The impact of changes to the way we work

Many of us talk about changing work styles and changes in the way people work. Behind this conversation there are real trends and stark predictions for the work force of tomorrow. Let me start by considering process work versus knowledge work (or as the figure below shows it routine versus non-routine). Source : (Stefania Albanesi et al, 2013):

Routine versus Non-Routine Work

The data from the US is stark. There is an almost constant move over 30 years from routine to non-routine work-styles. The change is almost linear except during recessions when it accelerates even more (recessions are shown by the grey bars). The impact on workplace services is that we need to support more workers who work outside of processes than we do those working within processes. Non-routine task workers and knowledge workers will increase and services will need to support this change. The chart though does show %’s rather than absolute numbers and one of the concerns with the data is that other factors will impact the number of people employed. The new emerging field of smart machines is one such area of impact. Gartner has some key strategic assumptions:

• By 2020, non-routine work will account for more than 65% of U.S. jobs (up from 60% in 2013).
  
• By 2020, the careers of a majority of people primarily engaged in non-routine work will be disrupted by smart machines in both positive and negative ways. Source : (Austin, 2014 < Gartner account required or you can read a free and publicly available summary on Tom Austin’s blog)
  

Smart Machines and their impact on workers

By smart what do we mean? Smart machines can help professionals do more; keeping them abreast of advancement in their field while allowing them to focus on their professional practice. Smart machines can incorporate new levels of automation, for example self-driving vehicles. Smart machines can also learn and advise. Austin et al predict that by 2017 10% of computers will be learning rather than processing. This will have a massive impact on the services revenues of traditional IT organizations, for example in the same report from Austin et al they focus on predictions from Credit Agricole that IBM revenues derived from Watson will grow from 1.5% by the end of 2015 to 10% by the end of 2018. Individuals and organizations will look for benefits realization from smart machine investments. Watson is already attracting more cognitive computing use cases and a recent IBM competition attracted 400 business concepts with the winners focusing on better personalized health recommendations, intelligent personalized education experiences and a sales enablement tool which identifies customers buying habits. (IBM, 2014).

As work is becoming non-routine other trends are impacting the way work is performed. Increased support for non-routine work is coming in the form of automated information based activities and virtual personal assistants. Virtual Personal Assistants are software on smartphones such as Siri (iPhone), Cortana (Windows Phone) and Google Now (Android). Through these smart things Gartner predict that 51% of US knowledge workers will be impacted with 17% losing their jobs by 2020 through a combination of smart machines and virtual personal assistants.

We can expect:

• Individuals will seek to differentiate themselves from their peers and improve their productivity through the use of virtual personal assistants and other smart data analysis services. In doing so to gain maximum value from those service more data will be exposed them; so for example are enterprises ready for the expectation of allowing Siri, GoogleNow, Cortana etc. access to corporate contacts and calendar information? BYO will be significant to the adoption and exploitation of virtual personal assistants.
  
• Enterprises will seek to support decisions with smart machines that analyse vast amounts of data and make recommendations based on that data.
  
• Data Security and Privacy issues will abound in the coming years as employees seek to exploit virtual personal assistants before either enterprise policies or technology is ready for users to expose more data to their devices and the public cloud in order to gain maximum benefit to their productivity.
  
• Unrest is likely. As more and more routine jobs are automated and smart machines remove even some non-routine roles society will have to cope with an increasingly polarized population.
  

So let us hope the education systems are readying tomorrow’s workforce for this.