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Companies know upskilling is critical. That doesn’t mean they’re doing it right

“Upskilling” is a buzzy word for companies these days, but MIT’s Gianni Giacomelli says there’s a right and a wrong way to do it.

Companies know upskilling is critical. That doesn’t mean they’re doing it right
[Photo: Mirko Blicke/Unsplash]

If there was any doubt that business leaders have caught on to the importance of upskilling, Amazon quelled it when they announced plans to invest $700 million in retraining 100,000 U.S. employees in July.

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With a growing skills shortage in the workforce, companies are desperate to hire the talent needed to succeed in an age that will depend increasingly on advanced technologies, such as artificial intelligence and blockchain. For many, upskilling is the only viable option.

Unfortunately, studies show companies face challenges in building the business case for reskilling and upskilling and setting up programs that deliver a return on investment. Genpact’s own research shows a disconnect between company offerings and employees. While 53% of senior executives said their organizations offer training, only 35% of workers say their companies have reskilling options, and less than a quarter say they have participated in such training.

So, what are businesses doing wrong?

For starters, most businesses tend to focus on training individual people on a specific topic in traditional, “teach and test” classroom settings. In so doing, they aim to build expertise in one subject at a time, with the goal of cultivating specialists in a specific category—think data science Python experts or natural language processing (NLP) coders. These methods aren’t scalable, as there are too many new skills for organizations to continuously design trainings around. Technical skills in the digital era tend to have a short shelf life, with new tools and practices replacing old ways of working at an unprecedented pace.

What’s more, retention rates for these types of training don’t justify the cost. Research shows that most adults studying alone will forget technical information within weeks of learning. They remember far more, however, if they’re able to discuss their learnings with peers, or apply it to real life situations.

Harnessing collective intelligence

To solve for these issues, businesses should invest in “collective intelligence”-based programs. Collective intelligence occurs when businesses create opportunities for employees to source knowledge and learn from each other, leveraging what people know, and what they get exposed to collectively. Then, they must work together in teams including experts in varied disciplines, who now have a better chance to understand each other and collaborate more productively.

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Sound abstract? It’s actually a very practical concept with a clear path to execution. Business leaders should start by identifying learning networks in the organization where the central nodes are experts (the masters) and the branches around them are the learners (the apprentices). Then activate it, almost like a brain’s neural network connects across the parts of the brain.

One possible activation is to establish master-apprentice groups, so that trainees can learn firsthand from experts and apply new skills to actual project and organizational needs. This group-based approach harnesses the collective intelligence of people who work together, matching different skills and knowledge sets of internal experts to address future project needs. Internal experts help curate and, more importantly, contextualize relevant knowledge for others on the team, promoting natural learning and encouraging a more productive information flow. The result, in MIT’s words, is a “supermind,” a combination of people’s knowledge, and machines—which can help curate and transfer that knowledge.

Innovation through collaboration

When employees are trained through collective intelligence-based programs, collaboration becomes the main source of usable new knowledge, laying the groundwork for innovation and transformation. For instance, NLP coders who collaborate with process reengineering experts, and insurance claims domain experts end up creating artifacts—contextualized pieces of knowledge that make learning easier for professionals. Teams can use this shared intelligence to train more of their respective colleagues, who in turn will be able to interoperate better, hence perpetuating the cycle of new knowledge formation—which is so critical when knowledge changes so fast.

At a time when every business must adapt to evolving customer demands and new technologies, most workers will be involved in transformation cycles that require close collaboration with peers. The ability to work with peers toward a common goal is so important, in fact, that survey respondents in LinkedIn’s 2018 Workplace Learning Report ranked leadership, communication, and collaboration as more important than any role-specific skills.

Obviously, the hard skills are still a priority, but innovation relies on an interplay of people with many different strengths and skill sets. Most workers don’t need to be an expert in any of the new disciplines—from Python to NLP, robotic process automation to computer vision, or Agile methodology to design thinking—but they do need to understand enough about them to collaborate with others who are experts.

Whether Amazon or a five-person startup, businesses investing heavily in training programs that occur in isolation are not setting up their people, or their business, for success in the digital age.

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A “learning supermind” is a better way to upskill and continuously embrace new knowledge, especially as the lifespan of skills continues to shorten. By adopting a group-focused approach to learning that leverages the knowledge an organization already has and encourages collaboration, leaders can build an adaptive organization where individuals and teams innovate better and faster than ever before.


Gianni Giacomelli is chief innovation leader at Genpact, a global professional services firm focused on delivering digital transformation. He also is head of innovation design at Massachusetts Institute of Technology’s Collective Intelligence Design Lab.

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