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AI Drives Better Business Decisions
Many forms of artificial intelligence, from simple decision support to self-guided systems, are coming into play across industries today. This video and accompanying article explain why companies should leverage variants of AI for faster, better decision making.
As in other industries, business leaders in the automotive and financial-services industries have an urgent need for trusted and actionable real-world insights that can help them know and serve their customers better while enabling rapid innovation.
Too often, however, executives have had to operate with uncertain, incomplete, and inconsistent information. Now advances in artificial intelligence (AI) have made the construction of data-based real-world models and simulations a reality.
A 2015 Tech Pro Research survey indicated that 24 percent of businesses across industries are currently using AI or had plans to do so within the year. While the health-care sector has been among the leading adopters of AI, financial-services and automotive companies are also increasingly turning to assisted, augmented, and autonomous intelligence. These organizations hope those three types of AI will help improve their efficiency and effectiveness, enhance their innovative capabilities, and better enable them to seize opportunities such as expanding into new markets. (For brief definitions of the three types of AI, see “AI at a Glance.”)
Beyond helping its automotive and financial-services clients understand how to use AI to their best advantage, PwC employs AI techniques within its own operations. For example, PwC Strategy& has developed a tool called DeNovo that allows both the company’s internal analysts and its clients to evaluate the disruptive potential of a particular financial technology and rapidly assess how to harness it for best use. DeNovo is heavily influenced by an internal PwC toolset, the Emerging Tech Radar, which is used to understand emerging trends by leveraging semantic natural-language analysis, graph processing, and supervised learning.
With DeNovo, “you’re very quickly forming a body of knowledge around the technology: the companies doing it, the venture capitalists investing in it, how many articles there were about it this week versus last week, and who are some of the leading players involved,” explains Anand Rao, partner and innovation lead, PwC Data & Analytics. Over time, DeNovo “learns” what type of information is most useful to users and delivers tailored results.
PwC also developed a marketplace of AI-enabled analytics apps for internal use by its analysts. Currently, the marketplace contains 60 apps, and PwC has plans in the works to release them for external and client use as well.
Understanding Ever-Changing Financial Needs
In the financial-services sector, PwC has developed a large-scale model of the lifetime financial and purchasing decisions of nearly 320 million U.S. consumers. This information was derived by combining U.S. Census Bureau data, U.S. consumer finance data, and information from several other publicly available and licensed sources. Developed and commercialized by PwC, this massive data set, called $ecure, provides a realistic model against which financial-services companies can evaluate consumers’ complex, multi-year strategic decisions. It creates a model of “someone like you” as well as what financial products that persona bought, when, where, why, and from whom. Financial-services companies can also leverage this data to validate their own real-time operational decisions in a split second.
The technology can also model “your future self” by simulating what is likely to happen to your financial statement in terms of income versus expenses as well as assets versus liabilities. Financial-services companies can model questions about customer behavior, such as “‘What is their behavior toward using credit cards, using loans, using insurance products?’ ‘How does that change over time?’ And ‘how does it change by segment?’ And not only how has it changed, but also ‘how will it change in the future based on various assumptions about the economy, about the market, about individuals?’” Rao says. “This is a very comprehensive system.”
AI: Driving the Future of Transportation
The automotive sector includes myriad current applications of AI as well as future possibilities, ranging from automated accident-claims adjustment, to providing safety warnings to drivers, to the eventual adoption and proliferation of autonomous cars. Many vehicles today are equipped with cameras and sensors that provide data that can be used to promote safety, Rao notes.
Today, most new cars are fitted with sensors in the front, in the back, and on the sides, as well as front-facing and rear-facing cameras, he says. Many are using that sensor and camera data to build machine learning that can identify anomalous patterns and warn drivers before an accident occurs. Add-on safe-drive systems are now leveraging this machine learning to warn of lane departures and imminent collision risks.
For example, some startup companies sell an inexpensive video camera that a driver can attach to a car’s windshield. “The camera takes in all that information, essentially looking out through the windshield just as the driver would, but it’s measuring everything: where the trees are, where the human who’s crossing the street is,” Rao says. “All those things are being collected, and the system can actually give you directions such as ‘You’re going too fast; you should slow down here,’ or ‘Watch out for the pedestrian on the other side.’”
Adaptive cruise control based on AI algorithms is another feature Rao believes will take hold soon. “If you set your speed to 70 miles an hour and the car in front of you is going 50, as you approach it within a safe distance, you’ll start slowing down automatically,” he says. “You don’t have to do anything.” PwC advises automotive-industry clients—both manufacturers and other players in the broader automotive ecosystem—on how to leverage machine learning to improve transportation and safety.
AI systems are also being used to model the entire automobile ecosystem of the future. Currently, millions of intelligent agents, or “bots,” capture the individual decisions being made by consumers, auto manufacturers, personal-mobility services providers (for example, taxis and car-share operators), and other players in the ecosystem. These systems then enable the modeling of customer adoption of car-sharing or of autonomous or electric vehicles; they also allow the modeling of different business models, advertising, and pricing of various services. Unlike typical strategy studies that run a few go-to-market (GTM) scenarios on the choices available to players in the ecosystem, these AI systems have run more than 200,000 GTM scenarios to develop individualized and optimal GTM scenarios to maximize revenues or profits.
Such sophisticated AI systems move beyond the current prescriptive models into augmented intelligence that enhances complex human decision making. Human decisions from the real world, in turn, inform these AI systems and teach them to perform more effectively in the future.
The ability to model outcomes of a limitless number of scenarios is the major breakthrough of modern AI techniques, in Rao’s view. AI systems start from zero, but once they are receiving a steady diet of big data, they can project unprecedented outcomes. “There’s an immense opportunity to use AI in all kinds of decision making,” Rao says.
According to the old-fashioned stereotype, smart machines will replace humans at work—thus stealing jobs. But with the advanced AI techniques now in use, people’s jobs will actually be enriched by the host of information delivered by AI, which they can use to make the best possible choice at the moment, Rao says. AI is already transforming the financial-services and automotive industries, among others, but business leaders in all sectors should prepare now, learning more about AI and how they can use it to their best advantage.
To learn more about which AI technique is right for your company, please explore PwC’s Emerging Technology blog.
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