Master and apprentice: Ensuring skills transfer, development and upgrades during digital transformation
While DT will enable workers to do many new, exciting things, current developments point toward a troubling trend. AI is disrupting how people learn and develop skills at work, on-the-job learning/training (OJL/T), and not positively. Organizations are prioritizing productivity at the cost of training and skills transfer. According to research into 28 different fields by Dr. Matt Beane, organizations are sacrificing skills for productivity with disastrous consequences for skills development and transfer.[60] It did not matter if the industry in question consisted of high-skilled or low-skilled work (see figure 8 for an overview of the 28 sectors analyzed).
This development will compound the FM industry's complex succession planning and recruitment picture. As many as 40 percent of facility managers in high-income countries will retire by 2026.[61] Facility managers must learn from the experiences of other industries and integrate DT without negatively impacting knowledge and skills transfer to the next generation.
DT has created an “automation paradox” in many industries. Human operators’ contributions become more crucial as systems become more automated, reliable and advanced. Systems will encounter unexpected circumstances that fall outside design parameters or fail in some way. When those moments occur, the operator must take over and bring their creativity and problem-solving to bear. These reasons are why surgeons are still required in increasingly automated surgical suites and airline pilots in cockpits, although autopilots are ubiquitous.[62] The challenge for facility managers will be to ensure that they have the skills and competencies available to “take control” when the unexpected occurs.
• Policing
• Higher education
• Investment banking
• Online labor platforms
• Chip design
• Journalism
• Data science
• Criminal justice
• Neonatal intensive care
• Public Education
• Music composition
• Robotics
• Open innovation
• Aerospace engineering
• Ridesharing
• Long-haul trucking
• Bomb disposal
• Drone piloting
• Foodservice
• Secondary fulfillment
• Radiology
• Construction
• Wealth management
• Retail
• Automotive engineering
• Call center operations
• Law
• Surgery
Figure 8 AI disrupts learning and knowledge and skills transfer in 28 industries (Source: Beane, 2022).
• Policing
• Higher education
• Investment banking
• Online labor platforms
• Chip design
• Journalism
• Data science
• Criminal justice
• Neonatal intensive care
• Public Education
• Music composition
• Robotics
• Open innovation
• Aerospace engineering
• Ridesharing
• Long-haul trucking
• Bomb disposal
• Drone piloting
• Foodservice
• Secondary fulfillment
• Radiology
• Construction
• Wealth management
• Retail
• Automotive engineering
• Call center operations
• Law
• Surgery
Figure 8 AI disrupts learning and knowledge and skills transfer in 28 industries (Source: Beane, 2022).
Organizations’ deployment of technologies “moves trainees away from learning opportunities and experts away from the action, and overloads both with a mandate to master old and new methods simultaneously.” Learners have a lower understanding of the complexities involved in their jobs, and they are not forming strong connections with experts/mentors that could help them progress.
While human operators are critical collaborators with automated systems, organizations are deploying workers with computerized systems in ways that promote productivity over learning and skills development. The extreme focus on productivity means that the skilled worker uses new technologies to augment their performance at the cost of learning opportunities for new and younger workers. Inexperienced workers are often turned into spectators when they should be learning on the job.[64]
Why is this a problem? Digital technologies are automating millions of jobs and will be creating millions more. As a result, workers will lose jobs and will have to learn skills for new ones. According to McKinsey & Company, up to 1 billion jobs worldwide will be affected or reconfigured by 2030, and the skills gap costs global manufacturing US$1 trillion.[65] Despite international organizations spending US$370 billion on educational programs,[66] most people do not learn the skills for their jobs through formal education. Formal education is good for acquiring basic knowledge, not developing skills. Only 1 in 5 people learn new skills this way, and everyone else acquires new skills through practical application and the process of seeing one, doing one, teaching one.[67]
Most OJT/L occurs through the following process. First, we watch an expert do a job. Then, we get to try our hand at more manageable tasks. Once we have demonstrated proficiency with a given task, we can complete more challenging assignments “under close supervision until we become experts ourselves.” This process is also called apprenticeship or mentorship.[68]
This method has worked for thousands of years because we are:
- Challenging ourselves by working at the edge of our capabilities.
- Getting a better understanding of the complexity surrounding our jobs.
- Developing connections where we share information and building trust.
Organizations’ deployment of technologies “moves trainees away from learning opportunities and experts away from the action, and overloads both with a mandate to master old and new methods simultaneously.”[69] Organizations are, in effect, instituting barriers to learning by separating learners from experts. They separate learners from tasks that challenge them and foster learning. As a result, they have a lower understanding of the complexities involved in their jobs, and they are not forming strong connections with experts/mentors that could help them progress.
“Shadow learners risk their jobs and status to learn new skills.”
– Matt Beane, Assistant Professor, University of California Santa Barbara and Fellow, MIT Institute for Digital Economy
There is a small group of learners who do not take this development sitting down. These individuals break rules and processes, risking their jobs and careers, to develop new skills. They are creating alternative approaches for learning new skills called shadow learning that come at personal and organizational risk and have negative consequences.[70]
Organizations should ask themselves: How can we handle technology in a way that gets results and builds capacity for our users?[71] Researchers have identified four classes of behavior that shadow learners exhibit that organizations within the FM industry can use to foster learning and skills development in the industry: “seeking struggle, tapping frontline know-how, redesigning roles and curating solutions.”[72] The following are some recommendations:
- Identify the domain and business problems to address as well as establish the business drivers for investments
- Design the rollout of new technologies so that learners have opportunities to struggle near the edge of their capacity in real work so they can make and learn from their mistakes
- Identify the best frontline workers and provide them opportunities and a framework to disseminate their practices to the broader organization or across the industry
- Ensure that roles and incentives restructure roles and incentives to help learners master new ways of working with intelligent machines
- Establish “searchable, annotated, crowdsourced” skill repositories containing tools and expert guidance that learners can tap and contribute to as needed
The specific approach to these activities depends on organizational structure, culture, resources, technological options, existing skills and, of course, the nature of the work itself.[73] If the FM industry cannot address these problems, replacements will not only be difficult to find, but they will also not want to stay in an industry where they have few opportunities to develop. The FM industry will draw talent from a smaller and shallower pool.[74]
[64] Matt Beane (2019) “Learning to Work with Intelligent Machines,” Harvard Business Review (September – October). https://hbr.org/2019/09/learning-to-work-with-intelligent-machines
[65] Matt Beane (2022) “Master & Apprentice: How we’re sacrificing skill for productivity and what we can do about it,” IFMA Leading the Digital Transformation Executive Summit.
[66] Training Industry (2021) Size of the Training Industry (29 March) https://trainingindustry.com/wiki/learning-services-and-outsourcing/size-of-training-industry/
[67] Matt Beane (2022) “Master & Apprentice: How we’re sacrificing skill for productivity and what we can do about it,” IFMA Leading the Digital Transformation Executive Summit.
[68] Matt Beane (2019) “Learning to Work with Intelligent Machines,” Harvard Business Review (September – October). https://hbr.org/2019/09/learning-to-work-with-intelligent-machines
[69] Matt Beane (2019) “Learning to Work with Intelligent Machines,” Harvard Business Review (September – October). https://hbr.org/2019/09/learning-to-work-with-intelligent-machines
[70] Matt Beane (2022) “Master & Apprentice: How we’re sacrificing skill for productivity and what we can do about it,” IFMA Leading the Digital Transformation Executive Summit.
[71] Matt Beane (2022) “Master & Apprentice: How we’re sacrificing skill for productivity and what we can do about it,” IFMA Leading the Digital Transformation Executive Summit.
[72] Matt Beane (2019) “Learning to Work with Intelligent Machines,” Harvard Business Review (September – October). https://hbr.org/2019/09/learning-to-work-with-intelligent-machines
[73 Matt Beane (2019) “Learning to Work with Intelligent Machines,” Harvard Business Review (September – October). https://hbr.org/2019/09/learning-to-work-with-intelligent-machines
[74] Arc (2018) ”Why Facilities Managers Should Adopt a Multi-generational Staffing Strategy.”