What are your assumptions about the future of work?
Back in February 2015, when we were at work on our first ideas about skill management and how a shared understanding of skills was core to the future of work, we set out our critical assumptions about the future of work. Assumptions should change as the world evolves. So we recently reviewed our basic operating assumptions and got input from experts in the field like Chuck Hamilton.
Here are our current beliefs. We use these to shape the design of TeamFit and how we work with our customers. Do you think we have these right?
What assumptions are you making as you build out your own business?How do your assumptions shape your strategy?
Assumptions about the world we play in
(1) Knowledge work is moving to a contingent economy in which more people will work in an on demand mode. (Relationships between individuals and small pods that work closely together over time thereby become more important.)
(2) Teams are at the center of work. Teams are more than the sum of the individuals that comprise them. Teams are increasingly dynamic and hybrid (combine people from more than one organization or with more than one working arrangement). Teams need social and collaboration skills in order to perform at the highest level.
(3) It is hard and will get harder to find people with the skills needed that will succeed as a team.
(4) Data about people’s work experience is valuable and meaningful over the course of a lifetime. Its relevance is not limited to a single organization or engagement. Giving the individual control of this information will be of value to both individuals and the organizations they work with.
(5) Lifelong learning will be the norm. Developing new skills is critical to all of our success. We all need to develop the foundational skills that support new skill development and to understand the potential skills that we could develop.
Assumptions behind what TeamFit is doing
(1) Understanding individual, team and organizational skills can help to improve performance, expand potential and increase intrinsic value.
(2) Better measures of skills are possible by mashing up many types of data including data from enterprise social systems, social software, project records, HRIS systems, learning and talent management systems, certifications, formal training, patents and publications and social skill rankings.
(3) We can gather enough data about skills, roles, projects, teams and outcomes, and the connections between them, to provide actionable insights:
• See skill gaps
• Find hidden pockets of potential at the individual, team and organizational level
• Predict project success based on the skills and track record of the team
• Recommend the additional skills needed to improve skill acquisition and for better collaboration
(4) We will be able to predict:
• What skills a person/team/organization has
• What skills a person/team/organization could have
• What skills/roles/people an organization will need in the future
• What skills will emerge as important in the future (as core or differentiating)
• What teams will make projects successful
An open question that we are investigating is the impact of artificial intelligence on knowledge work. We are seeing this emerge as a question at several of our professional services clients.Back in the 1980s, when machine translation was a hot topic in AI, we used to say that there is no machine translation. There is human aided machine translation and machine aided human translation and both will have a role to play. The same is true of many other knowledge jobs today.
Human Aided Machine Work - Most Routine Knowledge Work
Most routine work, from conducting audits, to drafting contracts based on established business principles, to the iteration of designs based on accepted patterns will all be done by robots (or agents). This includes things like optimizing allocations and team assignments.
Machine Aided Human Work - Most Creative Work
Most creative work, from business strategy to design, to authoring will be done with the help of artificial intelligence software. The agent or robot will be part of the team (we will be adding skill profiles of bots and agents to TeamFit, probably as early as 2018).
What will be left for humans to do on their own? Not a lot. We will all become deeply immersed in webs of data that we interpret with our AIs or which AIs analyse for us. This is why data ownership will be so important, and why we should be designing systems where people can own their own data. See Ownership of data in a collaborative age: Three unworkable approaches and a way forward.
