Work is constantly changing, but technological and social changes accelerate certain aspects of work. Working from anywhere has exploded since the start of the coronavirus pandemic and does not seem to disappear. The digital workplace requires unique skills to collaborate in distributed teams and cooperate in knowledge networks.
The most recent technology to influence the way in which work is done is artificial intelligence – in particular multimodal models of large generative language (GLM). The rate at which these new technologies are integrated requires an agile creation of workers adapting to the human work interface of human machine. It is very likely that the pace of change will continue and accelerate even.
Although we cannot predict the future of work or know how GLM develops, we can assess the human meta-skills necessary to understand individually and collectively work with smart machines. There are three meta-skills who can help us adapt to a future of work with smart machines.
- Learn “how” – curiosity and resolution to solve problems
- Adapt to change – Creation of agile meaning
- Collaboration and cooperation – Knowledge sharing
Learn “how” – not “what”
To learn to learn is to give meaning to the environment and to use this knowledge to act.
We learn experiences and exhibition to people and ideas. Social networks can be inspired, but the creation of meaning requires resolution to solve problems. This means the integration of learning and work.
Going from learning to work is a continuous process on a daily basis. Everyone must seek to understand their environment, seek and give meaning to new ideas, give meaning to practical experience and share new practices – continuously. We learn from our teams, our communities and our knowledge networks.
A basic competence is curiosity. Curiosity for ideas can promote creativity, while curiosity for people can develop empathy (not sympathy). We get new ideas for new people, not the same people we see every day. We obtain new perspectives from people whose life and experiences are different from ours. We cannot be empathetic for others unless we are first curious about them. We can only be creative if we are first curious to learn new ideas.
The networks are made up of nodes (people) and relationships between them. Curiosity and learning can create new connections between people and ideas. The constant learning of fractal beings can constitute more resilient knowledge networks.
Curiosity gives an overview.
It starts with Curiosity and humility.
Change
A lot of work today is in a state of perpetual beta – Adapt to constant change while getting things done.
The human work that emerges from the increase in automation is complex and creative. In complex environments, emerging practices must be developed while simultaneously engaging the problem. Social learning is the best way for groups of people to cooperate and learn with each other. While discourse increases formal training, social learning in knowledge networks becomes an essential competence in order to adapt to a changing work environment.
New methods and practices – often “just good enough” – must be developed, used, modified and ultimately rejected as the nature of the work changes. The only way to stay ahead of the machines will be to use our unique human capacities. In addition, people will have to understand how machines and algorithms work, to ensure real human surveillance.
Develop the skills of a knowledge craftsman In all areas of work, are essential for success. Although doing work in collaboration will continue to be important in all organizations, this will not be enough. New ideas will have to come from external professional networks in order to follow the pace of innovation and change in all areas.
Sure places are needed to connect new ideas to work to do – communities. The need for practice communities continues to develop while knowledge craftsmen are looking for places to integrate their work and learning in a space of trust. While the concert economy dominates, the communities of practice can bring a certain stability to our professional development. These belong to the practitioners themselves.
The creation of agile meaning could be described as our sense of complex challenges by interacting with others and sharing knowledge. The more diverse and open knowledge flows allow faster meaning.
Collaboration and cooperation
Work in networks requires skills different from those of directed hierarchies. Cooperation is a fundamental behavior to work effectively in networks, and it is in networks where most of us will work, if we are not already. Cooperation presupposes the freedom of individuals to join and participate, so that the people of the network cannot be informed of what to do, only influenced. If they don't like you, they will not connect. A lonely node is of little value for the network. In a rigid hierarchy, you just have to please your boss. In a network, you must be considered as having a certain value, but not the same value, by many others.
Cooperation is not the same as collaboration, although they are complementary. Collaboration requires a common objective while cooperation shares without any specific objective. Teams, groups and markets collaborate. Online social networks and practice communities cooperate. The work in cooperation requires a different state of mind from simple collaboration on a defined project. Being cooperative means being open to others outside your group.
Effective knowledge networks are made up of unique people working on common challenges, together for a discreet period of time before the network is concentrated again. We must go from a “single size” attitude on work and learning to a “everyone is unique” perspective. The network allows endless combinations between unique nodes.
This connectivity already leads to an increasing number of discoveries of non -traditional areas, as we have seen in the rapid development of vaccines during this pandemic. In a network workplace, where everyone is unique, there is a decreasing need for generic work processes (jobs, roles, professions) and for standard programs.
At Learnopoly, Finn has championed a mission to deliver unbiased, in-depth reviews of online courses that empower learners to make well-informed decisions. With over a decade of experience in financial services, he has honed his expertise in strategic partnerships and business development, cultivating both a sharp analytical perspective and a collaborative spirit. A lifelong learner, Finn’s commitment to creating a trusted guide for online education was ignited by a frustrating encounter with biased course reviews.