Even the most sophisticated and finely tuned AI models couldn’t predict the long-lasting magnitude of COVID-19. Its disruption on our personal and professional lives is hard to quantify. Last March, it was almost impossible to foresee how this past year would unfold -- including the tragic moments and devastation for so many around the world.
However, one year later, there have been plenty of lessons learned from this crisis -- and from a technology perspective, some of the most significant lessons center around the continuing evolution and importance of data, analytics, and artificial intelligence. The pandemic acted as a catalyst to drive a staggering rate of digital transformation, which enabled business continuity and resiliency. It also turned consumer behavior upside down -- leading to a far greater need and dependence on accurate predictive, prescriptive, and cognitive technologies. With many companies already struggling, and consumers scaling back their spending or buying through different channels, gaining and keeping loyal customers has been critical.
Before COVID-19, AI was often seen as an important space to pursue, but at times, lacked buy-in from the C-suite. But in the past year, it has proven to be an essential asset for businesses to reach customers and maintain operations, and for people to function within their day-to-day lives.
Here are three of the most impactful lessons learned from AI’s journey as it navigated the pandemic:
Consumers are embracing AI-driven interactions
In March 2020, it was remarkable to see two enormous shifts occur simultaneously: buyers demanded new, safe ways of interacting with organizations; and organizations were able to deliver the methods to make it possible. According to Capgemini research conducted just three years ago, 21% of consumers had daily AI-enabled interactions. As of July 2020, that increased to 54%, as people embraced chatbots, digital assistants, voice and facial recognition, and biometric scanners to replace person-to-person contact. Consumer trust in AI-driven interactions also spiked -- from 30% in 2018 to 46% in 2020. From contactless ordering for retail, groceries, and restaurants, to telehealth interactions replacing an in-office doctor visit, the consumer adoption of these touchless transactions has been a key and consistent change. Organizations are realizing that these methods aren’t likely to disappear once the pandemic ends.
History is broken for predictive modeling
Those changing consumer behaviors created an abrupt reality for data science teams: predictive AI and machine learning (ML) models and the data they are derived from were almost instantly outdated, and in many cases reduced to irrelevance. In the past, these models were based on historical data from several years of behavioral patterns. But in a world of tightened spending, limited purchasing options, changing demand patterns, and restricted engagement with customers, that historical data no longer applied. To combat this problem -- at a time when companies could not afford inaccurate predictions or lost revenue -- AI teams turned to such solutions as real-time, ever-changing forecasting. By constantly updating and tuning their predictive models to include incoming data from the new pandemic-driven patterns, organizations were able to reduce data drift and more effectively chart their paths through the crisis and recovery period.
In digital transformation, AI equals ROI
With their hand forced, companies needed to make difficult choices during the spring of 2020. Do they put their projects and initiatives on pause and wait for the pandemic to subside, or push forward in applying AI as a competitive differentiator during these challenging times? Many saw the latter as the best option, as advancing technology capabilities could be leveraged to better predict the future vs. conducting business through a rear-view mirror. However, that also came with natural pushback from the business since budgets were being tightened amid economic uncertainty. When technology transformations involved AI deployments, organizations have an enormous opportunity to gain business advantage while also receiving very high ROI. By choosing the proper use cases and executing correctly, AI-driven projects can pay for themselves within the first six months of deployment -- and bring multiples of ROI throughout project or program life. The upfront investments in areas such as data transformation (to enable AI) may seem very daunting. However, best practice case studies have proven that a more self-funded business case can actually be achieved.
AI is just one of many technology capabilities leaned on to help companies survive the pandemic. However, as we enter year two, many of these new ways of doing business have shown their long-term value. Having the technology to improve efficiency, work faster, and capture more accurate insights from data will continue to be highly relevant. While AI’s story and progress over the past year has been nothing short of transformational, it is likely that its journey has only just begun.
Jerry Kurtz is Capgemini’s Executive Vice President of for Insights & Data in North America. He has more than 30 years of management consulting experience working primarily in the manufacturing, high-tech, consumer products, retail, and logistics industries. His leadership experience includes data & analytics, artificial intelligence, internet of things, enterprise transformation with large scale ERP, supply chain management, shared services, and business process services. Jerry lives in Charlotte, N.C., and received his Bachelor of Engineering degree from Vanderbilt University.