Published on December 30th, 2019 | by Emergent Enterprise0
Competing in the Age of AI
Artificial intelligence should no longer be labeled by people like myself as an “emergent technology.” As Marco Iansiti and Karim R. Lakhani explain in this Harvard Business Review report, AI is firmly entrenched in businesses of all sorts and is driving production and performance at unprecedented levels. It’s not just emerging, it’s here. As consumers, citizens and employees, we interact with AI everyday as it makes decisions for us and provides guidance and support in a myriad of scenarios. Every business should be implementing AI to remain competitive and relevant.
In 2019, just five years after the Ant Financial Services Group was launched, the number of consumers using its services passed the one billion mark. Spun out of Alibaba, Ant Financial uses artificial intelligence and data from Alipay—its core mobile-payments platform—to run an extraordinary variety of businesses, including consumer lending, money market funds, wealth management, health insurance, credit-rating services, and even an online game that encourages people to reduce their carbon footprint. The company serves more than 10 times as many customers as the largest U.S. banks—with less than one-tenth the number of employees. At its last round of funding, in 2018, it had a valuation of $150 billion—almost half that of JPMorgan Chase, the world’s most valuable financial-services company.
Unlike traditional banks, investment institutions, and insurance companies, Ant Financial is built on a digital core. There are no workers in its “critical path” of operating activities. AI runs the show. There is no manager approving loans, no employee providing financial advice, no representative authorizing consumer medical expenses. And without the operating constraints that limit traditional firms, Ant Financial can compete in unprecedented ways and achieve unbridled growth and impact across a variety of industries.
The age of AI is being ushered in by the emergence of this new kind of firm. Ant Financial’s cohort includes giants like Google, Facebook, Alibaba, and Tencent, and many smaller, rapidly growing firms, from Zebra Medical Vision and Wayfair to Indigo Ag and Ocado. Every time we use a service from one of those companies, the same remarkable thing happens: Rather than relying on traditional business processes operated by workers, managers, process engineers, supervisors, or customer service representatives, the value we get is served up by algorithms. Microsoft’s CEO, Satya Nadella, refers to AI as the new “runtime” of the firm. True, managers and engineers design the AI and the software that makes the algorithms work, but after that, the system delivers value on its own, through digital automation or by leveraging an ecosystem of providers outside the firm. AI sets the prices on Amazon, recommends songs on Spotify, matches buyers and sellers on Indigo’s marketplace, and qualifies borrowers for an Ant Financial loan.
The elimination of traditional constraints transforms the rules of competition. As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur. The changes extend well beyond born-digital firms, as more-traditional organizations, confronted by new rivals, move toward AI-based models too. Walmart, Fidelity, Honeywell, and Comcast are now tapping extensively into data, algorithms, and digital networks to compete convincingly in this new era. Whether you’re leading a digital start-up or working to revamp a traditional enterprise, it’s essential to understand the revolutionary impact AI has on operations, strategy, and competition.
The AI Factory
At the core of the new firm is a decision factory—what we call the “AI factory.” Its software runs the millions of daily ad auctions at Google and Baidu. Its algorithms decide which cars offer rides on Didi, Grab, Lyft, and Uber. It sets the prices of headphones and polo shirts on Amazon and runs the robots that clean floors in some Walmart locations. It enables customer service bots at Fidelity and interprets X-rays at Zebra Medical. In each case the AI factory treats decision-making as a science. Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows.
Oddly enough, the AI that can drive the explosive growth of a digital firm often isn’t even all that sophisticated. To bring about dramatic change, AI doesn’t need to be the stuff of science fiction—indistinguishable from human behavior or simulating human reasoning, a capability sometimes referred to as “strong AI.” You need only a computer system to be able to perform tasks traditionally handled by people—what is often referred to as “weak AI.”
With weak AI, the AI factory can already take on a range of critical decisions. In some cases it might manage information businesses (such as Google and Facebook). In other cases it will guide how the company builds, delivers, or operates actual physical products (like Amazon’s warehouse robots or Waymo, Google’s self-driving car service). But in all cases digital decision factories handle some of the most critical processes and operating decisions. Software makes up the core of the firm, while humans are moved to the edge.
Four components are essential to every factory. The first is the data pipeline, the semiautomated process that gathers, cleans, integrates, and safeguards data in a systematic, sustainable, and scalable way. The second is algorithms, which generate predictions about future states or actions of the business. The third is an experimentation platform, on which hypotheses regarding new algorithms are tested to ensure that their suggestions are having the intended effect. The fourth is infrastructure, the systems that embed this process in software and connect it to internal and external users.
Take a search engine like Google or Bing. As soon as someone starts to type a few letters into the search box, algorithms dynamically predict the full search term on the basis of terms that many users have typed in before and this particular user’s past actions. These predictions are captured in a drop-down menu (the “autosuggest box”) that helps the user zero in quickly on a relevant search. Every keystroke and every click are captured as data points, and every data point improves the predictions for future searches. AI also generates the organic search results, which are drawn from a previously assembled index of the web and optimized according to the clicks generated on the results of previous searches. The entry of the term also sets off an automated auction for the ads most relevant to the user’s search, the results of which are shaped by additional experimentation and learning loops. Any click on or away from the search query or search results page provides useful data. The more searches, the better the predictions, and the better the predictions, the more the search engine is used.
Removing Limits to Scale, Scope, and Learning
The concept of scale has been central in business since at least the Industrial Revolution. The great Alfred Chandler described how modern industrial firms could reach unprecedented levels of production at much lower unit cost, giving large firms an important edge over smaller rivals. He also highlighted the benefits companies could reap from the ability to achieve greater production scope, or variety. The push for improvement and innovation added a third requirement for firms: learning. Scale, scope, and learning have come to be considered the essential drivers of a firm’s operating performance. And for a long time they’ve been enabled by carefully defined business processes that rely on labor and management to deliver products and services to customers—and that are reinforced by traditional IT systems.