Building a Resilient Systems Engineering Workforce with Knowledge Intelligence Transduction (KIT)

With any mature systems engineering approach, a defined problem statement is required. The present workplace and workforce are continuously evolving. Prior to the COVID pandemic, workforce turnover averaged a mean of 1.8 years. With the prevalence of COVID-19 and restrictions, more than 20 million jobs have turned to fully remote work. “Between 2019 and 2021, the number of people primarily working from home tripled from 5.7% (roughly 9 million people) to 17.9% (27.6 million people), according to new 2021 American Community Survey (ACS) 1-year estimates released today by the U.S. Census Bureau.” 1 According to the U.S. Bureau of Labor Statistics, the average employee turnover rate in 2021 was 47.2%.2 After COVID-19, 92% of people surveyed expect to work from home at least 1 day per week and 80% expected to work at least 3 days from home per week.3 Forty-seven percent of millennials are planning to leave their jobs within 2 years, and Gen Zers report a comparable number. With high turnover (e.g., the so-called “Great Resignation” with 47 million Americans voluntarily quitting in 20216 and an estimated 48 million in 202216) and isolated employees, finding good one-on-one mentors for employees is increasingly difficult. Wisdom gained from years of experience from senior mentors in a specific field is often not transferred and so is lost when the older employees retire. This is particularly true of our critical utilities, construction, and transportation infrastructures. Consequently, it is imperative to find a way to capture historical systems engineering lessons learned and enhance the knowledge of current and future employees. Henry Ford quoted, “The philosophy of life indicates that our principal business on this planet is the gaining of experience.”17 Thirty-seven percent of businesses and organizations currently employ artificial intelligence (AI). Research suggests that AI has the potential to boost employee productivity by approximately 40% by 2035. Ninety percent of data is unstructured, meaning that without technology to process the big data, companies are unable to focus on important data points. The direct motivation for this chapter is combining traditional systems engineering with cognitive science, meta-analytics, meta-algorithmics, and AI results in new SE constructs, focused around metacognition. The concept to aid with this introduced in the chapter is known as “Knowledge Intelligence Transduction” (KIT). This special issue will focus on the KIT concept and how it can be used to positively impact the future workforce and build resiliency of knowledge across generations and challenges.