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The AI-Based Competitive Revolution

The AI-Based Competitive Revolution
The Golden Age of the Techno-economic Revolution has been characterized by the rise of AI-based firms like Google, Facebook, Amazon.
Technology Briefing


The Golden Age of the Fifth Techno-economic Revolution has been characterized by the rise of AI-based firms including giants like Google, Facebook, Amazon, and Tencent, as well as smaller, rapidly growing firms, ranging from Zebra Medical Vision and Wayfair to Indigo Ag and Ocado. And that’s why experts like Marco Iansiti and Karim Lakhani of Harvard Business School refer to our current era as “the Age of AI.”

Consider this. 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. 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.

At the core of the new firm is a “decision factory” also called an “AI factory.” Its software runs the millions of daily ad auctions at Google and Baidu. Its algorithms decide which cars offer rides on Didi, Lyft, and Uber. It sets the prices of TVs 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 drives the explosive growth at most digital firms isn’t all that sophisticated. To bring about dramatic change, AI doesn’t need to be indistinguishable from human behavior or simulating human reasoning. All you need is a computer system that’s able to perform tasks traditionally handled by people.

With this kind of artificial intelligence, the AI factory can already take on a range of critical decisions. In some cases, it manages information businesses such as Google and Facebook. In other cases, it guides how the company builds, delivers, or operates actual physical products like Amazon’s warehouse robots or Waymo’s self-driving car service. But in all cases, digital decision factories handle most of the critical processes and operating decisions. In short, the software makes up the core of the firm, while humans are moved to the edges.

Four components are essential to every decision 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 will have the intended effect. And,
  • The fourth is infrastructure, the systems that embed this process in software and connect it to internal and external users.
From a strategic standpoint, the transformative power of artificial intelligence depends on its ability to remove the traditional limits of scale, scope, and learning that have defined business economics for the past 250 years.

The concept of scale has been central in business since at least the Industrial Revolution. The great business historian 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 is reinforced by traditional IT systems.

But, after hundreds of years of incremental improvements to the industrial model, the digital firm is now radically changing the scale, scope, and learning paradigm. 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—like the ability to produce ever more accurate and sophisticated customer-behavior models and then tailor services accordingly.

In traditional operating models, scale inevitably reaches a point at which it delivers diminishing returns. But we don’t necessarily see this with AI-driven models, in which the “return on a scale” can continue to climb to previously unheard-of levels. And this can have serious implications when an AI-driven firm competes with a traditional firm by serving the same customers with a similar (or better) value proposition and a much more scalable operating model.

Harvard’s Iansiti and Lakhani refer to this kind of competitive scenario as a “collision.” In such cases, both learning and network effects amplify volume’s impact on value creation, permitting firms built on a digital core to overwhelm traditional organizations. Consider the outcomes we’ve seen when Amazon collides with traditional retailers, Ant Financial collides with traditional banks, and Uber collides with traditional taxi services. As Clayton Christensen, Michael Raynor, and Rory McDonald argued in their landmark 2015 Harvard Business Review article titled “What Is Disruptive Innovation?”, such collisions are not caused by a specific innovation in a technology or a business model. They’re the result of the emergence of a completely different kind of firm. And that can fundamentally alter industries and reshape the nature of competitive advantage.

As Iansiti and Lakhani observe, it can take quite a while for AI-driven operating models to generate economic value anywhere near the value that traditional operating models generate at scale. Network effects produce little value before they reach critical mass, and most newly applied algorithms suffer from a “cold start” before acquiring adequate data. This explains why executives ensconced in a traditional model have a difficult time, at first, believing that the digital model will ever catch up. But once the digital operating model really gets going, it can deliver far superior value and quickly overtake traditional firms.

Collisions between AI-driven and traditional firms are already happening across industries as diverse as software, financial services, retail, telecommunications, media, health care, automobiles, and even agribusiness. In fact, it’s hard to think of a business that isn’t facing the pressing need to digitize its operating model and respond to the new threats.

Given this trend, we offer the following forecasts for your consideration.

First, as artificial intelligence is adopted across the economy, productivity will soar.

According to Paul Daugherty, Accenture’s chief technology officer, “Our research demonstrates that as AI matures, it can propel economic growth and potentially serve as a powerful remedy for stagnant productivity and labor shortages of recent decades.” Specifically, the study found that AI could increase productivity by an average of 40 percent while doubling annual economic growth rates for most countries by 2035. The researchers contrasted a baseline scenario for each country’s economy in 2035, which depicts projected economic growth without AI, versus an AI scenario, in which AI has been absorbed into the economy. They found that AI made the biggest impact on the U.S., propelling its annual growth rate from 2.6 percent to 4.6 percent by 2035, a rate which the country hasn’t experienced since the boom years of the 1980s. That’s equivalent to an extra $8.3 trillion in gross value added (or GVA) which is a close approximation of GDP. That increase would exceed the current GVA of Germany, Japan, and Sweden combined.

According to Accenture, other countries will also benefit from deploying AI. While none of them are projected to achieve the 4.6 percent annual growth rate of the U.S. economy, the size of their increase is in many cases potentially greater because their baselines are lower. Many countries—such as Austria, Finland, Germany, the Netherlands, and Sweden—will double their economic growth, while Japan’s growth will more than triple, from 0.8 percent to 2.7 percent.

Second, traditional companies who hope to succeed in facing off against digital rivals will have to rearchitect the firm’s organization and operating model.

Since the industrial revolution, companies have optimized their scale, scope, and learning through greater focus and specialization; which led to the siloed structures that characterize the vast majority of enterprises today. And generations of conventional information technology didn’t change this pattern. Why? For decades, IT was used to enhance the performance of specific functions and organizational units. And, traditional enterprise systems often reinforced silos and the divisions across functions and products. — But since silos are the enemy of AI-powered growth, digital businesses like Google Ads and Ant Financial’s MyBank deliberately forgo them and are designed to leverage an integrated core of data and a unified, consistent “codebase.” — When each silo in a firm has its own data and code, internal development is fragmented, and it’s nearly impossible to build connections across the silos or with external business networks or ecosystems.

It’s also nearly impossible to develop a 360-degree understanding of the customer that both serves and draws from every department and function. — The bottom line? You don’t have to be a software start-up to digitize critical elements of your business—but you do have to confront silos and fragmented legacy systems, add capabilities, and retool your culture.

Third, some traditional companies will thrive in the world of “AI factories” by digitizing and redesigning key components of their operating models while developing sophisticated data platforms and AI capabilities.

“Artificial intelligence” is to the first half of the 21stcentury what the “assembly line” was to the 20th and the “steam engine” was to the 19th: it’s the transformative technology that changes the way value is created and captured. And while it’s just one of the 12 technologies underpinning the Golden Age of the Fifth Techno-economic Revolution it’s the one that magnifies the power of all the others. How is AI transforming the economic engine of the modern world? What are the primary implications for business strategy? And what can you do to maximize the opportunities and minimize the threats AI poses for you? We’ll show you.

Fourth, conventional business strategy assumptions and approaches will increasingly become obsolete.

Let’s consider just four examples:
  1. Traditional “industry analysis” is becoming increasingly ineffective. Take automotive companies. They’re facing a variety of new digital threats, from companies outside traditional industry boundaries, like Uber and Waymo. But if auto executives think of cars beyond their traditional industry context, as a highly connected, AI-enabled service, they can not only defend themselves but also unleash new value—through local commerce opportunities, ads, news, and entertainment feeds, location-based services, and so on.
  2. You can’t just “stick to your knitting” anymore. The default advice to executives was once to stick with businesses they knew, in industries they understood. But synergies in algorithms and data flows do not respect industry boundaries. And organizations that can’t leverage customers and data across those boundaries are likely to be at a big disadvantage. In the world of the AI factory, a strategy needs to focus on the connections firms create across industries as well as the flow of data through the networks the firm uses.
  3. Organizations and their employees will have to embrace AI-enabled change. Machine learning will transform the nature of almost every job, regardless of occupation, income level, or specialization. In an AI-driven world, the requirements for competition have less to do with specialization and more to do with a universal set of capabilities in data sourcing, processing, analytics, and algorithm development. These new universal capabilities are reshaping strategy, business design, and even leadership. Strategies in very diverse digital and networked businesses now look similar, as do the drivers of operating performance. Industry expertise has become less critical. That is why when Uber looked for a new CEO, the board hired someone who had previously run a digital firm—Expedia—not a transportation company. And,
  4. We’re moving from an era of core competencies that differ from industry-to-industry to an age shaped by data and analytics and powered by algorithms—all hosted in the cloud for anyone to use. This is why Alibaba and Amazon are able to compete in industries as disparate as retail and financial services, and health care and credit scoring. These sectors now have many similar technological foundations and employ common methods and tools. As a result, strategies are shifting away from traditional differentiation based on cost, quality, brand equity, and specialized vertical expertise, toward advantages like business network position, the accumulation of unique data, and the deployment of sophisticated analytics.


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