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2 Harvard Business Review Analytic Services Briefing Paper | Keys to Successful Innovation through Artificial Intelligence and Karim R. Lakhani, Harvard Business School professors and coauthors of Competing in the Age of AI, wrote in a 2020 Harvard Business Review article of the same name. An AI/ML transformation starts with a well-conceived strategy to address specific business problems, then moves to getting buy-in from key stakeholders and democratizing the data so that everybody has access and can benefit from its use. From there, companies can implement AI to address the business problem initially identified. AI/ML Transformation, Fueled by Covid-19 As Covid-19 has shown, sometimes long-range planning simply isn't possible. The pandemic forced businesses in every sector to revamp their operations and invest in AI/ML, something that may have been unthinkable for many just a year before. Many enterprises had to create systems on the fly or were forced to scale projects they'd delayed for one reason or another, with great results in many cases. "AI-driven processes can be scaled up much more rapidly than traditional processes can, allow for much greater scope because they can easily be connected with other digitized businesses, and create incredibly powerful opportunities for learning and improvement," wrote Lakhani and Iansiti in the "Competing in the Age of AI" article. AI/ML was central to the fight against Covid-19. Scientists used these technologies to aid in everything from diagnosis and drug development to forecasting the spread of the disease as well as to monitoring and surveilling the population. 3 The benefits of these technologies during Covid-19 extend beyond advances in the health care industry, too. Jack Berkowitz, chief data officer at ADP Inc., says the HR technology company had already moved its people analytics and workforce benchmarks to the cloud, but the pandemic was a force multiplier for its use of AI/ML. "We were able to build new capabilities we never thought possible," says Berkowitz. "In addition to day-to-day operations and analytics for our clients, we were deeply involved in the PPP [Paycheck Protection Program] loans because we pay a large portion of the workforce, particularly for the small and midsize businesses. To do that processing and build those reports they needed to file the loans, [and] we had to do that all in the cloud." Within 20 days, all relevant information clients needed for their PPP applications was available and accessible in the cloud. Berkowitz was pleasantly surprised by clients' readiness to accept these technologies and capabilities. "In the HR space, people have been classically a bit more resistant to taking on new things, but they've really embraced those capabilities. When they're thought through and packaged in a way that's consumable, the uptake is through the roof." The BMW Group was also well into its AI/ML journey prior to the pandemic, but Covid-19 created a sense of urgency to take all its AI capabilities remote. "[The pandemic] was an accelerator, not only for AI and ML but also for gaining insight from all the data we had, as well," says Josef Viehhauser, platform lead and enterprise analytics at the BMW Group. "We already had use cases running on our supply chains, most of which are quite simple events. The pandemic brought an understanding of the influence of catastrophes on the supply chain in specific regions. We were able to boost our existing learnings to move us forward and get better in those areas." Successfully Innovating with AI/ML Long before the pandemic, companies around the globe were realizing the transformative effect of these essential, strategic technologies. A Harris poll found that 55% of companies reported that they accelerated their AI strategy in 2020 due to Covid-19, and 67% expect to further accelerate their AI strategy in 2021. 4 Still, according to a 2020 NewVantage Partners survey, nine out of 10 leading businesses have investments in AI technologies but less than 15% deploy AI capabilities in their work. This huge gap may be in large part because many companies don't fully understand the technology's full potential—or they don't have a clear strategy to use AI/ML to meet their goals. Organizations that do are able to innovate, develop intuitive products, and deliver better service. The BMW Group is one such company that leverages technology effectively to increase innovation. It employs AI/ML for everything from product development to forecasting demands for its goods and services, explain the BMW Group's Viehhauser and Maenner. The company developed a proprietary translation solution to help its multilingual workforce better communicate. And it processes "In every large corporation, you have somebody screaming, 'You need to do more AI!' The question from smart people is, 'For what reason?,'" says Mark Maenner, head of data transformation for the BMW Group.