Competing in The Age of AI: Strategy and Leadership When Algorithms and Networks Run the World

My 11th book that I’ve read this year was another reference for digital transformation, something that seems currently better driven by the Covid19 pandemic than any other efforts my organization has been doing so far. The book is titled “Competing in The Age of AI: Strategy and Leadership When Algorithms and Networks Run the World” by Marco Iansiti and Karim R. Lakhani from Harvard Business Review Press. It’s a very useful reading to enrich my knowledge on Artificial Intelligence (AI) as one of important aspect on digital transformation. As the summary below will mostly quite from the book, AI is becoming the universal engine of execution. As digital technology increasingly shapes “all of what we do” and enables a rapidly growing number of tasks and processes, AI is becoming the new operational foundation of business—the core of a company’s operating model, defining how the company drives the execution of tasks. AI is not only displacing human activity, it is changing the very concept of the firm.

When a business is driven by AI, software instructions and algorithms make up the critical path in the way the firm deliver value. Having software and algorithms done that has substantial ramifications. Digital, AI-driven processes are more scalable than traditional processes. They enable greater scope (or variety), as they easily connect with myriad of other digitized businesses, and they create powerful opportunities for learning and improvement, such as the ability to produce ever more accurate, complex, and sophisticated predictions and even gain fundamental understanding. The authors’ goal with this book is to provide leaders of organizations old and new, startups, and regulatory institutions a set of frameworks for understanding, competing, and operating in the age of AI. I will only cover a bit of that frameworks here and if you really want to understand, compete and operate in the age of AI, do read the whole book by yourself.

While the previous Industrial Revolution was all about industrializing the production process, analysis and decision making remained largely traditional, idiosyncratic process. Now, the age of AI is manifested by companies driving another fundamental transformation. This one involves industrializing data gathering, analytics, and decision making to reinvent the core of the modern firm, in what the authors call the “AI factory.” The AI factory is the scalable digital operating model of the twenty-first-century firm, Managerial decisions are increasingly embedded in software, which digitizes many processes that have traditionally been carried out by employees. No human auctioneer gets involved in the millions of daily search-ad auctions at Google or Baidu. Dispatchers do not decide which car is chosen on DiDi, Grab, Lyft or Uber. Digital operating models can take various forms. In some cases, they might only manage flows on information (think Ant Financial, Google, or Facebook). In other cases, operating models guide how the company builds, delivers, or operates actual physical products (think Ocado, Amazon, or Way). In either case, AI factories are at the core of the model, guiding the most critical processes and operating decisions, while humans are moved to the edge, off the critical path of value delivery. 

Experience from Netflix and other leading firms underlines the importance of a few essential AI factory components:

1. Data pipeline: This process gathers, inputs, cleans, integrates, processes, and safeguards data in a systematic, sustainable, and scalable way.
2. Algorithm development: The algorithms generate predictions about future states or actions of the business. These algorithms and predictions are the beating heart of the digital firm, driving its most critical activities.
3. Experimentation platform: This is the mechanism through which hypotheses regarding new prediction and decision algorithms are tested to ensure that changes suggested are having the intended (causal) effect.
4. Software infrastructure: These systems embed the pipeline in a consistent and componentized software and computing infrastructure, and connect it as needed and appropriate to internal and external users.   

To implement AI factory in order to transform an organization operating model is always easier said than done. Learning from Microsoft digital transformation journey and other successful organization from the authors’ research, there are five principles for transformation that could serve as insight:

1. One Strategy. The first essential principle in transformation is to develop strategic clarity and commitment. The goals should be stated clearly, as in building an integrated data platform or organizing as agile teams. One key element of the transformation is bringing unity to the company while changing it. Rearchitecting the company’s operating model requires rebuilding the company on a new, integrated foundation. Coordination becomes increasingly essential as interactions across the business multiply. Data knows no functional boundaries, and refocusing the company on a foundation of analytics and AI requires close, multifunctional collaboration to improve results while reducing risks.
2. Architectural Clarity. Second, it’s critical to bring clarity to the technical goals of the transformation. Everyone must understand what you want your future operating architecture to look like. A strong focus on data, analytics and AI requires some centralization and much consistency. Data assets must be integrated across the range of applications for an organization to realize the full benefit of the transformation. One of the biggest surprises in transformation efforts (maybe obvious in retrospect) is the frequent resistance of the CIO and of the IT organization. Many enterprise IT organizations were designed for a different purpose: to operate a complex IT back office, making sure everything works effectively and securely. Traditional IT charters have not included innovation and transformation, and traditional IT skill sets rarely include analytics, let alone AI.
3. Agile, Product-Focused Organization. Developing a product-focused mentality is essential to an AI-centered operating model. The teams deploying AI-centered applications must embed a deep understanding of the application settings they are designed to enable, as with any product-focused effort. Clearly, beyond a new approach to architecture and organization, transformation requires a major cultural shift. Digitizing the operating model really does mean developing a software culture and mindset. It is not about opening a Silicon valley location but about transforming the way the organization feels, from the dress code to reward systems, and from recruiting to compensation. This is not a pilot or a research effort. The focus on changing the core.
4. Capability Foundations. The most obvious challenge in building an AI-centered firm is to grow a deep foundation of capability in software, data sciences, and advanced analytics. Naturally, building this foundation will take time, but much can be done with a small number of motivated, knowledgeable people. More challenging is the realization that the organization need to systematically hire a different kind of person and build an appropriate career path and incentive system. A new generation of business leader should emerge, one who drives a deeper analytics and software mindset across the firm and is fully sensitive to the impact of AI—both helpful and harmful.
5. Clear, Multidisciplinary Governance. As AI becomes increasingly important to each firm, the challenges created by its broader impact on society will only continue to multiply. Digital governance should therefore involve a collaboration across disparate discipline and function. In doing so, it rejuvenates the role of legal and corporate affairs, whose people can be involved in product and policy decisions and not only participating in litigation and lobbying activities. AI requires deep thinking about legal and ethical exposure, and these activities should be actively staffed and supported. Finally, beyond building strong in-house governance processes, an organization should reach outside the firm to engage with its ecosystem of partners and customers, as well as the communities surrounding them.  

Finally, the last thing that I want to put here is about emphasizing that the rules of the game are changing again. As we enter the age of AI, we should pay careful attention to these emerging principles:

Rule 1: Change Is No Longer Localized; It Is Systemic. The age of AI is driven by a relentless and systemic driver of change. Rather than a number of separate waves of technological innovation, gradually spreading the Industrial Revolution across different industries and geographies, our new engine of change appears to be tackling all industries, globally, at just about the same time.

Rule 2: Capabilities are increasingly Horizontal and Universal. We are moving from an era of core competencies, differing from firm to firm and embedded deep in each organization, to an age shaped by data and analytics, powered by algorithms and hosted in the computing cloud for anyone to use. This is why Amazon and Tencent are able to compete in industries as separate as messaging and financial services, video gaming and consumer electronics, health care and credit scoring. Emphasis on primary differentiation on the basis of cost, quality, and brand equity is shifting from specialized, vertical expertise to the firm’s position in the network, its accumulation of differentiated data, and its deployment of a new generation of analytics. 

Rule 3: Traditional Industry Boundaries Are Disappearing; Recombination Is Now The Rule. Industries originally evolved from traditional trades to support the increasingly vertical specialization demanded by the Industrial Revolution. These clear boundaries are going away as widespread digitization drives ubiquitous connections across previously separate industries. We saw it when Google entered the auto industry and when Alibaba launched a bank. Digital interfaces easily allow operating models to cut across old verticals and enter new industries with new, highly connected business models. Industries are thus merging with each other, as capabilities become more universal, as data and analytics refined in one environment can be useful in other contexts, and as digital machines connect easily into massive networks. Digital networks are simply not constrained in the same ways that human-centered organizations are. While traditional organizations suffer from diminishing returns, not only as they grow in size but also as they connect to other networks.

Rule 4: From Constrained Operations to Frictionless Impact. As digital operating models continue to displace traditional processes, they also remove traditional operating constraints. This is why a new generation of firms has grown to unprecedented scale at unprecedented rates. Ant Financial is serving an order of magnitude more customers than the largest traditional bank. Facebook is providing news and information services to an order of magnitude more people than are served by the US Postal system.  

Rule 5: Concentration and Inequality Will Likely Get Worse. As in the Industrial Revolution, transformation drives the redistribution and concentration of wealth. But this time, the phenomenon is exacerbated by the dynamics of digital networks. The evolution of these networks leads to the concentration of the flow of transactions and data, and from that to increased concentration of power and value. The pattern toward concentration creates increased inequality, not only across workers but also across firms, which further segments wealth, power, and relevance across markets, industries, and geographies.  

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