Why does traditional skill development commonly fail? And how can employers develop more nimble workforces capable of thriving in the digital age?
The challenges of traditional up- and re-skilling
Most businesses tend to use traditional classroom, teacher-and-student settings for reskilling. They send people to training seminars or assign them online learning courses. Typically, these traditional means of education concentrate on drilling individuals on specific skills in isolation. For instance, data science is becoming increasingly important.
Organizations want people who can take large amounts of data and create predictive models with AI. A respective data science course will likely cover programming languages like Python and R. But given the speed of change with technology, it is hard to say if either one will be around in another two years’ time. To maintain the relevant skillsets, it is important for people to stay abreast of multiple things—and not just technology. Traditional training doesn’t do that well.
For example, data scientists and supply chain experts often need analytics “translators” to create a common ground between the two fields and identify supply-chain use cases where data-driven insights could be applicable. In the absence of formal translators, individuals from each discipline could better interoperate with one another if they had some level of proficiency in the other discipline. In learning science, this is called a “T-shaped” skillset, as opposed to “I-shaped.”
An I-shaped person has narrow expertise but is very deep in their domain. A T-shaped individual instead can bridge across many different fields. Most learning strategies don’t focus on T-shaping and collective teams’ skills, and as a result, continue to teach skills as if people will work by themselves. That’s certainly not true today, as transformative work takes a proverbial village.
The twist is that the very source of that challenge — technology — might help us solve it. That is if we don’t look at people and machines individually, but rather as collective networks.
For a decade, the Massachusetts Institute of Technology (MIT) Center for Collective Intelligence (CCI) has studied how groups of people can act more intelligently — including when learning — by collaborating better with each other and with increasingly intelligent machines. The CCI’s research raises a new possibility: that when large organizations adopt a group-focused approach to learning, they can become collectively more adept and master new skills in the process.
Creating networked intelligence that facilitates learning
When you have learning communities, you can cultivate more T-shaped people and activate a “networked intelligence” that enables professionals to complement each other and share new knowledge with others. Think of Wikipedia or Linux — the emergence of these collective movements show that large and non-traditionally-structured groups are effective for innovation, learning, and dealing with scarce or new knowledge.
For Genpact, our Genome reskilling initiative is inspired by MIT’s CCI work and concept of collective intelligence. The first step in that process is to “illuminate the network.” That is, to find out where the knowledge — and the connections between people — are.
We used an all-employee survey to surface a network of knowledge “gurus” (experts) and brokers (people with strong connections) who could disseminate their knowledge with the greater, collective whole. This enabled us to identify the best and most relevant minds and to understand how they are connected and can complement each other.
First, employees rated themselves on their proficiency for different competencies (e.g. Python coding, process data mining, or insurance underwriting). Then, they mentioned the names of colleagues who are go-to-resources for a subset of those skills (e.g. design thinking or robotics).
The second set of data was further processed through a network analysis that identified the most central employees in the new world, as well as the ones who possessed those individual skills the most. That enabled the creation of a “skill inventory” to later associate learners into communities of practice led by the experts and facilitated by the knowledge brokers.
Ultimately, people learn better from others and within the context of their real-world work. Knowledge brokers within these communities of practice can provide relevant educational resources and tools. They can make learning contextual and new skills immediately applicable to what learners are doing daily. Further, knowledge gurus and brokers can best sense how well people are mastering new skills and curate new information, such as interesting articles or other external courses.
This method allows us to also more accurately identify skill levels by role, with an eye to obtain the right level of combined skills in teams, not individuals. The key is eliminating friction so that people can build on each other’s ideas and collaborate through an array of tools, such as through virtual conferencing, learning portals, or knowledge management systems.
In all of this, digital technologies play an important role in helping to curate knowledge, enable virtual collaboration and uncover what behaviors enable people to learn faster and more effectively. For example, data-driven diagnostics can provide useful insight into learning patterns and learner experience, creating a rich feedback loop.
The result is a tight-knit learning community, capable of reskilling on a large scale and reacting more nimbly to the evolution of those skills. Each individual in the workforce operates like a node in what MIT dubs a “supermind,” where people and technology are connected, engaged, and collaborating towards higher knowledge, company performance, and radically improved adaptability to volatile and uncertain conditions.
