US and global organizations face a progressive threefold workforce challenge with digitization: (i) a younger pool of entry and mid-levels entering the workforce; (ii) a mature workforce; retire-ready within the next few years taking their expertise and knowledge with them; (iii) rapid advances in new technology and capabilities, such as, Cloud and AI. Covid-19 has accelerated digitization of the workplace giving precious little time for the workforce to react, learn, and progress. Despite the challenges, organizations have embraced technology and accelerated digitization. This acceleration in the use of technology, digitization, and new forms of working is going to be sustained. It has been reported that, as a result of Covid-19, organizations have moved 20 to 25 times faster than thought possible on building supply-chain redundancies, improving data security, and increased use of advanced technologies in operations and services.
The implications of accelerated digital transformation on workforce re-skilling cannot be overstated. Companies (and its workforce) that use digital capabilities to improve their businesses will surge ahead. It is also clear that while most companies believe digital transformation is a must, few have realized it to scale. To scale digital capabilities, the workforce must be prepared. How does an organization prepare its workforce for digital transformation. Let’s consider healthcare, manufacturing, supply chain, and service areas.
In Healthcare, virtual care and telehealth have accelerated to the point of almost becoming mainstream with most insurance plans covering these services. The changes to workflow, technology adoption, and consequently, skills will continue. The clinical workforce will have to learn how to effectively and efficiently use telehealth technology for desirable patient outcomes. This also depends on the services offered and place settings. For example, remote guidance in chart monitoring or arranging transport services will likely require different skills than providing teletrauma assistance. Accountants and medical coders have to learn how to interact with advanced billing systems that maximize revenue and assist with ever-changing CMS guidelines. As an example, if AI driven claims is presenting possible fraud or mis-bills before a claim is processed – today, this process is done after bills are generated and results in late loss recoveries or payments – how will employees be required to interact with the system apriori, evaluate the results, and make business decisions? This is a completely different workflow requiring different skills. What technical and operational skills would be needed by nurses when practicing remote patient monitoring with multiple connected devices such as pace makers, blood pressure monitors? These are questions now to which answers are mostly unknown.
For cloud sourced systems, the role of many employees are already changing. For cloud-based IT systems, there is no need to code or maintain systems; the role has moved being able to configure changes and workflow locally to system changes made in the cloud. As more systems move to the cloud, the role of IT will move as well. They will focused on IT cloud strategy, sourcing, procurement, and contracting in a cloud environment, support of service delivery, and business architecture re-think and support for cloud capabilities.
Covid-19 has brutally exposed over-reliance on China as the only manufacturing source with a re-focus on re-shoring. This has two challenges: (i) replicating capacity on-shore while risking established capacity ready manufacturing in China; (ii) finding the requisite labor pool to support the manufacturing. The latter applies equally for present domestic manufacturing. Specific to finding required labor capacity, we are at a point of losing a very knowledgeable and experienced workforce due to large-scale impending retirement. These are experts in manufacturing and supply chain, in CAD/CAM, MES, NC and automation systems, while being digitally non-native and not having been exposed to current technologies, such as, Cloud sourcing or use of AI in manufacturing. It is important to note that while this workforce has been very familiar with automation and automation systems they are not adept or prepared for digital. Automation is the mechanization of repetitive tasks, such as, pick-and-place, transfer line machining and assembly, or invoicing. Digitization is the application of digital technology to improve the business and is integrative and cross-cutting in nature. As digital natives enter manufacturing, how should they be prepared? The following are some thoughts.
- Develop new capabilities for workforce at various levels to elevate their role from machine operators to using big data generated analytics for addressing business or process risks.
- Develop skills for configuring, and re-configuring process data maps, process governance, and data architectures. For example in the shop floor, more and more sensor-based real-time data is being generated. This data can be used to augment traditional statistical process control using AI/ML tools. The question is which data to choose, how much data to choose, and how to identify and link this data to other dependent upstream or downstream data or processes, to monitor processes and their influences on each other. Thus, using digital technology for process control raises the role of process operator from merely analyzing cause-effect and corrective action, to a process and data architect and configurator. This elevation requires different training and capabilities. We can extrapolate this example to supply chains as well, where the supply chain operator also becomes the overseer of process influencers rather than a point controller.
- Digitally capture aging workforce skills and knowledge for training the newer workforce.
While there is fear that human workers will be automated out of the workforce, the growing consensus is that AI and humans can leverage complementary strengths and effectively augment each other. People and organizations that will understand how AI fits within workflows and how people can work collaboratively with algorithms will be more competitive than those that are unable to do so.
In summary, the following are some broad skill shift needs:
- Leverage AI and develop business friendly tools.
- Proactively assess digital transformation requirements and develop re-skilling paths.
- Train configuration of cloud-based applications as and when needed; identify methods for improving the human to machine interactions.
- Align vocational colleges with industry and assess the needs, and develop appropriate curriculum and training (basics of data science, process and data analysis frameworks).