The document discusses strategies for leading digital transformation, emphasizing the importance of leveraging data and digital capabilities to create value for customers. Key components include a coherent digital strategy, the significance of AI in business operations, and the need for agile and adaptable organizational structures. Additionally, it addresses challenges posed by legacy systems and highlights the necessity of continuous connection and ethical considerations in the age of AI.

1. On Leading Digital Transformation (HBR Press) One Page Book Summary
Edited by Alex G. Lee (https://www.linkedin.com/in/alexgeunholee/)
2. The Transformative
Business Model
3. Digital Doesn’t Have to
Be Disruptive
4. What’s Your
Data Strategy?
1. Discovery-Driven Digital
Transformation
5. Competing in the Age of AI 6. Building the AI-Powered
Organization
7. How Smart, Connected Products Are
Transforming Companies
8. The Age of Continuous
Connection
9. The Problem with Legacy
Ecosystems
10. Your Workforce Is
More Adaptable Than You
Think
The essential part of digital-transformation strategy is using data and digital capabilities to
create new value for customers. A key part of discovery-driven digital transformation is
identifying organizational problems that can be addressed with digital technology, the desired
improvement for each, and a metric for assessing progress toward it.
Business model describes how a company creates
and captures value. Business model defines 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.
Coherent dynamic strategy for organizing, governing, analyzing,
and deploying an organization’s information assets is needed.
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 increasing
revenue, profitability, and customer satisfaction.
To scale up AI, companies must make three shifts:
(1) Develop cross-functional teams with a mix of
skills and perspectives for interdisciplinary
collaboration ; (2) Develop data-driven decision
making procedure at the front line; (3) Develop
agile, experimental, and adaptable mindset
To launch successful AI, leaders should devote
early attention to several tasks:
(1) Explaining why AI is important to the business
and how they’ll fit into a new, AI-oriented culture; (2)
Anticipating unique barriers to change; (3)
Budgeting as much for integration and adoption as
for technology ; 4) Balancing feasibility, time
investment, and value
Through what we call connected
strategies such that companies are
addressing customers’ needs the
moment they arise nd sometimes even
earlier, customers get a dramatically
improved experience, and companies
boost operational efficiencies and lower
costs.
Identifies a problem, describes what a
solution would achieve, and proposes a
way to measure progress on that
solution
Digital
transformative
business model
can link a new
digital technology
to an emerging
market need.
Smart, connected products require (1)
companies to build and support an
entirely new technology infrastructure;
(2) a fundamental rethinking of product
development and manufacturing; (3) a
new organizational structure.
Smart, connected products create (1) new
production requirements and
opportunities; (2) new services.
Identify metrics that are more closely
linked to the specific improvements you
hope digital transformation initiatives
will bring about
Transformative business model
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
Digital transformation means using digital tools to
better serve the known customer.
Digital transformation often enables the elimination of
inefficient intermediaries and costly physical
infrastructure. But that doesn’t mean the physical
goes away entirely.
Digital transformation requires slow replacing of
legacy systems in a modular, agile fashion.
Balancing offense and defense requires
balancing data control and flexibility.
A company’s data architecture describes how
data is collected, stored, transformed,
distributed, and consumed.
A robust a single source of truth (SSOT) for
control and multiple versions of the truth
(MVOTs) data architecture for flexibility are
need.
The SSOT works at the data level; MVOTs
support the management of information.
Competing in the Age of AI
One Page Book Summary
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.
11. How Apple Is
Organized for Innovation
Bonus. Digital
Transformation Comes
Down to Talent in Four
Key Areas
They must function
well together.
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.
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.
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.
Technology is the engine of digital
transformation, data is the fuel, process is the
guidance system, and organizational change
capability is the landing gear.

2. Competing in the Age of AI (HBR Press) One Page Book Summary
Edited by Alex G. Lee (https://www.linkedin.com/in/alexgeunholee/)
2. Rethinking the Firm
3. The AI Factory
4. Rearchitecting
the Firm
1. The Age of AI
5. Becoming an AI Company 6. Strategy for a New Age
7. Strategic Collisions
8. The Ethics of Digital Scale,
Scope, and Learning
9. The New Meta
10. A Leadership Mandate
a. AI is transforming the very nature of companies—how they operate and how they compete.
b. AI is restructuring the economy.
Understanding the new
opportunities and challenges
has become essential, and
many time-honored
assumptions about strategy
and leadership no longer
apply.
Emergence of firms that are
designed and architected to
release the full potential of
digital networks, data,
algorithms, and AI (digital
operating models).
Case studies of three digital unicorns: Ant Financial,
Ocado, and Peloton
Firms need a fundamentally different operating architecture
to remove constraints on firm scale, growth, and learning
exploiting the full power of digital networks and AI.
Operating architecture for an AI-powered firm: a
common foundation of data inputs, software
technology and algorithms that are provided by an
AI factory, easily accessible (but carefully designed
and secure) interfaces that agile teams developing
individual applications can use.
Digital firms enable and require a
new approach to strategy exploiting
the digital network and learning
effects.
Leaders should be aware of how their newly
deployed digital capabilities can be misused
in ways they never intended-or possibly even
imagined.
The age of AI is defining a new
set of challenges for leaders.
Amazon's digital operating model
illustrates the advantages of digital
scale, scope, and learning. Its digital
systems scale more easily and
continue to improve despite the size
and complexity of its operation.
Digital unicorns’ operating model (delivers value) enables
striking capacity to drive scale, scope, and learning and their
business model enables creation and capture of value without
operational limitation.
The AI factory is the scalable decision engine that enable
data-driven and AI-driven automation, analysis, and
insights and powers the digital operating model of the
twenty-first-century firm.
AI factory components: the AI algorithms that make predictions
and influence decisions, the data pipeline that feeds them, and
the software, connectivity, and infrastructure that power them.
To become an AI-enabled firm that can leverage the power of
data, networks, and AI requires the transformation journey
of deploying a digital operating model.
Five guiding principles that characterize an
effective transformation process:
1. Development of strategic clarity and commitment
2. Development of a clear operating architecture
3. Development of a product-focused agile
organization
4. Development of a deep foundation of capability in
software, data sciences, and advanced analytics
5. Development of a clear multidisciplinary
governance
The stronger the network and
learning effects, the sharper the
increase in value with scale:
a. The most important value
creation dynamic of a digital
operating model is its network
effects.
b. Learning effects can either add
value to existing network effects or
generate value in their own right.
A new generation of digital operating
models transform the economics and
nature of service delivery, and thus,
competition.
As collisions between digital firms and
traditional firms multiply across the
economy, different industries become
increasingly connected to each other
coalescing around a small number of
digital superpowers (hub firms).
Ethical challenges created by the combination of
digital networks and AI: digital amplification,
algorithmic bias, data security and privacy, platform
control, fairness and equity
The age of AI is changing the rules of the game. The new rules are
defining the new age, shaping key arenas, and transforming our
collective future.
Rule 1: The age of AI is driven by a relentless and systemic driver
of change.
Rule 2: AI-driven world has more to do with a universal and
horizontal capabilities.
Rule 3: Traditional industry boundaries are disappearing.
Rule 4: As digital operating models continue to displace traditional
industrial processes, they also remove traditional operating
constraints.
Rule 5: Concentration and inequality will likely get worse.
Enterprise transformation
Entrepreneurial opportunity
Regulation
Community