Two Things Companies Should Do Now to Set Up for a Post-COVID-19 Future
Tuesday, March 31, 2020
By: Ravi Bapna
Managing a Business Has Changed in the Digital World
As the COVID-19 response underscores the new power of digital technologies, data, algorithms and AI, break-out organizations will rethink their man-machine mixology for tasks, projects, processes, operating models and even their strategy.
Companies who evaluate their operations and future strategy now can come back stronger from the COVID-19 pandemic.
Historically, the CEO's job has been to optimally manage the land-labor-capital mix that defines their company's production function in the context of their stakeholders (employees, shareholders, suppliers and customers) as well as their societal reality and constraints. Almost overnight, and thanks to social-distancing, every business has been forced to operate in a hyper-digital environment, where tele-work is getting tasks and projects moving forward, tele-medicine is already playing a big-part in the value delivery of healthcare to stake-holders, and tele-education is shaking up a fossilized higher-ed system that will train the future workforce in a very different way. Imagine the productivity impact of the COVID-19 pandemic in a world where we did not, hypothetically, have Zoom-like technologies.
The CEOs of tomorrow need to manage a new set of intangibles - digital technologies, data, algorithms and AI. That is very different from the classical mix of land, labor, and capital. They need to unbundle the key physical and digital components of their production functions and they need to find the ideal man-machine mix that utilizes the unique capabilities of humans as well as the offerings of state-of-the-art machine intelligence. But, in the current crisis, everything starts with treating employees with respect and using the luxury of time to upskill a good fraction of them to understand the complementarities between human and machine intelligence.
1. Invest in Upskilling Your Workforce
As I have articulated earlier, a key modern imperative for all organizations is a deeper understanding and self-reflection among leaders of human strengths and frailties in contrast to that of modern, software-based machines and algorithms. There are many cases where the automation of cognitive and decision-making tasks can be even more accurate than the best humans trained to do that particular type of job.
For example, in radiology, deep learning models are already rivaling humans with respect to accuracy. This does not mean radiologists should be stripped of their jobs. It simply means there’s an opportunity for that profession to shift focus. Perhaps the radiologist now has the ability to be more involved in the decision-making process and can play a more active role in connecting with the patients (they have historically been in the background). Or take tasks such as demand forecasting done in finance, lead scoring done by marketing, resume screening done by HR, backorder prediction done by operations, or fraud detection done by risk managers - machine learning models are fundamentally augmenting the capabilities of the best humans in these areas, freeing up their capacity to ask more interesting questions (a key future-proof skill) and automate routine tasks. As such changes occur, it goes without saying that organizations are going to have to provide a different career path for these employees as well as opportunities for them to continually create value.
As organizations shift to an AI-first world, they need a workforce which understands the world of data, analytics, and AI. It will be increasingly important to find, or reskill, people who can translate data strategy up to the CEO and down to the data scientists and the people working in the front lines. I can’t think of an industry that is not going to have to rethink its strategy and operating model to account for this new reality.
2. Rethink Operations and Strategy
As businesses work to redefine their man-machine mixology in terms of their tasks and processes, there’s an opportunity to reassess their fundamental operating models. For many organizations, including higher education, a digital and AI-first strategy will begin to make sense. In that context, many faculty members with today's digital technologies are doing a really good job teaching their classrooms, even interactive classes, using tools such as Zoom. If we’re able to educate in this way, it begs the reassessment of the land-labor-capital mix of education's production function. Do students need to be in school for eight hours a day? What components of the overall education bundle have to be delivered in the classroom, or on campus, and what should be done digitally? Does recognizing the intangible digital component in the mix change the way the higher education and universities should operate?
The COVID-19 crisis in China actually highlights a very good example of a company that operates with AI-first strategy. Very soon after the initial outbreak, Alibaba’s AI teams immediately recognized an opportunity to take the CT scans that were being used to diagnose the disease and train a deep learning model to automate that process. They were able to achieve upwards of 96% accuracy and 90% recall levels. Alibaba is certainly not a healthcare company, but their AI-first strategy positioned them for a new opportunity. This kind of technological approach has brought down the time of reading a scan to from 15 minutes to close to 20 seconds.
In a broader sense, there are many ways that companies can align their strategies to find new value when humans and machines work together. More deeply personalized educational experiences or shopping experiences and better service industry approaches are just a few examples that are waiting to be taken to the next level. If there’s a hotel chain out there willing to step up to understand my true travel preferences and curate those offerings for me in a way that's personalized and unique, they will make money off of me and many other travelers looking to be in places in flesh and bone post-COVID-19.
I think the companies that get the transition right will have a very different, positive kind of relationship with their consumers and the stakeholders. I'm looking forward to a time where, once we are past this immediate crisis, smart companies with smart leaders who have had time to reflect and reimagine their man-machine mixology make the world much more interesting and fun to be in.
Associate Dean for Executive Education
Curtis L. Carlson Chair in Business Analytics and Information Systems
Academic Director, Carlson Analytics Lab
Professor Ravi Bapna's expertise lies in helping companies reframe their understanding of analytics, AI, and machine learning, and then leveraging that understanding for competitive advantage. He specializes in social media, peer influence, monetization and design of Freemium communities, human capital issues, and design of IT communities, showing the breadth of application for big data analytics, and how they can transform industries.