This trend is crystal clear: US companies are ramping up their AI investments. Fifty-two percent of our survey respondents have accelerated their AI approach in the wake of the COVID-19 crisis. The results will be felt for years to come. These “accelerating” companies cite their top changes as new use cases for AI (40%) and increased AI investments (also 40%). Of all the participants in our survey, 86% say that AI will be a “mainstream technology” at their company in 2021.
AI is paying off in concrete ways, with benefits ranging from revenue growth to better decisions and improved customer experiences. In fact, the companies in our survey that have rolled out AI enterprise-wide are more optimistic about growth, despite COVID-19: 25% expect to increase revenue, compared with 18% for all companies.
The future payoff is even greater and could give early adopters an edge that competitors may never be able to overtake. AI leaders are building a virtuous cycle, sometimes called a flywheel: AI leads to better products, increased productivity and superior customer experiences. That leads to more customers who share more data. More data leads to smarter AI algorithms creating still better products and experiences that attract more customers who share more data, producing even smarter AI. For evidence that this flywheel exists and is turning fast, consider how those companies that have fully embraced AI are already seeing far more benefits than those still seeking to get their AI up to speed.
Yet attaining this flywheel isn’t easy. When considering not just the benefits, but the costs, 76% of organizations are barely breaking even on their AI investments. Breaking even isn’t necessarily bad for an investment that could be the foundation of your company’s future. But it’s possible to invest smarter, for better returns right now and long into the future.
Build virtuous cycles for data and talent.
Once you’ve cleansed and standardized data, you can train AI on that data—and then it can increasingly extract and standardize the data on its own, pulling what it needs from both digital and physical sources. Similarly, if you have AI talent skilled in building top-performing algorithms, you can grow profits and lead in innovation—attracting even more top talent.
Understand the costs.
AI costs are about much more than talent. You’ll likely also have to invest in gathering, cleansing and labeling data and, since AI needs serious computing power, in technology as well. But if you accurately assess the costs, sticker shock may be less likely to paralyze you—and you’ll be better able to direct AI investments to applications with real business value.
Pick the right operating model.
Choose an AI operating model that ensures a consistent approach to data, governance and model use across your company. A centralized hub is an excellent option, but it’s not the only one. With well-structured governance and AI-savvy managers, companies may benefit from embedding AI capabilities within business units.
PwC’s annual AI Predictions survey, now in its fourth edition, explores the activities and attitudes of US business and technology executives who are involved in their organization’s AI strategies. Among this year’s 1,032 survey respondents, 71% hold C-suite titles and 25% were from companies with revenues of $5 billion and up. They are from the following industries: industrial products (20%), consumer markets (20%), financial services (18%), tech, media and telecommunications (17%), health industries (17%), and energy, utilities and mining (8%). The survey was conducted by PwC Research, PwC’s global Center of Excellence for market research and insight, in October 2020.