HENT212 Designing Social Enterprise

today’s transition to a workforce of the future will require low- and basic-skilled workers to face a far steeper learning curve to remain productive. This learning curve will be particularly challenging for older workers as strong problem solving skills are needed in the computer and software fields. As a result, an additional societal cost is at play here that will need to be shared by firms and governments: that of education and training that focuses on older workers.

What in the past was considered premium will now be considered standard, a phenomenon that will necessarily push human workers out from lower-value labor in to more value-added services. For example, plant and machine operators could become specialty technicians and robotics supervisors. Companies, therefore, have much to benefit from by investing in the productivity gains from automation in their human workers, particularly older ones.

Unfortunately, we tend to conflate proposals for retraining older workers with the ways in which we prepare young high school graduates for direct entry into the workforce. Apprenticeships or technical high school programs in fields like computers and software or specialized skills like aviation may be a natural path for non-college-bound students, but older workers who have been in the workforce for many years may need a different approach.

Older workers who are suddenly displaced often have little interest in extended programs. The people we talked to often had family responsibilities and were focused on quickly replacing their lost income streams.

older workers “are willing to take a 16-week course” — but that’s about it. Older students also vastly prefer learning in a work setting rather than in a classroom.

This learning strategy focuses on building core capabilities and then layering on additional skills in steps.

This is consistent with a “just in time” approach to training, available when an individual needs it throughout his or her career, not just at the beginning.

Why don’t we have community colleges and employers working together to set a curriculum up?” The key to designing effective programs is to understand local needs and connect them to federal and other support. While federal programs can provide the ingredients, a local leader on the ground needs to match the resources to the needs of employers and retrainee candidates in their area.

Redefine the workweek.

Reimagine the workplace.

Mind the mix.

Each generation – including older, experienced workers – has different ways of working and putting their skills to use. Mentoring and reverse mentoring can boost morale and productivity. Older workers can bring institutional knowledge and perspective, social maturity and stability, and can pass on critical knowledge or business relationships to younger workers. Younger workers can bring a more collaborative mindset and can help older workers to become more digitally literate.

reorientation, recuperation, and repurposing as individuals ramp up and ramp down their work commitments will all be necessary in a multistage life.

MetLife identifies five factors that organizations should consider in workforce segmentation: Demographics such as age, gender, income, education, and life events Firmographics such as job tenure, company size, industry, role, and blue vs. white collar Attitudes toward life such as optimism toward the future, future vs. present orientation, orientation toward change, and sources of pleasure/stress Attitudes toward work such as work-life balance, attitudes toward retirement, and motivations for staying at the job Needs from employer such as career development support, salary, benefits, work culture, and work subject matter

we recommended that organizations find ways to retain mature talent that create value for the organization without creating a “gray ceiling” that limits advancement opportunities for younger workers.

organizations should reconsider workforce strategies and programs based solely on age or generation and instead consider workers’ interests, values, preferences, and opinions to better tailor opportunities to workers’ individual attributes.

New technologies and techniques for collecting and analyzing workforce data will be critical to understanding distinct employee archetypes. A global energy company provides an example of how advanced analytics can help organizations segment the workforce in different and productive ways. The company used unsupervised learning algorithms to identify unique segments in its leadership population, looking for meaningful clusters (such as leaders with deep networks, high adaptability, or strong analytical capability) that cross-cut demographic categories. The company then compared these clusters to its high-potential leadership pipeline to determine whether certain geographies or employee cohorts were underrepresented in its leadership programs.

To that end, governments might consider providing vouchers that workers can use to pay for training that will support progress in multiple jobs, rather than focusing on specific job- or company-related skills.