Title: HBR’s 10 Must Reads on Leading Digital Transformation (with bonus article “How Apple Is Organized for Innovation” by Joel M. Podolny and Morten T. Hansen) – Harvard Business Review
Harvard Business Review’s (HBR’s) must-read series curates the most important articles on a particular subject matter as written by the world’s best thought leaders. HBR’s 10 Must Reads series is the definitive collection of ideas and best practices for aspiring and experienced leaders alike. These books offer essential reading selected from the pages of Harvard Business Review on topics critical to the success of every manager.
HBR’s 10 Must Reads on Leading Digital Transformation – Harvard Business Review curated the most important articles on reinventing your digital strategy, overcoming barriers to change, and winning in the continuously connected world.
The ten HBR Must Reads on Leading Digital Transformation
- Discovery-Driven Digital Transformation by Rita McGrath and Ryan McManus;
- The Transformative Business Model,” by Stelios Kavadias, Kostas Ladas, and Christoph Loch;
- Digital Doesn’t Have to Be Disruptive,” by Nathan Furr and Andrew Shipilov;
- What’s Your Data Strategy?,” by Leandro DalleMule and Thomas H. Davenport;
- Competing in the Age of AI by Marco Iansiti and Karim R. Lakhani;
- Building the AI-Powered Organization by Tim Fountaine, Brian McCarthy, and Tamim Saleh
- How Smart, Connected Products Are Transforming Companies by Michael E. Porter and James E. Heppelmann;
- The Age of Continuous Connection by Nicolaj Siggelkow and Christian Terwiesch;
- The Problem with Legacy Ecosystems by Maxwell Wessel, Aaron Levie, and Robert Siegel;
- Your Workforce Is More Adaptable Than You Think by Joseph B. Fuller, Judith K. Wallenstein, Manjari Raman, and Alice de Chalendar;
- How Apple Is Organized for Innovation by Joel M. Podolny and Morten T. Hansen;
- Digital Transformation Comes Down to Talent in Four Key Areas by Thomas H. Davenport and Thomas C. Redman.
HBR’s 10 Must Reads series is the definitive collection of ideas and best practices for aspiring and experienced leaders alike.
Discovery-Driven Digital Transformation by Rita McGrath and Ryan McManus
Idea in Brief
The Problem
Established companies spend billions trying to turn themselves into digitized orchestrators of some new ecosystem, only to fall flat on their faces.
Why It Happens
The CEOs believe that the existential threat posed by digital disrupters requires a gigantic, model-busting response.
The Solution
Adopt an incremental experimental approach: discovery-driven digital transformation. Look for problems to fix with digital technology, but exploit your rich knowledge of customers, broad operational scope, and deep talent pools while learning your way to a new business model.
Applying discovery-driven planning (DDP) approach to digital transformation five key steps:
1. Define the operating experience: It’s not just about digital
2. Focus on specific problems: Identify outcomes and progress metrics
3. Identify your competition: Cast a wide net
4. Look for platforms: Don’t forget the ecosystem implications
5. Test your assumptions: Failures are lessons too
Digital transformation is complex and requires new ways of approaching strategy. Starting big, spending a lot, and assuming you have all the information is likely to produce a full-on attack from corporate antibodies—everything from risk aversion and resentment of your project to simple resistance to change.
A discovery-driven approach gets leaders past the common barriers to digital transformation. By starting small, spending a little on an ongoing portfolio of experiments, and learning a lot, you can win early supporters and early adopters. By then moving quickly and demonstrating clear impact on financial performance indicators, you can build a case for and learn your way into a digital strategy.
You can also use your digitization projects to begin an organizational transformation. As people become more comfortable with the horizontal communications and activities that digital technologies enable, they will also embrace new ways of working.
2. The Transformative Business Model by Stelios Kavadias, Kostas Ladas, and Christoph Loch
Idea in Brief
The Question
No new technology can transform an industry unless a business model can link it to an emerging market need. How can you tell whether a model will succeed in doing that?
The Research
The authors undertook an in-depth analysis of 40 companies that launched new business models in a variety of industries. Some had transformed their industries; others looked promising but ultimately didn’t succeed.
The Findings
Transformative business models tend to include three or more of these features: (1) personalization, (2) a closed-loop process, (3) asset sharing, (4) usage-based pricing, (5) a collaborative ecosystem, and (6) an agile and adaptive organization.
Business Model
Definitions of “business model” vary, but most people would agree that it describes how a company creates and captures value. The features of the model define the customer value proposition and the pricing mechanism, indicate how the company will organize itself and whom it will partner with to produce value, and specify how it will structure its supply chain. Basically, a business model is a system whose various features interact, often in complex ways, to determine the company’s success.
You cannot guarantee the success of an innovation (unless you choose a market niche so small as to be insignificant). But you can load the dice by ensuring that your business model links market needs with emerging technologies. The more such links you can make, the more likely you are to transform your industry.
3. Digital Doesn’t Have to Be Disruptive by Nathan Furr and Andrew Shipilov
Idea in Brief
The Problem
Many managers believe that digital transformation involves a radical disruption of the business, new investments in technology, a complete switch from physical to virtual channels, and the acquisition of tech start-ups.
Why It Happens
Digital technology is being applied to almost every part of company value chains, making it difficult for managers to identify priorities.
How to Fix It
The authors dispel five critical myths about digital transformation and offer executives a better understanding of how to respond to current trends.
Digital Transformation – Adapting an organization’s strategy and structure to capture opportunities enabled by digital technology.
For most companies, even those truly threatened by disruption, digital transformation is not usually about a root-and-branch reimagining of the value proposition or the business model. Rather, it is about both transforming the core using digital tools and discovering and capturing new opportunities enabled by digital.
The keys to success have been a focus on customer needs, organizational flexibility, respect for incremental change, and awareness that new skills and technology must be not only acquired but also protected—something the best traditional companies have always been good at.
4. What’s Your Data Strategy? by Leandro DalleMule and Thomas H. Davenport
Idea in Brief
The Challenge
To remain competitive, companies must wisely manage quantities of data. But data theft is common, flawed or duplicate data sets exist within organizations, and IT is often behind the curve.
The Solution
Companies need a coherent strategy that strikes the proper balance between two types of data management: defensive, such as security and governance, and offensive, such as predictive analytics.
The Execution
Regardless of its industry, a company’s data strategy is rarely static; typically, a chief data officer is in charge of ensuring that it dynamically adjusts as competitive pressures and overall corporate strategy shift.
5. Competing in the Age of AI by Marco Iansiti and Karim R. Lakhani
Idea in Brief
The Market Change
We’re seeing the emergence of a new kind of firm—one in which artificial intelligence is the main source of value creation and delivery.
The Challenge
The AI-driven operating model is blurring the lines that used to separate industries and is upending the rules of business competition.
The Upshot
For digital start-ups and traditional firms alike, 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 the authors called “ The AI factory.” Its software runs 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.
The Four Components of AI Data Pipelines
- 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.
6. Building the AI-Powered Organization by Tim Fountaine, Brian McCarthy, and Tamim Saleh
Idea in Brief
The Problem
Many companies’ efforts to scale up artificial intelligence fall short. That’s because only 8% of firms are engaging in core practices that support widespread adoption.
The Solution
Cutting-edge technology and talent are not enough. Companies must break down organizational and cultural barriers that stand in AI’s way.
The Leadership Imperatives
Leaders must convey the urgency of AI initiatives and their benefits for all; spend at least as much on adoption as on technology; organize AI work on the basis of the company’s AI maturity, business complexity, and innovation pace; and invest in AI education for everyone.
7. How Smart, Connected Products Are Transforming Companies by Michael E. Porter and James E. Heppelmann
Idea in Brief
A Radical Shift
Smart, connected products are forcing companies to redefine their industries and rethink nearly everything they do, beginning with their strategies. This article, the second in a two-part series, focuses on the impact of these products on companies’ operations and organizational structure.
New Relationships
The unprecedented data and capabilities that smart, connected products provide are changing the way firms interact with their customers. Those relationships are becoming continuous and open-ended.
New Processes
The new product capabilities and infrastructure and the data they generate are reshaping the work of virtually every function in the value chain, including product development, IT, manufacturing, logistics, marketing, sales, and after-sale service. In addition, far more intense coordination among functions is now required.
New Structures
New forms of cross-functional collaboration and entirely new functions are emerging. These include New forms of cross-functional collaboration and entirely new functions are emerging. These include unified data organizations, units to continuously improve products postsale, and groups charged with optimizing customer relationships.
8. The Age of Continuous Connection by Nicolaj Siggelkow and Christian Terwiesch
Idea in Brief
The Old Approach
Companies used to interact with customers only episodically when customers came to them.
The New Approach
Today, thanks to new technologies, companies can address customers’ needs the moment they arise—and sometimes even earlier. With connected strategies, firms can build deeper ties with customers and dramatically improve their experiences.
The Upshot
Companies need to make continuous connection a fundamental part of their business models. They can do so with four strategies: respond to desire, curated offering, coach behavior, and automatic execution.
9. The Problem with Legacy Ecosystems by Maxwell Wessel, Aaron Levie, and Robert Siegel
Idea in Brief
The Question
Why do so many well-resourced, historically strong companies fail to keep pace with digital-native challengers?
The Answer
The failure stems partly from how hard it is to walk away from a successful business model. But there’s another, subtler reason: The new disrupters know more about customers, because they have access to better data.
Recommendations
To build effective new business models that take advantage of digital technology, older companies need to agree on the way forward, adopt new performance metrics, and rebuild their supplier, distributor, and partner networks
10. Your Workforce Is More Adaptable Than You Think by Joseph B. Fuller, Judith K. Wallenstein, Manjari Raman, and Alice de Chalendar
Idea in Brief
The Problem
As they try to build a workforce in a climate of perpetual disruption, business leaders worry that their employees can’t—or just won’t—adapt to the big changes that lie ahead. How can companies find people with the skills they will need?
What the Research Shows
Harvard Business School and the BCG Henderson Institute surveyed thousands of business leaders and workers around the world and discovered an important gap in perceptions: Workers are far more willing and able to embrace change than their employers assume.
The Solution
This gap represents an opportunity. Companies need to start thinking of their employees as a reserve of talent and energy that can be tapped by providing smart on-the-job skills training and career development
Bonus: How Apple Is Organized for Innovation by Joel M. Podolny and Morten T. Hansen
Idea in Brief
The Challenge
Major companies competing in many industries struggle to stay abreast of rapidly changing technologies.
One Major Cause
They are typically organized into business units, each with its own set of functions. Thus the key decision-makers—the unit leaders—lack a deep understanding of all the domains that answer to them.
The Apple Model
The company is organized around functions, and expertise aligns with decision rights. Leaders are cross-functionally collaborative and deeply knowledgeable about details.
APPLE IS WELL KNOWN FOR ITS innovations in hardware, software, and services. Thanks to them, it grew from some 8,000 employees and $7 billion in revenue in 1997, the year Steve Jobs returned, to 137,000 employees and $260 billion in revenue in 2019. Much less well known are the organizational design and the associated leadership model that have played a crucial role in the company’s innovation success
Digital Transformation Comes Down to Talent in Four Key Areas by Thomas H. Davenport and Thomas C. Redman
Idea in Brief
Digital transformation requires talent. Assembling the right team of people in four domains—technology, data, process, and organizational change capacity—may be the single most important step that a company contemplating digital transformation can take. Each of these areas requires a certain set of skills.
In the technology domain, you need people with technological depth and breadth, and the ability to work hand-in-hand with the business. Leaders of the technology domain must be great communicators, and they must have strategic sense. You’ll need this same breadth and depth in the next domain: data. You also need the ability to convince large numbers of people at the front lines of organizations to take on new roles as data customers and data creators.
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