Before the onset of the new machine age, the average job was stable, linear, and singular. People commonly chose one career path and pursed it over the course of their entire lives, from education through to retirement—the skills acquired through post-secondary education and at-work training sufficiently ensuring them career success. However, the rise of automation and AI has raised questions about the employable skills, attitudes, and behaviors required for people to participate in the future of work. In reality, many jobs will be substantially altered, if not completely replaced, by AI-driven machines in the coming decade. However, on the flip side, many new jobs (some that still seem like science fiction today) will also be created. As a result of these changes, employable skills are now like mobile apps that need frequent upgrades and these skills will continue to evolve more rapidly in the future than they do now.
In order to accommodate such an accelerating pace of technological change, there will be a need for continuous, uninterrupted, and rapid reskilling and upskilling of both students and employees to reliably prepare the workforce for the transformations ahead as our old-school, traditional methods are no longer fit for the challenge. For example, one-and-done training and/or “check-the-box activity” models are no longer sufficient to meet the demands of the digital economy. The new world of work demands that our roles are continually augmented and the concept of “learning while working and working while learning” will continue to become the new normal. Consequently, businesses will have to adapt to frequently reskilling the workforce for future work, whether this means reskilling employees every one to two months, fortnightly, or even continuously. This process will require the reprioritization of investments and workforce management strategies and as a result, companies will need to develop a new approach towards learning, one that is dynamic and continuous.
An interesting example of this process in action is Singapore’s SkillsFuture initiative, which is aimed at helping people develop new skills in addition to their existing skillset in order to foster career resilience or help establish a new career path. Adults over the age of 25 are given a credit of $350 to pay for any training courses provided by 500 approved providers, including universities and MOOCs. The credit does not expire, and the government also provides periodic supplements, so individuals may accumulate credit over time.
With this SkillsFuture example in mind, in order to make continuous reskilling a reality, I believe we must reboot three core elements of learning:
- Skill identification: From intuition to data driven. In the future, data will serve as a foundation for accelerating the speed of reskilling and will be dependent on how quickly we collect skill identification data (labor market data, employee and student data, etc.) and leverage predictive analytic tools to forecast current and future skill demands. For example, at Udemy, organizations can spot the trending skills that 20+ million people are learning worldwide on the company’s platform, which can help them assess skills gaps within their workforce and allow them to act accordingly.
- Content: From creation to curation. With a massive skill shift underway, the traditional six-month course creation cycle is no longer acceptable. Businesses will be unable to foster the required new skills without a major content overhaul and a new approach to delivery that is on-demand and continuous in nature. To accommodate these needs, new modes of content delivery will emerge, with Netflix-style, on-demand digital assets that will allow for anytime, anywhere self-learning. However, for these types of changes to evolve effectively, business leaders must look outside their own four walls and join external ecosystems via platforms and partnerships.
- Training: From physical to invisible. Tomorrow’s learning experiences will be more active, interactive, and frictionless and will take place in an environment that blurs the boundaries between the traditional classroom and the world outside of it. AI will break our current one-size-fits-all reskilling approaches by personalizing them in the future. By utilizing AI in our organizational workflows, learning and development teams can make on-the-job training more relevant, tailored, and focused. Imagine if every learner had a personalized, intelligent, virtual assistant to help them make continuous reskilling a reality—the possibilities would be endless.
It’s time to face reality: the traditional linear model of education-employment-career is no longer sufficient. In order to survive the future market and technological changes ahead, organizations must adapt to a mindset of continuously skilling and reskilling their workforces. Ultimately, success in the new machine age will be less about survival of the fittest and more about survival of the fastest. See you at the finish line.