The year 2021 will be focused on recovery—overcoming the COVID-19 pandemic and rebuilding the economy. This new economy will look very different from the one that existed prior to the pandemic. Many of the jobs that have disappeared may never come back, forcing some people to look for jobs in new sectors. The jobs that do come back will in many cases be very different, relying heavily on automation. McKinsey projected before the pandemic that 375 million workers, or 14% of the global workforce, would have to switch occupations or acquire new skills by 2030. It now concludes that the pandemic has made reskilling even more urgent, and urges companies to start reskilling their workforces now.
There is thus an urgent need to reskill the workforce quickly, to help workers re-enter the workforce, and to ensure that skilled workers are available for the new jobs. Otherwise, the skills gap that became apparent before the pandemic will re-emerge and only get worse, and unemployment problems will continue to fester.
Since its inception, Alelo has been developing artificial intelligence (AI) technologies that help people quickly learn new skills. Trainees practice in interactions with AI-driven avatars, in simulations of real-world encounters. Alelo’s language training courses have helped military members quickly develop communication skills in foreign languages, to reduce tensions in conflict zones and even save lives. Our cultural training courses, which quickly impart useful cross-cultural skills, are required training for personnel deploying to over 80 countries. We are now focusing our attention on the problem of rapid reskilling, to help us quickly recover economically from the COVID-19 pandemic.
We believe that rapid reskilling requires a radical rethink as to how training should be done. Conventional training methods are too slow and inefficient, and do not develop useful skills quickly enough. Too many workers have already been unemployed too long, and have no time to waste. Training methods that are optimized for reskilling should help trainees adapt and repurpose the skills that they already have, and not assume that trainees are starting from scratch. This means that reskilling methods should be individualized to the skills and capabilities of each worker.
A case in point is healthcare. The COVID-19 pandemic is intensifying existing workforce shortages. As we emerge from the pandemic, there will be a huge need for public health workers to trace COVID-19 contacts, to promote vaccination compliance, and to monitor patients who are immunocompromised and unable to receive a vaccine. Certificate programs in public health can take a year or more to complete, and so are unlikely to turn out skilled workers fast enough. Meanwhile, there are workers who have lost jobs in hospitality and retail who have experience interacting with people with diverse backgrounds. What would it take to help such people quickly retrain to become community health workers?
In the next blog post in this series, we will discuss in detail the reskilling model that Alelo is implementing. It focuses from the very beginning on adapting the skills that trainees already have and applying skills to realistic tasks. Along the way, trainees acquire the academic knowledge that they need to be effective in performing tasks and solving problems. We will also discuss the central role that AI has in implementing this model, to adapt training to each individual trainee’s skillset, to provide realistic training opportunities and provide feedback, and to automate assessment.