COVID-19 has accelerated digitization of the workplace giving precious little time for the workforce to react, learn and progress. Organizations face a threefold challenge: 

• a younger pool of entry and mid-levels entering the workforce; 
• a mature workforce; retire-ready within the next few years taking their expertise and knowledge with them; 
• rapid advances in new technology and capabilities, such as cloud and A.I. 

Despite the challenges, organizations have embraced technology and accelerated digitization. This acceleration in the use of technology, digitization and new forms of working will continue 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 increasing use of advanced technologies in operations and services.

The implications of accelerated digital transformation on workforce re-skilling cannot be overstated. Companies 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? 

Remote Healthcare

Virtual care and telehealth are accelerating toward the mainstream — requiring a variety of new skills, technologies and changes to workflow. For example, remote guidance in chart monitoring or arranging transport services will likely require different skills than providing tele-trauma 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 A.I.-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 results and make business decisions? 

For cloud-sourced systems, the roles of many employees are already changing. For cloud-based IT systems, there is no need to code or maintain systems; the role has moved from 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 focus on IT cloud strategy; sourcing and contracting in a cloud environment; support of service delivery; and business architecture redesign to support cloud capabilities.

Next-Gen Manufacturing

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: 

• replicating capacity on-shore while risking established capacity ready manufacturing in China; 
• 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 A.I. in manufacturing. It’s 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? There are four places to start:

• 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 process data maps, governance and 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 A.I./ML tools. The question is which data to choose, how much and how it connects or influences other upstream or downstream processes. 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.
• Develop simulation and virtual training tools.

While there is fear that human workers will be automated out of the workforce, the growing consensus is that A.I. and humans can leverage complementary strengths and effectively augment each other. People and organizations that will understand how A.I. 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:

• Leverage A.I. and develop business friendly tools;
• Proactively assess digital transformation requirements and develop re-skilling paths with relevant simulation or virtual tools;
• Train configuration of cloud-based applications;
• Identify methods for improving human to machine interactions; and
• Align vocational colleges with industry needs.

Shubho Chatterjee is a digital transformation, strategy, technology and operations executive.