Strategy & Roadmap

The Hard Job of Driving Value from AI

| Charlie Ranalli

Generative AI arrived quickly, with bold promises dominating headlines. But anyone who has lived through past technology shifts knows the real story: the hard job of driving value is not proving it can work – it is adopting it to transform how companies work. Just as with PCs, e-commerce, or mobile, impact will not be fast or easy.

Customers, colleagues, and friends — nearly everyone I exchange perspectives with is already using GenAI to work faster, smarter, and better. That matches findings from a recent paper by Andrew McAfee and colleagues, which shows the technology delivering rapid productivity gains across many occupations. (And yes, I used GenAI tools in researching this article.)

The challenge is that individual gains – especially in knowledge-based work – don’t automatically scale into business or economic impact, at least in most sectors. To matter, they must flow into optimized processes, unit economics, and value levers like prices, margins, or productivity in high-volume workloads. As McAfee and his co-authors note, that means “complementary innovations and organizational reinvention.”

The good news is we finally have some evidence to work with. Early experiments show where AI can create traction and where it tends to stall. They also make one thing clear: getting value out of AI means tough choices and visible commitment from the top team.

The Road to Structural Gains

Across industries, AI pilots are everywhere – copilots for coders, chatbots for service, tools to automate routine tasks. They show what’s possible, but most remain stuck at the pilot stage. Local productivity improves, yet the broader structure of how work gets done stays the same.

This is familiar. Every major technology shift follows a pattern: early enthusiasm, a rush of experimentation, and only later the hard work of embedding new tools into the fabric of business.

The PC revolution is a widely researched case. Email, spreadsheets, and collaboration tools became available in the 1980s. Yet measurable productivity gains only showed up in the mid-1990s, once workflows were redesigned, people retrained, and cultures changed. It took 15 years before the technology showed up in macroeconomic statistics.

E-commerce followed a similar path. Despite 30 years of innovation, investment, and exceptional consumer convenience, only about 16% of U.S. retail spending is online today.

Gartner’s 2025 Hype Cycle places generative AI just past the Peak of Inflated Expectations, signaling that today’s surge of pilots and proofs of concept will only translate into value once organizations take on the harder work of reinvention.

Generative AI is best understood as a general-purpose technology, much like electricity, PCs, or the internet: broad in potential, but dependent on complementary change before its impact can be fully realized. Research by Andrew McAfee and colleagues suggests this wave could diffuse faster than earlier ones because the infrastructure is already in place, tools are widely accessible, and adoption often requires less retraining. Yet the speed of progress still depends heavily on an organization’s own maturity – some have ways of working and modern tech stacks ready to absorb AI, while others face years of groundwork before they can capture meaningful value.

Early signals remain ambiguous. A Stanford study found that workers aged 22–25 in AI-exposed roles saw employment fall by about 13% since 2022, while more experienced peers held steady – suggesting the first effects of AI might be showing up in hiring rather than productivity. A more recent analysis from Yale’s Budget Lab offers a different lens: instead of losses, it examines shifts in the occupational mix – which tasks are being automated, which are being augmented, and how work itself is changing as AI spreads. Apparent declines in some roles may instead reflect a reallocation of tasks. The evidence remains faint — more about changing work than vanishing jobs.

As a technology executive and entrepreneur who lived through the dot-com wave, the rise of e-commerce, mobile, and social media, I’ve seen this pattern repeat: first the hype, a dose of disillusionment, and finally the long, grinding work of structural change. Generative AI may spread faster than past waves, but the hard job to generate value will be the same.

Why AI Value Will Be Slow to Materialize

Integration Matters More than Deployment

Customer service bots, document summarizers, and coding copilots are easy to deploy. They spread fast but don’t transform most industries. The next step is Agentic AI – systems that combine business logic and different types of AI to perform multi-step workflows, interact with humans, and span business functions.

The challenge is integration. Agents don’t operate in isolation. They need redesigned interfaces and workflows: decision points, exception handling, escalation rules, and system integration. As McKinsey observed after studying 50+ agentic AI builds: “It’s not about the agent, it’s about the workflow.”

“Fewer than 10% of generative AI use cases have made it past the pilot stage.”
“Only 21% of organizations using gen AI report that they have fundamentally redesigned at least some workflows.”
“Among the 25 organizational practices tested, workflow redesign has the strongest relationship with reported EBIT impact.”
– McKinsey, State of AI: How Organizations Are Rewiring to Capture Value (2025)

Reengineering Operating Models

Some industries will see direct disruption – translation, for example, where AI can deliver acceptable quality at near-zero cost. But most require operating model reinvention: reconfiguring how decisions are made, how accountability flows, and how value is created.

McKinsey’s State of AI 2025 survey illustrates the gap: “Fewer than 10% of generative AI use cases have made it past the pilot stage.” Tools are spreading, but few firms have revamped their operating models to absorb them.

No Sustainable Edge in Base Technology

Generative AI is trained on public domain knowledge and released broadly. As MIT Sloan has argued, AI itself is unlikely to provide sustainable competitive advantage.

McKinsey’s survey is blunt: “Only 21% of organizations using gen AI report that they have fundamentally redesigned at least some workflows. Among the 25 organizational practices tested, workflow redesign has the strongest relationship with reported EBIT impact.”

The edge comes from differentiated data, proprietary processes, and organizational creativity. Cloud platforms can be acquired easily; unique ways of working cannot.

The Human Factor

AI changes organizational dynamics. Vertical supervising tasks give way to orchestrating systems of people and agents. Leaders must re-skill employees, manage resistance, and build trust in AI-driven systems.

Human-in-the-loop design is critical. Generative AI introduces risks of hallucinations, bias, and IP leakage that can’t be solved by technology alone. In a Harvard Business Review article, Andrew McAfee and his co-authors argue that employees need to be trained to recognize these risks, escalate them, and build confidence in using AI responsibly.

And it’s not only frontline or entry-level roles. A recent MIT Sloan article argues that AI will increasingly automate coordination and monitoring – traditional managerial functions – while creating demand for new roles in oversight, design, and orchestration.

Adoption will be uneven. Early enthusiasts embrace new tools; skeptics push back, especially after errors. Clear accountability, transparent communication, and cultural adaptation are as critical as technical readiness.

Platform Readiness: Data, Systems, and Governance

Finally, structural adoption depends on platform readiness. AI requires clean, governed data — still a rarity in most organizations. Just as critical is the underlying architecture: many ERP, CRM, and supply chain systems are built on rigid interfaces and hard-coded business logic. To take advantage of AI, those systems need more flexible designs that can support dynamic workflows. AI can’t be simply bolted onto legacy systems.

Governance must also mature alongside technology. Privacy, compliance, and risk management can’t wait until systems are scaled if they are exposed to the outside world – they need to be embedded from the start.

The Productivity Promise Is Still Real

Despite these challenges, the long-term upside is enormous. McKinsey estimates that Agentic AI could unlock $450–650 billion annually by 2030 in advanced industries such as manufacturing, logistics, and energy.

Functions like supply chain, software development, and customer service can all be reimagined. But because the base technology is broadly available, the competition to innovate will be fierce. The winners will be those that integrate faster and deeper, building on proprietary assets.

From my experience with past technology shifts, this is where the champions pull ahead. When mobile and social networks disrupted consumer engagement, the companies that succeeded weren’t the ones with the flashiest apps – they were the ones that cleverly reimagined marketing, sales, and service for two-way, mobile-first interactions. The same will be true with AI.

From Experimentation to Transformation

The practices below reflect what I’ve seen in working with clients, along with insights from global leaders and academics. Together, they highlight what helps organizations move beyond experimentation toward real transformation.

Activate the Leadership Team

Transformation begins at the top. This is not just a technology project but a significant change effort. The senior team has the credibility to ask the hard questions, the clout to mobilize resources quickly, and the leverage to remove obstacles. If they aren’t in the room and visibly committed, the rest of the organization will sense it – and resistance will dampen progress.

Map Opportunities

With customer teams, we start broad and deep. The goal isn’t just to identify obvious efficiencies, but to uncover disruptive opportunities. We map ideas across two dimensions: strategic business pillars, and the angles of innovation that AI can unlock.

This stage is about thinking differently – exploring what could change the rules of the game rather than just automate today’s tasks. It’s a step for conceiving opportunities, not a commitment to chase them all.

Invest in a Balanced Portfolio

Once opportunities are conceived, the next step is to commit to a portfolio that balances ambition with pragmatism. In practice, the portfolios that resonate most with leadership teams usually contain three kinds of bets: disruptive ideas that carry risk but could redefine value creation; quick wins that deliver visible results and momentum; and defensive moves that may not create lasting advantage but are quickly becoming industry baseline.

Start with the Questions

AI is not an end in itself. The right starting point is a business question: If I could forecast X more accurately, what would change? If I could automate Y, how much capacity would I free up?

One industrial client is embedding AI agents into a reactive B2B service chatbot. The initial aim is faster response, but the next goal is to extend into proactive commercial processes – identifying cross-sell opportunities, guiding orders, anticipating needs. The key question wasn’t an open-ended “what can AI do,” but “can AI turn knowledge into growth while managing risk up front?”

Advance Initiatives and Capabilities

Run promising use cases first but do so with discipline: set substantial business goals, track adoption and value, and be ready to kill experiments that don’t deliver. In parallel, invest in the capabilities that enable scaling: data assets, governance, orchestration technology, and workforce skills.

One of my retail clients launched a Copilot program to organize and classify knowledge. The first phase focuses on individual and small team productivity, but the explicit goal is to mature into AI-enabled collaborative innovation. Sequencing matters: early initiatives build experience and momentum, while capabilities ensure the organization is ready to accelerate what works.

Close Business, Technology, and Risk Collaboration

AI can’t sit in a technical silo. Business leaders must co-own initiatives. To build trust and ensure each step forward stands on firmer ground, risk and compliance need to be involved from the start. Governance has to be continuous, with clear paths for when agents fail.

One useful approach, highlighted in HBR and practiced by most early adopters, is to move deliberately in stages: experiment in a sandbox, then run tightly scoped pilots, and only scale once risk and governance structures are proven to hold.

The Case for Simplicity

When teams start mapping AI opportunities, the challenge isn’t scarcity – it’s abundance. The possibilities are endless, and the actions required to pursue them quickly become complex and interdependent. Without a simple top-level frame, discussions can fragment across business, operations and technology perspectives.

A practical way to bring structure is to group AI applications into broad categories, for example:

Predictive & Optimization – improving foresight and decision quality through analytics and modeling.

Autonomous Decision & Action – embedding intelligence into processes that can sense and act in real time.

Knowledge-Management & Generative – expertise, content, and amplifying human capability.

End-to-End – connecting and orchestrating workflows across functions to create compound value.

Illustrative opportunity matrix. Sanitized from project material to show structure, not content.

This structure, borrowed from a recent workshop, isn’t meant to be followed verbatim – it must be reframed based on each organization’s industry and AI posture, and paired with strategic pillars to give a full 360-degree view. It simply shows how to organize a portfolio around general concepts rather than specific technologies or use cases.

The Takeaway

AI opens unprecedented opportunities but is not a silver bullet. Pilots and use cases are necessary, but they are not sufficient. Like PCs, e-commerce, and mobile before it, AI’s real value will only come when organizations reinvent their structures to embed it deeply in business and operating models.

The winners won’t be those who run the most experiments. They will be the organizations willing to rewire themselves so that AI becomes part of the fabric of how business is done. For leaders, that means rolling up your sleeves and doing the hard job of transformation.

References

The impact of generative AI as a general-purpose technology
MIT Sloan School of Management, 2024
https://mitsloan.mit.edu/ideas-made-to-matter/impact-generative-ai-a-general-purpose-technology

Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence
Stanford Digital Economy Lab, 2025
https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/

Evaluating the Impact of AI on the Labor Market: The Current State of Affairs
Yale Budget Lab, Yale University, 2025
https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs

One Year of Agentic AI: Six Lessons from the People Doing the Work
McKinsey & Company, 2024
https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work

The State of AI: How Organizations Are Rewiring to Capture Value
McKinsey & Company, 2025
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Why AI Will Not Provide Sustainable Competitive Advantage
MIT Sloan Management Review, 2025
https://sloanreview.mit.edu/article/why-ai-will-not-provide-sustainable-competitive-advantage/

How to Capitalize on Generative AI
Harvard Business Review, 2023
https://hbr.org/2023/11/how-to-capitalize-on-generative-ai

Why Robots Will Displace Managers – and Create Other Jobs
MIT Sloan Management Review, 2025
https://sloanreview.mit.edu/article/why-robots-will-displace-managers-and-create-other-jobs/

The Economic Potential of Generative AI: The Next Productivity Frontier
McKinsey & Company, 2023
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

Six Concepts That Will Shape Your Digital Strategy

Six Concepts That Will Shape Your Digital Strategy: Vision, People, Models, Architecture, Data and Execution
| Charlie Ranalli

We recently published an updated digital strategy framework to help organizations start – or restart – their digital strategies.

Drawing on years of experience and collaboration with visionary leaders, this article explores the six essential concepts at the heart of the framework. These concepts—Vision, People, Models, Architecture, Data, and Execution—offer a clear and actionable foundation for organizations to achieve transformative success, even amid the inherent complexity of the implementation.

Vision: The Catalyst for Change

At the heart of any digital strategy lies a bold and innovative vision. This vision sets the organization on a new path—or accelerates its existing trajectory—by leveraging technology as a transformative force. It is the driver that redefines what’s possible, enabling businesses to challenge and disrupt the status quo.

It’s easy to dismiss Netflix, Amazon and Uber as “digital natives”, but they exemplify how a compelling vision can revolutionize industries. By reimagining entertainment, retail, and transportation, they disrupted decades—and in some cases centuries—of established norms, leaving competitors struggling to catch up.

Netflix executed three transformative strategy pivots over three decades, each reshaping its industry. First, in the late 90s, it delighted customer eliminating late fees and decimated Blockbuster with a DVD delivery subscription model. Ten years later, it revolutionized television with on-demand streaming, replacing traditional viewing habits. Finally, Netflix disrupted Hollywood itself by producing its own content —and established binge-watching, starting with House of Cards— in the process becoming a powerhouse studio and redefining entertainment forever.

A powerful vision doesn’t merely guide technology adoption; it redefines the organization’s mission and purpose in a digital-first world. It inspires teams to think beyond incremental improvements, pushing them to explore entirely new business models and customer experiences. The vision must be ambitious yet achievable, offering a clear direction that unites stakeholders around a common goal.

People: The Driving Force Behind Transformation

While technology is an enabler, people are the true rainmakers of digital innovation. From executives to frontline employees, the success of a digital strategy depends on the creativity, expertise, and commitment of the organization’s workforce.

Digital transformation starts at the top, with leadership embracing their role as champions of change. The CEO and executive team must drive the vision, ensuring alignment across all levels of the organization. However, transformation cannot stop at the leadership level. A digital strategy requires a cultural shift that permeates every corner of the organization, encouraging innovation, collaboration, and a willingness to challenge the status quo.

Empowering teams with the skills, tools, and autonomy to experiment and iterate is critical. As technology becomes increasingly commoditized, it is the ingenuity and determination of people that will differentiate successful organizations from their competitors.

Models: Unlocking Innovation Through Disruption

Identifying opportunities for innovation should be first step of any digital strategy. Yet, even creative business leaders sometimes struggle to envision what to transform, especially in traditional industries where long-standing practices and structures dominate.

This is where the Models component of the framework comes into play. By disassembling the business model, customer lifecycle, and operating frameworks, organizations can uncover hidden opportunities for disruption and growth.

This process of creative deconstruction allows leaders to think like startups—challenging established paradigms and imagining new ways to deliver value. Whether it’s rethinking customer engagement, rewiring supply chains, or introducing new pricing models, the possibilities are endless. The goal is to create structural and lasting competitive advantages that set the organization apart in a crowded marketplace.

Architecture: Building the Foundation for Innovation

Once new business models and processes are identified, the next step is to design a Digital Business Platform that brings them to life. The Architecture component of the framework focuses on building a modular, flexible, and scalable infrastructure that enables rapid innovation and adaptation. This requires more than just adopting state-of-the-art technology—it demands a fundamental rethinking of how processes and models are reflected in technology.

Digital leaders are not afraid to start with a blank slate, rebuilding their IT tech stacks and engineering practices to enable composable designs that align with operational capabilities. These architectures must enable seamless integration across legacy systems, cloud platforms, and emerging technologies.

None of the organizations we worked with over the past decade had architecture functions configured to drive digital innovation effectively from the outset. But with the right approach, they were able to build teams to support agile, customer-centric operations and enable transformative growth.

Data: The Nervous System of the Digital Enterprise

Data is the central nervous system that connects every moving part of the organization. To succeed, businesses must skillfully capture, manage, and analyze data. The goal is to create a unified data repository that removes silos, enables efficient execution and becomes a single source of truth that informs decision-making, uncovers insights, and drives innovation. This often requires overhauling existing data models, architectures, and governance practices.

The importance of data-driven decision-making has grown exponentially with advancements in analytics and AI. These technologies offer organizations unprecedented opportunities to understand customers, optimize operations, and uncover new pockets of growth.

However, building a data-driven culture takes time and effort. It requires robust governance structures, seamless integration of data assets, and a commitment to turning raw data into actionable intelligence.

Execution: Connecting Strategy to Results

The final concept—Execution—is where ambition meets reality. Execution is about connecting all the pieces of the digital strategy with agility, precision, and a relentless focus on value.

It starts with a solid strategic execution discipline, ensuring that initiatives and investments are aligned with business priorities and measured against clear performance targets. Organizations with well-established strategic execution practices have a significant advantage, as they can integrate digital initiatives into existing management processes and drive results more effectively.

Success requires a remarkable commitment to managerial hygiene: plan, execute, measure, adjust, and repeat. While this may sound straightforward, the graveyard of failed digital transformations proves that it’s anything but. According to McKinsey & Company, approximately 70% of digital transformation initiatives fail to achieve their intended goals. Leaders must prioritize alignment, accountability, and continuous learning, ensuring that the organization remains focused on implementing change and delivering meaningful outcomes. Digital innovation is a marathon, not a sprint.

The six concepts—Vision, People, Models, Architecture, Data, and Execution—are the foundation of a successful digital strategy. They offer a structured yet flexible framework that empowers organizations to navigate the complexities of digital innovation with confidence.

Transformation Strategy: An Execution Toolbox

| Charlie Ranalli

Originally published May 2022, updated June 2024.

More than a decade ago, George Westerman, a Research Scientist at MIT Sloan’s Center for Digital Business, and his team embarked on a quest to answer a question that had eluded them thus far: why do companies with comparable investments in technology yield radically different impacts and returns?

The resulting report was named one of the five most influential thought leadership papers of the decade. The revealing finding is that there are two dimensions to measure digital maturity: digital intensity and transformation management intensity. These two ingredients have different impacts on revenue growth and profitability. Digitally intense companies may drive more revenue from their assets, but they are not necessarily more profitable.

The difference? Transformation: mastering the deployment of technology to reshape business models, customer experiences and operating capabilities. Organizations excelling in this second dimension are 9% more profitable than their peers, while those excelling at both dimensions enjoy a 26% increase in profitability over their peers. Conversely, laggards experience a 24% gap in profitability.

It’s common for leaders to feel excited about implementing innovative technologies but less enthusiastic about embarking on the painstaking task of redesigning their business from strategy to every customer touchpoint, process and job description. This toolbox aims to simplify the latter. While digital strategy outlines “what” needs to be achieved to remain relevant in the digital age, transformation strategy defines the “how” of achieving these objectives.

Defining the “How” in Digital Transformation

The methods for crafting digital and transformation strategies differ. As discussed in a previous article (refer to Anatomy of a Digital Strategy), the “strategy matrix” offers a nearly universally applicable framework for a digital vision. It is a logical sequence, starting with a maturity diagnostic, aligning with key strategic business priorities in the “pillars,” formulating ambitious visions for digital innovation initiatives, and then meticulously planning the development of technical, talent, and execution enablers. The matrix feeds a roadmap that establishes a pace of implementation and change, which must be calibrated to the organization’s ambitions, capabilities and resources. This structured approach not only simplifies the process but also channels creative energies toward defining the “what” within the matrix.

The transformation strategy, on the other hand, is highly tailored to the target organization’s initial maturity level, aspirations, operating model, and practical constraints. Over a decade of experimentation, research, and experience by businesses, academics, and consultants has yielded a set of best practices encapsulated in “building blocks” that can be customized to suit the unique needs of individual organizations. I refer to this collection as the transformation toolbox.

Redesigning the Organization for Digital Innovation

At the heart of a transformation strategy is a constant, organic and methodical retooling of the organization. This begins with clearly assigning each “pillar” of the digital strategy to a senior executive. These executives become the champions of their respective domains, driving technology-enabled business model innovations. The Chief Marketing Officer (CMO) could be responsible for digital customer engagement, whereas the Chief Information Officer (CIO) will in most cases be tasked with the pillar of digital infrastructure.

In parallel, middle management must be organically restructured around key “macro-processes” that are pivotal for disruptive change or organic digitalization. This involves identifying processes that are ripe for digital reinvention or enhancements.

The Transformation Office: Central Command

A transformation office serves as the “control tower” of the entire digital transformation process. This centralized entity orchestrates strategic execution, oversees digital initiatives, manages change, and removes obstacles that impede progress. It is responsible for maintaining the momentum of transformation efforts and ensuring that all parts of the organization move cohesively towards the shared digital vision.

The Digital Innovation Hub: Idea Engine

The digital innovation team (more here) is the source of vision, knowledge, and experience in how technologies will transform the way a company does business. It is the heart of the transformation program and will cement the organization’s ability to innovate by identifying and adopting future technologies beyond the initial vision.

While the name may vary depending on company size and convention – Digital Innovation Center in larger corporations, Digital Innovation Team in smaller ones, Excellence Center(s) after becoming a digital leader – the purpose is similar: a compact but assertive group of specialists to lead the design and implementation of initiatives that bring Digital Transformation to life.

Agile and Fusion Teams: Catalysts of Change

Change is driven by people, and in the implementation of digital transformation, Agile and fusion teams are the established approach. Agile teams are small, multidisciplinary groups that work in rapid iterations and adapt business capabilities to change. They are designed to experiment, iterate, and deliver solutions swiftly.

Fusion teams, on the other hand, combine the expertise of traditional business functions with digital savviness. They are instrumental in embedding digital capabilities into the DNA of the organization without disrupting core operations. These teams are essential for ensuring that digital transformation initiatives are not siloed but are integrated across the business.

The composition, management, and governance of these teams are crucial. Typically, the leads (often referred to as “product owners”) should possess extensive experience in the company’s specific business capabilities that the team aims to transform. They report to the line of business, with the transformation office (and if applicable the innovation teams) guiding the inception and vision, establishing processes, providing training, facilitating change and orchestrating governance.

Ideally, these teams will assume operational responsibility of a small fraction of the business (for example a single or a few stores or branches in a retailer or bank, a few routes in a distribution business) to test the digitally transformed models or processes.

This is why business ownership of the outcomes is non-negotiable. These change-focused teams operate in parallel to “live” functions but are not as pressured for short-term results, affording frontline and middle managers additional leeway and resources to experiment safely. However, design and testing cannot occur in isolation, nor can responsibility be solely delegated to the transformation office, innovation functions or project organizations.

Governance Model: Assigning Responsibility and Streamlining Decision Making

The governance model establishes the framework for decision-making, responsibility, and accountability. It is essential for ensuring that digital initiatives are aligned with the strategic objectives and that there is clarity regarding decision rights and responsibilities. This model facilitates effective management of the transformation process, ensuring that resources are allocated efficiently and that initiatives progress as planned.

Most digital transformation programs result in a “federated” model with centralized teams, typically innovation teams and a transformation office, reporting to a C-level executive. These teams establish and orchestrate agile teams operating within business units. The governance model formalizes the mechanics of this federated system, clearly defining leadership responsibilities, operational accountability, decision rights, and effective coordination mechanisms to manage the program, initiatives, and outcomes.

Reference Frameworks: The Blueprint for Transformation

A critical component of the transformation process is the set of reference frameworks. These are comprehensive guidelines that emerge from the digital strategy, dictating the design and implementation of the initiatives.

Customer Lifecycle

The customer lifecycle framework addresses how the organization will attract, serve, and delight customers in a digital context. It outlines models for customer engagement across various touchpoints, leveraging digital tools to create a seamless and personalized customer experience.

Operating Model

The operating model acts as the “internal cabling” of an organization, describing macro-processes in a silo-less manner. This horizontal approach to design end-to-end processes produces customer focused outcomes, efficiency and flexibility, allowing the company to respond swiftly to market changes or customer needs.

Enterprise Architecture

Enterprise architecture defines the technological backbone of the organization. It dictates how technology will be deployed to support and enhance business processes, ensuring that the digital solutions are scalable, secure, and integrated with existing systems.

A common misunderstanding is that the architecture is merely “technical stuff.” While some layers of the architecture are indeed very technical, the uppermost layers describe the business: operating model components, data domains, customer touchpoints, functions and end-to-end processes. Leaders, managers, and teams working on transformative projects all need to know, use, and comply with the enterprise architecture as a frame of reference. This requires a fresh approach from IT leaders and teams, elevating the scope of the architecture from wiring schematics to digitally enabled business capabilities.

The IT Delivery Model: From Services to Value

Redesigning IT delivery mechanisms from a service to a value-driven model is crucial for supporting digital transformations. This shift focuses on aligning IT efforts with the pillars of the digital strategy, success and impact of the initiatives and business outcomes, emphasizing the creation of tangible value rather than supporting business operations.

By adopting a value-centric approach, IT can better support strategic initiatives and support innovation. This transformation requires a fundamental change in mindset, processes, and metrics, ensuring that IT initiatives are directly tied to the organization’s strategic goals. We will explore this topic in greater detail in a future article, discussing practical steps and best practices for achieving this enabling transition.

Digital Transformation Dashboard: Measuring Progress and Impact

Lastly, a digital transformation dashboard is indispensable as the single source of truth to track the transformation journey’s advances. It provides a holistic view of strategic and operational gains, measuring key performance indicators (KPIs) and tracking milestones. This dashboard is crucial for keeping leadership informed and making data-driven decisions.

Conclusion

Digital transformation is not a destination but a journey of continual adaptation and growth. A robust transformation strategy provides the “how” to navigate this journey, ensuring that the organization remains nimble, innovative, and resilient. It requires an orchestrated effort across all levels, a commitment to agile and collaborative ways of working, and a dedication to a customer-centric approach in a technologically advanced business environment.

By embracing these principles and putting in place a structured approach to transformation, businesses can achieve the digital excellence necessary for success in today’s dynamic market landscape.

More on Strategy

Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI

| Charlie Ranalli

Authored by experts at McKinsey, this guide is a comprehensive and insightful exploration into the transformative power of digital technologies and artificial intelligence. They meticulously outline the frameworks and methodologies that successful companies have employed to not only adapt but thrive in the rapidly evolving digital era. It presents a blend of theoretical insights and practical applications, making it a valuable resource for business leaders aiming to navigate the complexities of digital transformation.

One of the standout features of “Rewired” is its pragmatic approach to integrating digital and AI technologies into existing business models. The authors emphasize the importance of aligning digital initiatives with core business strategies, ensuring that technological advancements contribute to business goals. The book is full of case studies and real-world examples, illustrating how various organizations have successfully implemented digital transformations. These examples provide readers with tangible, actionable insights that can be adapted to their unique business contexts. Additionally, the guide addresses common challenges and pitfalls associated with digital transformation, offering solutions and best practices to mitigate risks and maximize returns.

“Rewired” highlights the human aspect of digital transformation, focusing on the crucial role of leadership, culture, and talent management. Aligned with the core tenants of this blog, the authors argue that while technology is a powerful enabler, the true drivers of transformation are the people within the organization. They provide strategies for nurturing a culture of innovation, continuous learning, and agility, which are essential for sustaining competitive advantage in the age of digital and AI. Overall, “Rewired” is an indispensable resource for any business leader seeking to harness the full potential of digital and AI technologies, offering a roadmap to outcompete and excel in the modern business environment.

Rewired: the McKinsey Guide to Outcompeting in the Age of Digital and AI
by Eric Lamarre, Kate Smaje, Rodney Zemme

Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers

| Charlie Ranalli

This is a belated review of a book very, very useful for enterprise intrapreneurs and start-up entrepreneurs.

Business Model Generation provides a comprehensive and practical toolbox to design and evaluate business models: a reference framework (the widely adopted Business Model Canvas), a set of patterns (e.g., unbundling, long tail, multi-sided platforms), design technics (a lot in common with Design Thinking), strategy (in the authors’ own words “approaches to reconsider business strategy though the lens of the business models canvas”) and finally a process for business model design.

It is full of real-life examples of how companies invented or disrupted markets with ingenious business models.

For teams that want to push the boundaries of business model innovation, this book provides an extremely practical framework to walk the exploratory journey with focus and technique.

Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers
by Alexander Osterwalder and Yves Pigneur

Anatomy of a Digital Strategy

| Charlie Ranalli

The strategy is the starting point of the digital transformation journey. Digital leaders have a sound strategy. Strategy is repeatedly cited in digital transformation papers, books, media coverage, and this blog. Strategy, strategy, and more strategy.

So, how does a digital strategy look like?

One important clarification before going further, this post is not a comprehensive strategic thinking methodology to develop a digital transformation strategy. For those beginning the digital journey, the framework provides a practical guide to get started. This post proposes a structure to organize ideas, priorities, initiatives, and projects to describe the digital future and the route to get there, in a logical and understandable format.

Back to the strategy. The details will vary significantly depending on industry characteristics, competitive dynamics, starting maturity, the possibilities available to each company, and the ambition of the leadership team in charge. But experience points to a few common elements.

Structure of a Digital Strategy

Digital Vision

The digital vision describes as precisely and as simply as possible how business will be conducted after being transformed by technology.

Will the means of delivering value to customers change? Can the customer experience be thoroughly reinvented? Can a digitally enabled commercial model change the rules of an industry?

Fractional or pay-per-use, peer-to-peer platforms, anything-as-a-service, open-anything are the sort of models that give digital visions stamina and have a higher chance of putting the leaders behind the strategy in the offensive and the competitors on the back foot.

Not all business or sectors can be wholly reconfigured by technology, but many seem stagnant until a clever entrepreneur comes with a disruptive idea. Simply prescribing more use of technology, for marketing, process automation or old-school ecommerce (it’s almost 30 years old already!) will rarely make a digital strategy disruptive or even competitive enough to move the needle. Combined adoption of several technology innovations, aggressive investment in exceptionally strong capabilities and nimble execution may provide sustainable competitive advantage. Designing and successfully implementing a D2C model that complements the role of traditional channels without creating conflict may also it.

Summary: the digital vision must point to structural and significant changes to how business is conducted, por instance to novel approaches to engage with customers, create value or use assets, enabled by technology. Adopting more platforms, no matter how advanced, is an IT plan, not a digital vision.

Digital Aspiration

The digital vision translated into measurable ambitions: market busting pricing or value creation structures, disruptive growth or market penetration targets, massively reconfigured financial ratios, [probably resulting in] improvements in EBITDA, etc. There are two references to develop the targets: one is bottoms-up, coming from the business cases supporting specific initiatives and investments, the other is top-down: the aggregate jump in financial performance compared with the past, the industry, or benchmarked against digital leaders.

The digital aspiration converts the vision in quantifiable impact, provides targets to align expectations, and supports the case for investment and change. The targets in the digital aspiration should be one of the first exercises of the “single source of truth” practiced by digital leaders. Tweaks and recalibrations are typical in a journey full of uncertainties and experimentations, but if the metrics or targets keep on changing, or different stakeholders look at different versions, it is time to reconsider if things are really going in the right direction.

It is beyond debate that digital leaders drive better results, here is where a leadership team must agree on the drivers that will turn investment and change in quantifiable impact.

Strategic Pillars

These are a few “themes” that support the vision and align the components of the strategy. Some are industry specific (streaming may a theme for media but not for other industries) but some (like customer experience and data-driven business) can be innovation vectors in many industries.

Identifying and adopting the pillars serves several useful purposes:

  • Build consensus on which are the key innovation and value-creation drivers
  • Provide a structure to align priorities, initiatives, technologies, capabilities and investments across teams and business units, and
  • Give the vision and strategy focus, stability, and credibility over time as priorities, initiatives and technologies evolve and shift

The pillars are one – if not the most – significant elements connecting the pieces of the digital strategy. They should be carefully picked and tightly aligned with the vision.

Initiatives and Technologies

These are the concrete plans, actions, and investments to convert the vision in actions and tangible results. They should be described at a very high level in the digital strategy itself, leaving the details to stand alone plans or mission statements for the dedicated agile teams tasked with the execution. (More on this below.)

Some initiatives and technologies will span multiple pillars – in fact these are the more attractive plays because application of multiples technologies to the same business situations have a multiplicative effect in terms of innovation, and if properly executed and the underlying competencies perfected are very difficult to imitate.

An example: bundling industrial equipment “as-as-service” with auto-reordered supplies based on historic consumption rates and real-time customer site inventory spans customer experience, data-driven business and process automation as pillars, and will require integrating remote sensors, advanced analytics, IoT and a highly autonomous B2B e-commerce platform.

The proposed format of compact summaries in each intersection of pillars, initiatives and technologies promotes strategic alignment and consistency. From this point commonly employed strategic planning methodologies like OGSM can be used to define and track measurable goals and actions across different projects, parts of the organization, etc. Organizations with robust strategic planning processes can leverage them.

Capabilities

The vision describes the future, the pillars the change/value drivers and the initiatives and technologies map actions and investments. The capabilities are the enablers.

Most companies will have significant gaps to fill here. Some of are new organizational functions like the digital innovation hub and the transformation office, others are new skills like change management and Agile methodologies, and others are capacities like a modern operating model on the IT function, a modular digital architecture or good data governance. Each is described at more detail in the framework or specific articles.

The example in the picture is representative, but actual strategies can vary significantly. The capabilities section of the strategy will play a significant role in the design of the transformation strategy.

Goals and KPIs

Finally, the goals and KPIs are an execution-grade version of the targets in the digital aspiration, complemented with those from the business cases or initiative-specific.

I recommend using three types of KPIs. The execution indicators track that the basics are happening: people hired or reassigned, projects started, organizational changes implemented. These seem obvious, but when the to-do list is long this basic tracking anticipates roadblocks early on. The second set of transformation indicators track change. These are initiative-specific, but the common denominator is that they confirm that projects are churning along, and the innovative changes are being rolled-out. Then final set is the real thing: the digital KPIs track the actual impact: customers adopting new revenue models or better yet migrating from the competition, shift from human-assisted to fully digital order processing, etc. These are the ultimate proof of success!

Execution and transformation indicators are initiative or project-specific and probably transitory. Once enough execution and change momentum is achieved they can be discarded and focus shifted to the digital indicators that track the deep, disruptive vectors. Some metrics related to capabilities or culture (e.g. mid-level managers fully embracing digital and Agile by leading or originating ideas) may be kept in place for years to track transformation momentum beyond specific initiatives and projects.

A Word About Simplicity

The best digital strategies are surprisingly simple and compact. The structure pictured above can extend to two or three pages after initiatives, technologies, capabilities, and key metrics are broadly described and maybe some business or functional-unit level details are incorporated.

But if it cannot be kept at two or three pages, something is out of place. Details may have to be pushed down to specific action plans, or worse yet, the quantity of initiatives, projects or technologies be unrealistic. A complicated strategy or even a complicated presentation of the strategy can negatively impact communication, comprehension and alignment.

There are usually challenging actions, changes, and investments even behind beautifully simple and focused strategies. If it looks too complicated, it most likely is.

More on Strategy

The New Elements of Digital Transformation

| Charlie Ranalli

The team behind The Digital Advantage: How Digital Leaders Outperform their Peers in Every Industry and The Nine Elements of Digital Transformation reflected on their influential research after surveying 1300 executives in more than 750 global organization.

Their earlier research on digital transformation identified two dimensions through which leading companies outperform their peers: digital capability and transformative leadership capability. They found that the elements of leadership capability have endured, but new elements of digital capability have emerged.

Particularly opportune is the addition of a digital architecture as a critical platform that enables nimble innovation. In my own experience, the lack if a well-designed and implemented architecture prevents the roll-out of initiatives large and small, consuming more technical resources and frustrating business partners.

The New Elements of Digital Transformation
By Didier Bonnet and George Westerman
MIT Sloan Management Review, November 2020

Fast Times: How Digital Winners Set Direction, Learn, and Adapt

| Charlie Ranalli

In Fast Times, a team of McKinsey consultants share the recipe they apply to help their customers be first movers and win the digital race.

“[Fast Times] is for senior executives who are frustrated by the slow pace and limited return on investment (ROI) of their digital transformations, and are unsure what’s holding them back” in the word of the authors.

While not a detailed blueprint to design a Digital Transformation initiative, they cover critical imperatives to develop a Strategy, Capabilities, Adopt and Scale, and they cleverly do it answering provocative questions like “Are you clear about the which transformation model is best for your company?” or “Have you hired digital stars?”

They provide insightful tips on Speed, Scale, Talent and Culture. A must read for leaders already embarked in a digital journey or in need of a reset.

Fast Times: How Digital Winners Set Direction, Learn, and Adapt
by Arun Arora, Peter Dahlstrom, Klemens Hjartar, and Florian Wunderlich

Digital Transformation Is Not About Technology

| Charlie Ranalli

Most business executives think – often reflected by who they bring to the room when you discuss the topic with a CEO – that Digital Transformation is about technology. It is not.

Certainly, top notch technology capabilities are a critical ingredient in all Digital Transformation success stories, but there is a lot more to it. This is the purpose and central theme of this blog: debunking the notion that technology is the most important factor in making a start-up or centuries old companies more competitive with digitally driven innovation, and expanding the field of view of executives to include the broader set ingredients that they will have to mix and match to lead their companies into the digital future.

Digital Before Transformation

Software is eating the world. In a now famous 2011 Wall Street Journal piece, technology entrepreneur and venture capitalist Marc Andreessen brilliantly made the case of why most companies were in the way of becoming software companies – across industries, from entertainment and banking to cars, retail and logistics.

“Six decades into the computer revolution, four decades since the invention of the microprocessor, and two decades into the rise of the modern Internet, all of the technology required to transform industries through software finally works and can be widely delivered at global scale” he wrote.

While banks still own branches, airlines fly planes and Amazon last year ordered 100,000 electric delivery trucks, technology is now in charge of engaging with customers, managing risk and fares, or deploying assets – and make companies winners or losers depending on how good their algorithms are.

Most of the underlying technology has been around for some time. But now its maturity, ubiquity, and affordability – and the ingenuity of the engineers, entrepreneurs and innovators deploying it – are giving it a dramatically more significant role in shaping business models and strategies. A Transformative role.

Time to Transform

Some industries and companies can be wholly reconfigured by technology – remember Blockbuster or Tower Records? – but not all. McKinsey puts is very well: “the number of companies that can operate as pure-play disrupters at global scale are few in number, and rarer still are ecosystem shapers that set de facto standards and gain command of the leverage created by hyperscaling digital platforms.”

But even in asset or activity-intense sectors that can’t be entirely switched to digital-only experiences, modern technology is driving major change, and visionary executives the world over have taken note and quietly started reshaping strategies, business models and organizations to exploit these new opportunities. They updated their leadership styles, cultures and platforms to design and deploy entirely new ways of doing things. They embarked in a Digital Transformation.

The Impact

Academics and consulting outfits have very carefully analyzed the advances in digitization and linked them with financial performance, and the relationship is undeniable.

A seminal two-year study by the MIT Sloan School of Management analyzed more than 400 large firms and found that digitally transformed businesses are 26% more profitable than their industry competitors, drive 9% more revenue through their employees and physical assets and are 12% more valuable than their peers.

The researchers developed a digital maturity model to show how different companies are reacting to technological opportunity, and cleverly analyzed how businesses invest in technology, but more importantly, how the true leaders create the leadership and change management capabilities necessary to drive innovation, which they called transformation management intensity.

They proved that the ability to modernize strategies, organizations and processes is as – or more – important than the technology itself in the quest to be a digital leader.

Another by McKinsey found that focusing on the right digital practices, B2B companies –currently trailing B2C companies in digital maturity – can create long-term value, with the most advanced in their transformation programs driving five times more revenue growth than their peers.

Digital Leadership

Digital transformation is about Strategy, People, Innovation and Execution. Photo by standret.

So, what is Digital Leadership about then?

It is about reinventing strategies, operating models, and processes. It is about putting the customer in the center of attention of the entire organization and designing their experience from the outside in. It is about fact driven decision making and agile change management.

It is about fostering a culture of nonstop innovation and fearless renewal, of mercilessly abandoning established ways of doing things and adopting digitally enabled models.

Technology allows all of this, from the advanced analytics platforms that support decision making and action to the omnichannel platforms that support seamless customer experiences. From process automation to remote collaboration. But despite the broad availability and growing affordability, technologies alone are useless without the leadership to drive change.

Transformation is more important than Digital. And Transformation is about Strategy, People, Innovation and disciplined Execution, the components of the framework proposed in this blog.


Originally written late 2017, updated December 2020.