How Predictive Technology can shape L&D Landscape

What is more predictable? Is it our wants or our needs?

Common logic suggests that the latter is much more predictable. However, Predictive technology has advanced more in the arena of our wants and has been instrumental in predicting our likely choices. Consider Netflix or Big Basket for that matter. They can predict what we would want to watch next or buy next; and we end up doing exactly that. If ML can recommend what we want, then it can also figure out what we need. But when it comes to predicting learning needs of an organization, it is not as easy as it sounds. Let’s explore why…

In the world of learning and development, the need to upskill oneself is a constant. And most often, it is the need that leads to the Training Need Analysis and Training Need Identification for a suitable Learning Intervention.

But despite the simple looking formula, Predictive Technology hasn’t made many inroads in L&D! Whether it’s sales, IT functions, finance or HR, repeated research and surveys have conveyed that personalization is the top priority for all leaders across industries, from large MNCs to mid scale start-ups. This expectation of personalized learning experience has been applying an added pressure on most L&D managers to find new ways of meeting these needs. And this is exactly where ML/AI can play a pivotal role to manage learning needs based on scalable personalization.

So let’s dive deep and understand what exactly needs to be done. First, let’s take a quick look at what has been happening thus far. The three main types of existing recommendation systems for learning, are based on the concept of ‘What’s next’! – Popularity based, Content based and Collaborative filtering.

The Popularity based basic system, has taken shape based on what previous learners have opted for, without taking into account the nature of the learning content or the characteristics of the learner. In the Content based system, the recommendation happens by taking classification factors such has, skill and modality into consideration without accounting for the learner’s current competencies or defining characteristics. In the Collaborative filtering process, the exact opposite happens where the system picks up learning content based on what other users with similar characteristics have opted for, without focusing on the need based nature of the content.

The shortcomings of the these systems are quite evident where none of them offer a high value personalized recommendation. ML/AI has been instrumental in helping several organizations with their HR functions, from hiring and recruitment to finding team efficiency and stickiness of employees. When it comes to learning interventions, most trainings fail even today. The basic problem persists at large and Predictive Technology has not been effectively used in filling up that void.

For these systems to effectively analyze the need of an individual and recommend a personalized learning path, it needs a spectrum of Data that can cover all bases. Take a sales person for example. The end goal may look relatively simple – getting better numbers; but to achieve that however, it is very important to understand the current competencies of the person. Domain knowledge, detailed understanding of the product, confidence, negotiation, communication, selling technique, empathy, strategic thinking, problem solving and many more such skills combine together to make a high productive sales person.

We, at understand that need from close quarters, and this is where we want to step in with our clearly defined matrix at each level. From planning and strategy to execution and operations, we are bringing forth an AI model that will be deeply integrated with the DNA of the organization. With an intensive list of all the measurable matrices and well defined thresholds, the models will be able to constantly evolve with the results of the learning engagement, performance data, surveys and assessments. Running our predictive engine on top of the super set matrix will produce highly personalized results.

Skill assessment tools like Mettl help our cause to make a basic foundation on which the integrated system is being developed. This will further down to automating high level Organizational Development interventions based on organizational, business and market Data. We are poised to bring the very best of AI driven personalized learning solutions that will disrupt the L&D space. The future is not far away…



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