“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,” wrote Harvard professors Marco Iansitiand Karim Lakhani in “Competing in the Age of AI”, a recently published article in the Harvard Business Review (HBR). Earlier this year, they also published of the same title, which expands on the ideas in the article and illustrates them with a number of concrete use cases.
The age of AI is being ushered by the emergence of a new kind of digital firm. Rather than just relying on traditional business processes operated by its workers, these firms are leveraging software and data-driven algorithms to eliminate traditional constraints and transform the rules of competition. Managers and engineers are responsible for the design of the new AI-based operational systems, but the system then runs the operations pretty much on its own.
“At the core of the new firm is a decision factory – what we call the AI factory,” note the authors<em>. “[T]he 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… As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur.”
The Industrial Revolution transformed the economy by developing a scalable, repeatable approach to manufacturing. The AI factory is now driving another fundamental transformation by industrializing the data gathering, decision making, and overall digital operations of 21st century firms.
The AI factory involves four key components:
- Data pipeline – the process which systematically gathers, cleans, integrates, and safeguards data;
- Algorithm development, – the component which generate predictions about the future states of the business and drives its most critical operating activities;
- Experimentation platform – the mechanism on which predictions are tested to ensure that they will have the intended effect; and
- Software infrastructure, the systems that embed these various components in software and connect it as needed to the appropriate internal and external users.
Scale, scope, and learning are generally considered the key drivers of a firm’s performance, helping them achieve higher levels of production and lower units costs. But scale inevitably reaches a point of diminishing returns when relying on traditional operating models based on carefully defined processes that require human labor and management decisions. Reinventing firms arounddata-driven AI algorithms removes the traditional constraints that have long restricted their growth, giving them a significant edge over competitors.
“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.”
“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… You need only a computer system to be able to perform tasks traditionally handled by people.”
The book discusses some of the radical changes made possible by removing the limits on scale, scope and learning.
The underlying architecture of a firm is determined by its business model and its operating model. The business model encompasses the overall strategy of the firm, including how it seeks to differentiate itself from competitors by monetizing its unique set of goods or services. The operating model includes the systems, processes and other capabilities that enable the firm to deliver goods and services to its customers. “Whereas the business model creates a goal for value creation and capture, the operating model is the plan to get it done. As such, the operating model is crucial in shaping the actual value of the firm.”
The operating model is the ultimate constraint to achieving the firm’s potential value. Operating models are often quite complex, encompassing the activities of thousands of people, lots of processes, advanced technologies and millions of lines of code. But ultimately, the goals of the operating model are relatively straightforward: deliver value at <em>scale, achieve sufficient scope, and continuously evolve by engaging in sufficient learning. Let me briefly summarize the authors’ arguments.
Scale is all about designing an operating model to deliver as much value to as many customers as possible at the lowest cost. “Classic cases of improving scale involve efficiently increasing production volume or the number of customers served in, say, car production or fast food restaurants. Other examples may involve delivering products of increasing complexity in, say, completing a corporate merger or building an airport.”
Scope is defined as the range of activities the firm performs, such as the variety of products and services it offers its customers. “Some assets and capabilities can help an organization reach economies across diverse kinds of businesses… With efficiencies of scope, firms can create and deliver a variety of goods and services efficiently and consistently.”
Learning drives the continuous improvements that increase operating performance over time, as well as the innovations necessary to develop new products and services. “In recent years, the focus on learning and innovation has increased across the board to deal with threats and capitalize on opportunities.”
Digital firms are now radically changing the scale, scope, and learning paradigms by deploying a fundamentally new kind of operating model. “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.”
The transition from traditional firm to AI-driven organization requires a holistic effort guided by five key principles:
- One strategy – Rearchitecting a firm’s operating model implies rebuilding each business unit around a new, integrated foundation of data, algorithms, and software;
- A clear architecture – Data assets need to be integrated across the firm’s functions and applications to maximize their impact;
- The right capabilities – Rearchitecting the firm’s operating model will take time and require a small number of highly motivated, knowledgeable people;
- An agile “product” focus – Transforming traditional processes into an AI-centric operating model requires a product-focused mentality and a deep understanding of the new use cases being enabled; and
- Multidisciplinary governance – Such a complex technical and organizational transformation requires a well though-out collaboration across the firm’s disparate disciplines and functions.
Finally, Iansiti and Lakhami warn that “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.”
“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.”
