By Thomas H. Davenport, Barry Libert, and Megan Beck
Artificial intelligence (AI) is undermining many of our industrial age ideas – including the best ways to develop business models, investment strategies, and critical assets. The result is that many companies are playing with an outdated rulebook, and they are being punished by capital markets for it.
The industrial age was, and remains, the foundation of business thinking for most organizations and their leaders, including Michael Porter’s 5 P’s and even some of the most notable Nobel Prize in Economic Sciences winners. The basics of how businesses and countries are managed and measured no longer bring the admiration of investors. One problematic management approach is accounting systems based on Generally Accepted Accounting Principles (GAAP), which classifies plant and property as assets, and people and intangible assets as expenses. The capital asset pricing model (CAPM) also contributes to the problem, because it defines what a capital asset is and isn’t, and assumes that investor behaviors don’t influence market prices. Companies can’t change these measurement systems by themselves, but they don’t have to use them as their only models.
Because today these time-honored tools of capitalism are butting heads with artificial intelligence and the data rush of the Fourth Industrial Revolution. Many speak of company and industry disruption, but few look beyond the surface to all the ‘systems’ that are being disrupted and must be updated to reflect the reality of this age.
Looking only at the surface, it is clear that in the last century, companies like Walmart, Exxon, and Ford Motor Company rose to dominance by leveraging the physical assets of their time and creating economies of scale. These companies had advantages of size, vertical integration and closed, physical systems. But those days are coming to an end. Today, the property, factories, and inventory these giant firms own are increasingly liabilities (despite accounting and investment treatments). Their physical assets aren’t flexible, are costly to move or sell, and can create real disadvantages for the firms that hold them. Walmart, Exxon, and Ford, since they produce or sell only physical assets, are not highly valued by investors despite their enormous size. The ratio of their market value to their sales is 0.56, 1.25, and 0.22, respectively—all quite low in comparison to firms with business models not based on physical assets.
Today, the companies that customers and investors love don’t leverage physical assets, but rather create software, data and other forms of technology. Take Microsoft, for example. Its primary assets are software and the technology to host it. Its physical assets are few, but it is much more highly valued than the three industrial behemoths, with a price to sales ratio of about 7.
The most highly valued companies, of course, are network or platform-based businesses with no physical assets to speak of. We know by now, for example, that when Uber and AirBnB—two companies without physical assets—go public soon, they are likely to be worth more than General Motors and Marriott—companies in their own sectors with vastly greater assets. And there are other such valuable companies as well. Match Group, for example, a company that connects people seeking relationships with its Internet apps, has a price to sales ratio of almost 10.
So, what are the many ways that today’s automation-based economics differ from yesterday’s economies and what do the leaders of companies do to remain competitive? The answer lies in three key shifts of thought and action that reinforce each other.
- Shift from physical to digital. Legacy firms focus on manufacturing, distribution, marketing and sales of physical goods, from oil to washing machines to trucks. Platform-focused companies create software, artificial intelligence, and networks. Once developed, these virtual products and services can be scaled at near-zero cost. Further, they are deflationary in nature, producing customer offerings at lower costs each year with more capacity.
- Shift from product and service to platform, network and data. Platform companies, unlike legacy firms, encourage others to use their assets – friends, photos, algorithms, cars, houses and data. Facebook, Airbnb, Uber, Pinterest, Yelp and eBay all operate in this manner as does Amazon and Apple. Even some legacy companies are dipping a toe or two into contemporary business models. Ford is teaming with Lyft on self-driving cars (though it’s too early for this to be reflected in Ford’s stock price), and makeup brands are finding lift by co-creating products with popular beauty bloggers.
- Shift from oil to data as the source of value creation: It is clear that GAAP and CAPM were viable systems to measure and manage the assets of yesteryear. But they don’t work so well in the production of data – a very intangible and often unmeasurable asset and the basis of today’s growth economies in countries and companies. And since companies use the same GAAP and CAPM systems to measure their performance and attract capital, only using a measurement system built for a different age doesn’t make much sense if we want companies and countries to prosper in the next age.
So what do you do if you are a leader of a company and want to make the shift from the product and services economies of the last economy to the scale and economics of the current one based on platforms, networks and AI? Our answer has five components:
- Leave the ownership and manufacturing of assets to others: Uber has managed to eat into the automobile marketwithout ever manufacturing a single car. Uber didn’t have to build a single production plant and has less than 10% as many employees as Ford, and yet its estimated market value is more than double Ford’s. By “outsourcing” key activities and assets to partners, platform companies are leaner and meaner than their predecessors.
- Build your business around networks and open frameworks. Today’s platform companies build vibrant firms with network effects where the value increases with each customer or citizen participant. Platform companies fuel this effect by incentivizing the user base to grow itself through referrals. When Uber expands into a new city, their app makes it easy, as opposed to scaling up manufacturing and distribution facilitates or training new workers. These rapid growth curves have led some to call network companies “exponential organizations.”
- Diversify your business model and get good at changing it.Companies that make or sell physical assets may not be able to extract themselves from that business model easily, but they can at least diversify and create a mix of models. Amazon, for example, has added both tech-based (Amazon Web Services) and platform (Amazon Marketplace) models to its business, and its price to sales ratio is much higher than when it was only selling things. Apple, primarily a hardware company for most of its history, has wisely diversified into platforms in its music and apps business, and is now diversifying further into entertainment production.
- Consider an alternative accounting framework for your business. If you lead a public company, you don’t have any choice whether or not to use GAAP accounting to report your financial performance. But you can keep an alternative set of internal books. Accounting professors like Bob Kaplan and Tom Johnson long ago provided more detail on why traditional accounting no longer makes sense, and some like Baruch Levcan tell you about alternative methods that properly take intangible assets into account.
- Leverage today’s machines (AI) and expanding and alternative data sets. Artificial intelligence enables organizations to power their networks at a scale never before imagined. For example, AI powered platform companies can scale up or down at near zero marginal cost by matching demand to supply without investing in capital assets. If fewer people want to travel because of bad weather, Airbnb does see less revenue, but it doesn’t have hundreds or rooms sitting empty like Marriot or Hilton because it has shared that risk with its network. It has invested in other capabilities instead: Airbnb manages many petabytes of data, has a large data science group, and makes extensive use of AI to predict and optimize its business.
The trend is absolutely clear, and the economics behind it indicate that this isn’t just a short-term trend. Today’s AI and platform-driven economics have a clear advantage over the economies of scale of the prior age, just as the industrial age was faster, better and cheaper in creating value than the agricultural economy. In fact, the sheer market dominance of platform and AI powered organizations are fast becoming a threat to competitive capitalism, so much so that President Donald Trump—long known for advocating for more coal mining–finally agreed that AI needs to be a pillar of his economic policy to keep pace with China’s commitment to this reality.
The shifts in technology capabilities and capital allocation are taking a bite out of the building blocks that made the industrial revolution so powerful and resolute. However, the time has come for every company to move beyond the old thinking, acting, measuring and investing that underpins yesterday’s economies. The health of the global market depends on updating our underlying measurement systems, business models, and technologies. It’s time to overhaul our business and management approaches to reflect today’s realities. Living in the Fourth Industrial Revolution with measurement and management systems from previous eras is simply not viable.
* This article was originally published on Forbes.com on April 5, 2019.
Barry Libert is the CEO and co-founder of AIMatters, a startup that is using machine learning to transform strategy and leadership consulting.
Megan Beck is the President and co-founder of AIMatters.
Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College in Wellesley, Massachusetts, as well as a fellow at the MIT Initiative on the Digital Economy and a senior adviser to Deloitte’s Analytics and Cognitive practice.