Predictions involving the impact of automation and artificial intelligence on the workforce of the near future all reach the same consensus: these forces will profoundly alter the employment landscape that current college and university students will have to negotiate after graduation. For example, a two-year study from McKinsey Global Institute (2017) suggests that by 2030, intelligent agents and robots could eliminate as much as 30 percent of the world's human labor. This study implies that the automation revolution could rival in its impact on American workers the move away from agricultural labor during the 1900s in the United States, and more recently, the explosion of the Chinese labor economy and loss of American manufacturing jobs. Depending upon the speed and scale of adoption, automation could displace between 400 and 800 million jobs by 2030, requiring as many as 375 million people to switch job categories entirely.

While job loss to automation might impact those with college degrees, most of the impact is predicted to fall upon blue collar occupations. Of more relevance to this QEP is the other predicted effect of the increased use of automation or Machine Learning/Artificial Intelligence: employers restructuring jobs for which AI and Machine Learning can take over some tasks, and having employees spend more time with higher-level tasks. In a recent conference paper, Erik Brynjolfsson and Daniel Rock, with MIT, and Tom Mitchell, of Carnegie Mellon University, (2018) pointed out that the impact of Machine Learning, the self-programming, self-adjusting core of AI, on current employment will result not so much in job loss as job transformation. "ML will affect very different parts of the workforce than earlier waves of automation." Instead, they predict, automation will occur on a task-by-task basis. "Tasks within jobs typically show considerable variability in 'suitability for machine learning' while few—if any—jobs can be fully automated using machine learning . . . .Machine learning technology can transform many jobs in the economy, but full automation will be less significant than the re-engineering of processes and the reorganization of tasks." James Manyika and Kevin Sneader, writing for the McKinsey Global Institute (2018), analyzed more than 2000 work activities across more than 800 occupations, and concurred with this prediction. They noted that "certain categories of activities are more easily automatable than others. They include physical activities in highly predictable and structured environments, as well as data collection and data processing. These account for roughly half of the activities that people do across all sectors." It is not so much entire jobs that will be automated, but activities that are currently seen as a part of those jobs. Manyika and Sneader found "that about 30 percent of the activities in 60 percent of all occupations could be automated. This means that most workers—from welders to mortgage brokers to CEOs—will work alongside rapidly evolving machines. The nature of these occupations will likely change as a result." This points to the need for career preparation that focuses on the skills that will not be among those made obsolete by Machine Learning or automation.

If we accept these predictions that Machine Learning and Artificial Intelligence can take over limited tasks, we must accept that there will be tasks that simply cannot be replicated by ML/AI or automation; it is these skills that Joseph Aoun (2017) has termed "robot-proof," and that are at the heart of this QEP. There is ample research to suggest that Aoun is correct in his assumptions about some skills being "robot-proof," and that these skills should therefore be the focus of higher education. For example, Stanford professor Andrew Ng, writing for the Harvard Business Review (2016), articulated a good rule of thumb for determining which types of jobs are ripe for automation: "If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future," putting higher-order skills beyond the reach of AI. As Bernard Marr, writing for Forbes, (2018) has observed,