that 75% of employees at different work locations watch training videos about 3 times a day and there are about 5 billions videos that are being watched on YouTube every single day just to gain knowledge. These data points suggests a strong need to have better learning and enablement systems. We will explore this further in the following paragraphs.
An ideal AI based learning program is an excellent need of the hour. Let’s take an example use case of how the evolution to AI based learning has to happen.
Every team’s learning needs to be unique and inbuilt with a thought of how the overall evolution of the skill has to happen. The thought of building an horizontal organisational training plan that satisfies multiple teams together might not be a successful model.
Migration from a Proactive to a Predictive training model that covers horizontal functions, automation , skill enhancement, role movement enablement , provide cost and efficiency gains and finally linked to performance is needed and has to be provided.Lets look at the example below
An Operations Support Engineering Team needs to enhance the skills to help the team migrate to SRE or to a Devops role. The analogy of L1 , L2 support to a high end Engineering Team probably would be a good one for an associate to achieve.
Learning enablement should look as follows:
Maturity Levels (Definition of maturity )
Target Level(Ideal level for the organisation)
Current Level (Calculated based on assessments)
Gap (Difference between Current and Target levels)
Once a detailed assessment is done with the sample population, an assessment engine based on the learnings is arrived at. It needs to integrate with the training system to enable the overall learning.
The system is built with a closed loop feedback. On each training that is enabled and how it is provided and how effective the same has been in workplace. Any training found redundant can be moved out and newer ones can be replaced. The feedback loop make the system strong enough to enabled a successful AI based learning Algorithm.unsplash.com)
Once the understanding of data is achieved, deep diving into individual training needs becomes so much more easier. A relevant training calendar has to be user analytics and progression driven. What that enables is a closeness to the plan and gives a sense of urgency and satisfaction to the learner.
What can be achieved
1. Gather Intelligence of the current status at which each of the team are and where they need to get to.
2. Identify the potential improvements each and everyone have to get.
3. Set goals for the future by intelligently predicting .
The next blog will be more on how to implement ,practicing the same and see how and what has been achieved and what are the improvement areas.