Shulou(Shulou.com)06/02 Report--

Competition in the era of artificial Intelligence

Feng people crazy talk: close your eyes and think about the world of tomorrow. Is it Baidu, Google or Toyota or Volvo that holds the ears of the auto industry? Data and algorithms have become the bottom of the whole world, and the traditional logic based on the scarcity of resources in the material world, either or or, and the bounded rationality of the human brain seems to be completely subverted. The more data, the stronger the algorithms, the stronger the strong, and the wise take all. This is not only a very terrible picture, but also an exciting picture. Smith used division of labor to describe the development of the world, Marx used class to analyze the future of mankind, in this new era, we need new logic of thinking, data and algorithms are the key to our understanding of tomorrow.

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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 $150billion-almost half that of JPMorgan Chase, the world's most valuable financial-services company.

In 2019, Ant Financial Services Group was founded only 5 years ago, and the number of customers broke through the 1 billion mark. Born in Alibaba, Ant Financial Services Group uses artificial intelligence and Alipay data (Alibaba's core mobile payment platform) to run a variety of businesses, including consumer loans, money market funds, wealth management, health insurance, credit rating services, and even an online game that encourages people to reduce carbon emissions. Ant Financial Services Group has more than 10 times as many customers and less than 1/10 employees as the largest bank in the United States. In its most recent funding round in 2018, it was valued at $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 ona 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.

Unlike traditional banks, investment institutions and insurance companies, Ant Financial Services Group is based on a digital core. There are no workers on the "critical path" of its business activities, and AI dominates everything. No manager approves loans, no employees provide financial advice, and no representative approves consumers' medical expenses. Without the operational constraints that limit traditional enterprises, Ant Financial Services Group can compete in an unprecedented way, achieve unfettered growth, and have an impact across multiple 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 emergence of this new type of company leads the arrival of the era of artificial intelligence. There are many companies like Ant Financial Services Group, including giants such as Google, Facebook, Alibaba and Tencent, as well as many smaller and fast-growing companies, from zebra healthcare (Zebra Medical Vision) and Wayfair to Indigo Ag and Ocado. Every time we use the services provided by these companies, we see the same and very unforgettable scene: unlike relying on workers, managers, engineers, supervisors, or customer service representatives to run traditional business processes, the value we get is provided by algorithms. Satya Nadella, Microsoft's chief executive, calls artificial intelligence the company's new "runtime". It is true that managers and engineers have designed artificial intelligence and developed software to make algorithms work, but after that, it is intelligent systems that realize value on their own through automated programs or by taking advantage of the ecology of external suppliers. AI sets prices on Amazon, recommends songs on Spotify, matches buyers and sellers on Indigo, and selects qualified lenders for Ant Financial Services Group.

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 elimination of traditional constraints has undoubtedly changed the rules of competition. As digital networks and algorithms are introduced into the enterprise architecture, industries begin to operate in different ways, and the boundaries between industries begin to blur. These changes are not only brought about by these new digital companies, in the face of new competitors, traditional organizations are also beginning to shift to an artificial intelligence-based operation model. Wal-Mart, Fidelity, Honeywell and Comcast are making extensive use of data, algorithms and digital networks to win competition in a new era. Clearly, whether you are leading a digital start-up or trying to transform a traditional company, it is essential to understand the revolutionary impact of artificial intelligence on business operations, strategy and competition.

The AI Factory

Artificial intelligence 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.

At the heart of a new company like Ant Financial Services Group is a decision-making factory-what we call an "artificial intelligence factory". On Google and Baidu, software runs millions of advertising auctions every day. On Didi, Grab, Lyft and Uber, the algorithm determines which cars can provide services. On Amazon, smart algorithms price headphones and polo shirts. In some Wal-Mart stores, robots are cleaning the floor. Fidelity uses robots to provide customer service, and zebra medical uses robots to interpret x-ray images. In each case, artificial intelligence factories regard decision-making as a science, and data analysis software systematically transforms internal and external data into prediction, insight and selection to guide and automate workflow.

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."

Oddly, the artificial intelligence that drives the explosive growth of digital companies is often not complex. Despite the dramatic changes, the artificial intelligence needed is not what is in science fiction-the ability to behave or simulate human reasoning, which is sometimes referred to as "strong artificial intelligence". In fact, you only need a computer system to accomplish tasks traditionally done by people-this is often referred to as "weak artificial intelligence".

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.

With weak artificial intelligence, AI factories are able to make a series of key decisions. In some cases, it manages information businesses (such as Google and Facebook). In other cases, it instructs companies on how to build, deliver or operate physical products (such as Amazon's warehousing robots or Google's self-driving cars). In all cases, the digital decision factory handles the most critical processes and operational decisions, the software forms the core of the company, and people 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 ina 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.

For artificial intelligence factories, there are four essential elements. One is the data pipeline, which is a semi-automated process that collects, cleans up, integrates, and protects data in a systematic, sustainable, and scalable manner. The second is the algorithm, which generates predictions about the future state or actions of the business. The third is the experimental platform, on which the hypothesis of the new algorithm is tested to ensure the desired effect. The fourth is the infrastructure, which embeds artificial intelligence into the software platform and connects it to the systems of internal and external users.

The AI that drives explosive growth often isn't even all that sophisticated

The artificial intelligence that drives explosive growth is usually not very complex.

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.

Take search engines like Google or Bing as an example. Once someone starts typing a few letters into the search box, the algorithm dynamically predicts the entire search term based on the words entered by many users and the user's past behavior. These predictions are displayed in the drop-down menu ("Auto suggestion Box") to help users quickly lock down related searches. Every keystroke and every click is captured as a data point, and each data point improves the prediction of future searches. Artificial intelligence can also generate organic search results from previously collected web indexes and optimized based on clicks generated by previous search results. The addition of the word also led to automatic auctions of ads most relevant to users' search, a result formed by other experiments and learning cycles. Any click or leave search query or search results page will provide useful data. The more the search, the better the prediction effect, the higher the utilization rate of the search engine.

Removing Limits to Scale, Scope, and Learning

Eliminate the restrictions of scale, scope and learning on enterprise growth.

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.

Since the industrial revolution, the concept of scale has been the core of business. The great Alfred Chandler once described how modern industrial enterprises reached unprecedented levels of production at much lower unit costs, giving large companies an important advantage over smaller competitors. He also highlighted the benefits that companies can gain from expanding the scope of production or increasing the variety. With the increasing importance of innovation, enterprises have increased the demand for learning ability. Size, scope and learning ability are considered to be the main drivers of a company's business performance. For a long time, they have been implemented through well-defined business processes that rely on labor and managers to deliver products and services to customers and are enhanced by traditional IT systems.

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.

Although after hundreds of years, the competitive model of enterprises is only slowly changing. Now digital companies have completely changed the competitive paradigm of scale, scope and learning. Compared with traditional business processes, AI-driven business processes expand service capabilities and scope of services much faster. They can easily connect with other digital businesses, create incredibly powerful learning and improvement opportunities, produce more accurate and complex customer behavior models, and customize corresponding services.

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 scale can continue to climb to previously unheard-of levels. Now imagine what happens 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.

In the traditional mode of operation, the scale reaches an equilibrium point, after which the returns begin to decline. But in an artificial intelligence-driven operating model, this may not happen, and returns to scale may continue to climb to unprecedented levels. Now, imagine that when an AI-driven company competes with a traditional company, an AI-driven company uses a highly scalable operation model to provide similar (or better) value services to the same customers. what's the result?

How AI-Driven Companies Can Outstrip Traditional Firms

How do artificial Intelligence-driven companies surpass traditional companies

The value that scale delivers eventually tapers off in traditional operating models, but in digital operating models, it can climb much higher.

In the traditional operation model, the value of this scale growth will eventually gradually decrease, but in the digital operation model, it can climb higher.

We call this kind of confrontation a "collision." As both learning and network effects amplify volume's impact on value creation, firms built ona digital core can overwhelm traditional organizations. Consider the outcome when Amazon collides with traditional retailers, Ant Financial with traditional banks, and Didi and Uber with traditional taxi services. As Clayton Christensen, Michael Raynor, and Rory McDonald argued in "What Is Disruptive Innovation?" HBR, December 2015), such competitive upsets don't fit the disruption model. Collisions are not caused by a particular innovation in a technology or a business model. They're the result of the emergence of a completely different kind of firm. And they can fundamentally alter industries and reshape the nature of competitive advantage.

We call the confrontation between artificial intelligence-driven companies and traditional companies "conflict". Because learning and network effects magnify the impact of quantity on value creation, companies based on digital cores can surpass traditional organizations. Consider the consequences of conflicts between Amazon and traditional retailers, Ant Financial Services Group and traditional banks, Didi and Uber and traditional taxi services. As Clayton Christensen, Michael Renault and Rory MacDonald point out in what is disruptive Innovation (Harvard Business Review, December 2015), such competitive subversion is not in line with the subversive innovation model. Conflicts are not caused by specific innovations in technology or business models. They are the result of a completely different company. They can fundamentally change the industry and reshape the nature of competitive advantage.

Note that 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. Ant Financial grew rapidly, but its core payment service, Alipay, which had been launched in 2004 by Alibaba, took years to reach its current volume. This explains why executives ensconced in the 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.

Note (as shown above) that the economic value generated by an artificial intelligence-driven operation model may take a long time to approach the scale value generated by a traditional operation model. The value of the network effect is very small before reaching the critical scale, and the algorithms of most new applications encounter a "cold start" before getting enough data. Ant Financial Services Group is growing rapidly, but its core payment service, Alipay launched by Alibaba in 2004, took years to reach its current scale. This explains why executives sitting in the traditional model find it hard to believe that the digital model will catch up at first. But once the digital operation model really starts to operate, it can bring value far beyond the traditional enterprises, and quickly surpass the traditional enterprises.

Collisions between AI-driven and traditional firms are happening across industries: software, financial services, retail, telecommunications, media, health care, automobiles, and even agribusiness. 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.

Conflicts between artificial intelligence-driven enterprises and traditional enterprises are taking place in industries such as software, financial services, retail, telecommunications, media, medical, automotive and even agribusiness. It is hard to imagine that an enterprise does not face the urgent need to digitize its operating model and respond to new threats.

Rebuilding Traditional Enterprises

Rebuild traditional enterprises

For leaders of traditional firms, competing with digital rivals involves more than deploying enterprise software or even building data pipelines, understanding algorithms, and experimenting. It requires rearchitecting the firm's organization and operating model. For a very, very long time, companies have optimized their scale, scope, and learning through greater focus and specialization, which led to the siloed structures that the vast majority of enterprises today have. Generations of information technology didn't change this pattern. For decades, IT was used to enhance the performance of specific functions and organizational units. Traditional enterprise systems often even reinforced silos and the divisions across functions and products.

For the leaders of traditional enterprises, the competition with digital enterprises is not just the deployment of enterprise software, or the establishment of data pipelines, understanding algorithms and experiments. It needs to restructure the organization and operation model of the company. For a long time, companies have been optimizing their size, scope and learning model through centralization and specialization, forming the chimney structure owned by most enterprises today. Although information technology has experienced several generations of development, it has not changed this model. For decades, information technology has only been used to improve the performance of specific functions and organizational units. On the contrary, it strengthens the chimney structure of traditional enterprises and promotes the decentralization of enterprise functions and products.

Silos, however, are the enemy of AI-powered growth. Indeed, 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 code base. 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. So when firms set up a new digital core, they should avoid creating deep organizational divisions within it.

However, the chimney structure is the enemy of the artificial intelligence-driven growth model. In fact, companies like Google Ads and Ant Financial Services Group's MyBank have deliberately abandoned these services in order to take advantage of an integrated data core and a unified and consistent code base. When each chimney in the company has its own data and code, internal resources, capabilities, and development are decentralized, making it almost impossible to connect across chimneys or to external business networks or ecosystems. It is almost impossible to have a comprehensive understanding of customers, not only to serve customers, but also to obtain information from various departments and functional units. Therefore, when a company builds a new digital core, it should avoid deep organizational differences within it.

While the transition to an AI-driven model is challenging, many traditional firms-some of which we've worked with-have begun to make the shift. In fact, in a recent study we looked at more than 350 traditional enterprises in both service and manufacturing sectors and found that the majority had started building a greater focus on data and analytics into their organizations. Many-including Nordstrom, Vodafone, Comcast, and Visa-had already made important inroads, digitizing and redesigning key components of their operating models and developing sophisticated data platforms and AI capabilities. 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.

While the transition to an artificial intelligence-driven model is challenging, many traditional companies-some of which have worked with us-have begun to make the transition. In fact, in a recent study, we studied more than 350 traditional companies in the service and manufacturing industries and found that most companies began to pay more attention to data and analysis. Many companies, including Nordstrom, Vodafone, Comcast and visa, have made significant progress by digitizing and redesigning key components of their operating models and developing complex data platforms and artificial intelligence. You don't have to be a software startup to digitize your key business elements, but you have to face a chimney-like, decentralized traditional information system, empower it, and reconstruct the company culture.

Fidelity Investments is using AI to enable processes in important areas, including customer service, customer insights, and investment recommendations. Its AI initiatives build on a multiyear effort to integrate data assets into one digital core and redesign the organization around it. The work is by no means finished, but the impact of AI is already evident in many high-value use cases across the company. To take on Amazon, Walmart is rebuilding its operating model around AI and replacing traditional siloed enterprise software systems with an integrated, cloud-based architecture. That will allow Walmart to use its unique data assets in a variety of powerful new applications and automate or enhance a growing number of operating tasks with AI and analytics. At Microsoft, Nadella is betting the company's future on a wholesale transformation of its operating model.

Fidelity is using artificial intelligence to empower business processes in key areas, including customer service, customer insight and investment advice. Its artificial intelligence plan builds on years of efforts to integrate data assets into a digital core and redesign the organization around it. Although this work is not over, the impact of artificial intelligence has been clearly reflected in many high-value applications of the company. To compete with Amazon, Wal-Mart is rebuilding its business model around artificial intelligence, replacing traditional, chimney enterprise software systems with an integrated, cloud-based architecture. This will enable Wal-Mart to use its unique data assets in a variety of powerful new applications to automate more and more tasks and improve efficiency through artificial intelligence and data analysis. At Microsoft, Nadella is betting the company's future on the overall transformation of its operating model.

Rethinking Strategy and Capabilities

Rethink strategy and capabilities

As AI-powered firms collide with traditional businesses, competitive advantage is increasingly defined by the ability to shape and control digital networks. See "Why Some Platforms Thrive and Others Don't," HBR, January-February 2019.) Organizations that excel at connecting businesses, aggregating the data that flows among them, and extracting its value through analytics and AI will have the upper hand. Traditional network effects and AI-driven learning curves will reinforce each other, multiplying each other's impact. You can see this dynamic in companies such as Google, Facebook, Tencent, and Alibaba, which have become powerful "hub" firms by accumulating data through their many network connections and building the algorithms necessary to heighten competitive advantages across disparate industries.

With the collision between artificial intelligence-driven enterprises and traditional enterprises, the ability to shape and control digital networks can more and more define competitive advantage. (see Harvard Business Review, January-February 2019, "Why some platforms thrive while others don't"). Organizations that are good at connecting enterprises, aggregating data, and extracting their value through analysis and artificial intelligence will have the upper hand. The traditional network effect and the learning curve driven by artificial intelligence will strengthen and promote each other. You can see this in companies such as Google, Facebook, Tencent and Alibaba. These companies have become powerful "central" enterprises, accumulating data through many network connections and building the necessary algorithms to enhance the competitive advantage of different industries.

Meanwhile, conventional approaches to strategy that focus on traditional industry analysis are becoming increasingly ineffective. Take automotive companies. They're facing a variety of new digital threats, from Uber to Waymo, each coming from outside traditional industry boundaries. 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.

At the same time, the traditional strategic analysis methods focusing on traditional industry analysis are becoming more and more ineffective. In the case of auto companies, for example, they are facing a variety of new digital threats, from Uber to Waymo, each from outside the boundaries of traditional industries. But if auto industry executives can go beyond conventional thinking and see cars as highly interconnected, artificial intelligence-driven services, they can not only protect themselves. new value can also be released through business opportunities, advertising, news and entertainment information, location-based services, etc.

The 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 ata big disadvantage. Instead of focusing on industry analysis and on the management of companies' internal resources, strategy needs to focus on the connections firms create across industries and the flow of data through the networks the firms use.

The advice once given to executives is to stick to the business you are familiar with in an industry you are familiar with. However, the synergistic effect of algorithm and data flow does not respect industry boundaries. Organizations that cannot leverage customers and data across these boundaries may be at a big disadvantage. The strategy needs to focus not on industry analysis and the management of the company's internal resources, but on the cross-industry connections and the data flow in the network the company is using.

All this has major implications for organizations and their employees. Machine learning will transform the nature of almost every job, regardless of occupation, income level, or specialization. Undoubtedly, AI-based operating models can exact a real human toll. Several studies suggest that perhaps half of current work activities may be replaced by AI-enabled systems. We shouldn't be too surprised by that. After all, operating models have long been designed to make many tasks predictable and repeatable. Processes for scanning products at checkout, making lattes, and removing hernias, for instance, benefit from standardization and don't require too much human creativity. While AI improvements will enrich many jobs and generate a variety of interesting opportunities, it seems inevitable that they will also cause widespread dislocation in many occupations.

All these changes have a significant impact on the organization and its employees. Machine learning will change the nature of almost all jobs, regardless of occupation, income level or field of expertise. There is no doubt that the operation model based on artificial intelligence will have a real impact on employment. Several studies have shown that half of the current work may be replaced by artificial intelligence systems. We should not be too surprised by this. After all, the operating model has long been designed to make many tasks predictable and repeatable. For example, the processes for scanning products, making lattes and removing hernias during examinations can all be standardized and do not require much human creativity. While artificial intelligence will enrich many jobs and create interesting opportunities, it seems inevitable that they will also cause widespread confusion and adjustment in many professions.

The dislocations will include not only job replacement but also the erosion of traditional capabilities. In almost every setting, AI-powered firms are taking on highly specialized organizations. 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. When Uber looked for a new CEO, the board hired someone who had previously run a digital firm-Expedia-not a limousine services company.

This confusion and adjustment includes not only the replacement of work, but also the weakening of traditional abilities. In almost every case, artificial intelligence companies are challenging highly specialized organizations. In the artificial intelligence-driven world, competitiveness has less to do with specialization and more to do with general functions in data source, processing, analysis and algorithm development. These new generic capabilities are reshaping strategy, business design, and even leadership. Today, in very diverse digital and networked companies, strategies look similar, as do the drivers of operating performance. Industry expertise has become less important. When Uber was looking for a new chief executive, the board hired someone who had run a digital company, running Epaidi, not a limousine service company.

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. Strategies are shifting away from traditional differentiation based on cost, quality, and brand equity and specialized, vertical expertise and toward advantages like business network position, the accumulation of unique data, and the deployment of sophisticated analytics.

That's why Alibaba and Amazon can compete in completely different industries such as retail and financial services, health care and credit scores. These departments now have many similar technical bases and use common methods and tools.

The Leadership Challenge

Challenge the leadership

Though it can unleash enormous growth, the removal of operating constraints isn't always a good thing. Frictionless systems are prone to instability and hard to stop once they're in motion. Think of a car without brakes or a skier who can't slow down. A digital signal-a viral meme, for instance-can spread rapidly through networks and can be just about impossible to halt, even for the organization that launched it in the first place or an entity that controls the key hubs in a network. Without friction, a video inciting violence or a phony or manipulative headline can quickly spread to billions of people on a variety of networks, even morphing to optimize click-throughs and downloads. If you have a message to send, AI offers a fantastic way to reach vast numbers of people and personalize that message for them. But the marketer's paradise can be a citizen's nightmare.

While it can unleash huge growth, removing operational constraints is not always a good thing. The friction-free system is easy to be unstable and difficult to stop once it is running. Think of a car without brakes or a skier who can't slow down. Digital signals-for example, viral memes (meme)-can spread quickly through the network and are almost impossible to stop, even the organization that originally released it or the entity that controls the key hub of the network. Without friction, a video inciting violence, or a fake or manipulated title, can quickly spread to billions of people over a variety of networks, and can even be transformed to optimize click-through rates and downloads. If you have messages to send, artificial intelligence provides a wonderful way to reach a large number of people and personalize information for them. But a marketer's paradise can be a citizen's nightmare.

Digital operating models can aggregate harm along with value. Even when the intent is positive, the potential downside can be significant. A mistake can expose a large digital network to a destructive cyberattack. Algorithms, if left unchecked, can exacerbate bias and misinformation on a massive scale. Risks can be greatly magnified. Consider the way that digital banks are aggregating consumer savings in an unprecedented fashion. Ant Financial, which now operates one of the largest money market funds in the world, is entrusted with the savings of hundreds of millions of Chinese consumers. The risks that presents are significant, especially for a relatively unproven institution.

The digital operation mode may also gather and magnify the damage while creating value. Even if the intention is positive, the potential negative impact is huge. A single mistake can cause a huge digital network to suffer devastating cyber attacks. If not checked, the algorithm may exacerbate biases and error messages on a large scale. The risk may be greatly magnified. Think of digital banks pooling consumer savings in an unprecedented way. Ant Financial Services Group currently manages one of the world's largest money market funds, which is entrusted with managing the savings of hundreds of millions of Chinese consumers. The risks are enormous, especially for a relatively unproven institution.

Digital scale, scope, and learning create a slew of new challenges-not just privacy and cybersecurity problems, but social turbulence resulting from market concentration, dislocations, and increased inequality. The institutions designed to keep an eye on business-regulatory bodies, for example-are struggling to keep up with all the rapid change.

The size, scope and learning of numbers create a series of new challenges-not only privacy and cyber security issues, but also social unrest caused by market concentration, employment adjustment and rising inequality. For example, the institutions that oversee companies, that is, regulators, are trying to keep up with all these rapid changes.

In an AI-driven world, once an offering's fit with a market is ensured, user numbers, engagement, and revenues can skyrocket. Yet it's increasingly obvious that unconstrained growth is dangerous. The potential for businesses that embrace digital operating models is huge, but the capacity to inflict widespread harm needs to be explicitly considered. Navigating these opportunities and threats will be a real test of leadership for both businesses and public institutions.

In an artificial intelligence-driven world, once the product matches the market, the number of users, participation and revenue will soar. However, it is becoming increasingly clear that unfettered growth is dangerous. Companies that embrace digital operating models have great potential, and their ability to cause widespread harm needs to be taken seriously. Balancing these opportunities and threats will be a real test of the leadership of companies and public institutions.

Introduction to the author

Marco Iansiti is the David Sarnoff Professor of Business Administration at Harvard Business School, where he heads the Technology and Operations Management Unit and the Digital Initiative. He has advised many companies in the technology sector, including Microsoft, Facebook, and Amazon. He is a coauthor (with Karim Lakhani) of the book Competing in the Age of AI (Harvard Business Review Press, 2020).

Marco Iansiti Professor of Business Administration at Harvard Business School, responsible for technology, operations management and digital innovation, provides consulting services to many technology companies, including Microsoft, Facebook and Amazon, and co-authored Competition in the Age of artificial Intelligence with Karim Karim Lakhani.

Karim R. Lakhani is the Charles Edward Wilson Professor of Business Administration and the Dorothy and Michael Hintze Fellow at Harvard Business School and the founder and codirector of the Laboratory for Innovation Science at Harvard. He is a coauthor (with Marco Iansiti) of the book Competing in the Age of AI (Harvard Business Review Press, 2020).

Karim R. Lakhani is a professor of business administration at Harvard Business School and the founder and co-director of Harvard University's Innovation Science Laboratory. He is one of the co-authors of Competition in the Age of artificial Intelligence.

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