The future of learning is not in the classroom, it’s in the field

The future of learning is not in the classroom, it’s in the field

With talk about artificial intelligence (AI) and robotics facilitating a growing number of tasks and replacing jobs, there needs to be a new way of thinking about the future of learning, say two leading educators in the U.S.

Today’s AI-enabled, information-rich tools are increasingly able to handle jobs that in the past have been exclusively done by people— like tax returns, language translations, accounting, even some kinds of surgery. These shifts will produce massive disruptions to employment and hold enormous implications for business leaders and policy makers.

Writing in an open letter in the McKinsey Quarterly, Amy Edmondson, the Novartis Professor of leadership and management at Harvard Business School, and Bror Saxberg, vice president of learning science at the Chan Zuckerberg Initiative, explain why there needs to be a new emphasis on lifelong learning in the future. They say that the future of learning is not in the classroom. It’s in the field—finding ways to do better while doing the work, and that this won’t happen by chance.

Organization leaders need to model learning behaviors and invest in the development of learning processes and tools. There is simply no room for arrogance in a highly dynamic and uncertain world. They also need to create a psychologically safe environment in which people feel comfortable taking the risks that come with experimentation and practice; giving and receiving candid feedback; asking questions; and acknowledging failures. Learning must be built into every aspect of the organization.

It’s not just about the hard skills, it’s the soft skills too

Edmondson and Saxberg say, “When we talk about learning, the emphasis is often on ‘hard’ skills, such as coding, analytics, and data science. While these skills will be critical, they are only part of the story. The dynamics we described at the outset, in which information-rich tools become ubiquitous and people are a differentiator, paradoxically, increase the importance of such ‘soft’ attributes as collaboration, empathy, and meaning making.”


In most organizations, teamwork will be more important and valuable than ever. In both scientific discovery and commercial innovation, for example, the size of innovating teams has grown larger and the skills brought together are more diverse than ever. This is because, as knowledge expands, expertise both deepens and narrows—necessitating collaboration across fields to produce great results.

In a way that would have seemed far-fetched 20 years ago, building a car requires integrating cross-disciplinary expertise in artificial intelligence, computer science, advanced lighting, and materials, in addition to the classic automotive-engineering disciplines of design and manufacturing. Or consider the rescue of the Chilean miners in 2010. The miners themselves formed an extraordinary team to support their mutual survival. But they also needed the cross-disciplinary expertise of the team of above-ground rescuers who integrated expertise from geologists, engineers, physicians, and naval special forces.

Teamwork doesn’t necessarily mean collaborating within teams in the classic sense of bounded groups of people working together on specific tasks. Instead, it’s often about teaming—communicating and collaborating with people across boundaries, such as expertise or distance, spontaneously and continuously. People need to have, or develop, the skills for effective teamwork.


Global marketplaces can threaten the ability to spontaneously empathize, especially when we cannot see other people’s faces—for example, in geographically dispersed workforces or through remote service encounters. Genuine human connections can be made, and broken, quickly. Customers and employees alike feel deep loyalty to organizations that treat them with respect.

To some extent, empathy can be taught—through perspective-taking exercises and through quick but profound exchanges between people. For that to happen, leaders at all levels have to be engaged and model the right behavior.

The authors say, “Observe your customers and how they interact with your company. Use design-thinking tools such as empathy maps as a starting point for conceiving new products and features and for identifying customer pain points. In an era of customization, empathy matters more because it requires putting yourself in the minds of many different kinds of customers, not just the familiar ones for whom a product or service was designed.”

Meaning making

Meaning making in the AI era starts with an appreciation of what machines can and cannot do. It may be possible, for example, for a machine to make certain kinds of diagnoses more accurately than a person can. But it will be up to nurses, doctors, and therapists to help patients understand the implications and manage the consequences. It’s the difference between knowledge and meaning.

The search for meaning informs many kinds of decisions: it could be a work challenge overcome, a way to advance a career, a resolution to a personal issue, or matters related to health and wellness. As information-rich tools help provide better solutions to complex situations, organizations will need to understand what matters for each person. Meaningfully connecting decisions, even those made by algorithms, to individual circumstances is likely to be the work of skilled people for a long time to come—if we prepare our organizations to think like this.

Edmondson and Saxberg are members of the Consortium for Advancing Adult Learning & Development (CAALD), convened by McKinsey & Company. To read the full letter, click here.

[Image: The Association for Talent Development]

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