Automation has made a lot of progress, in the physical world with robotics as well as more recently in the virtual world of the services professions and industries. There are a lot of predictions about how automation will impact the white-collar workforce. Here are four changes I see developing the next decade and a half:
- The Gig Economy flourishes. A larger part of the workforce will be contractors rather than employees. This will either be on a time- or work-product basis. The movie industry was an early model when it replaced the old studio “factory” system in the 1960s by hiring writers, directors, actors and others on a contract basis for a specific production.
- BYOT – bring your own technology/tools. Choices on tools and platforms will increasingly be left to the worker, as applications become more interoperable and decoupled from data.
- Eighty percent of service-sector jobs will be automated. Four major types of work subject to automation are discussed below.
- The remaining 20 percent will grow by more than fivefold. History shows that new, smart work will be leading this charge – but retooling and training will be needed.
While services automation will affect a huge number of people, the need will grow for skilled workers to manage the automation. Let’s discuss the types of jobs that will become automated and the workforce required to support them.
Here are the four areas where I see the greatest potential for automation:
- Repetitive tasks. Much has already happened in this area, of course. For example, “new client onboarding” is typically fully automated by today’s popular SaaS solutions. But in many corporations this is still done by knowledge workers. It can be fully or 99-percent automated.
- Research tasks to support activities. Machine learning combined with appropriate data capturing will allow many research tasks to be automated. A good example is tax return preparation, in which machine learning can suggest choices and indicate best answers. Another example would be legal discovery, which would reduce the many hours lawyers currently spend reviewing documents.
- Judgment-supported knowledge work. This is the next step in the research process, in which professionals form opinions on discovered data. These tasks will also become predictive based on past analysis, and will allow for automation. Currently, tasks involving judgments are done by lawyers, accountants, doctors and other professionals. Not all are fully automatable, but they can at least be guided.
- Complex knowledge work. This will be the last significant frontier and will require a more integrated implementation. Complex work combines repetitive tasks, research and judgment. Certain aspects of these processes will be automated—ultimately reducing the complexity and creating the automation. An example: an analyst supporting a private-equity firm’s transactions, applying her own judgment after viewing automated investment models that combine historical data and real-time market conditions.
The remaining 20 percent of the workforce will transition to higher-skilled positions. Granted, we will see a need for training and retooling but, in the end, these workers will have better focused and more satisfying positions. Here are the some examples of these jobs:
- Sales and other prospect and customer contacts. The value of the personal touch will only grow in the highly automated world.
- Data and technology. This automation will be highly dependent on efficient and effective capabilities in these areas.
- Human Resources. Unless already experienced with the gig economy, a new set of skills and processes are needed.
- Work planning, assignments, tracking and reporting. High velocity work, with contract resources and workflow automation will be the core platform.
- Strategy and organizational design. Careful and precise planning will be needed on a continuous basis or organizations will lag in the automated world. See my colleague Jonathan Murray’s blog on the composable enterprise.
- Professional services. These will focus on two areas: a) planning and prevention (e.g., Medical), and b) developing and delivering composable implementation teams.
How do you see automation reshaping your own organization?