Differences, Similarities, and Roles of Business Models and Business Plans

1. Introduction 

• Utterback and Abernathy (1975) categorized technological innovation into Product Innovation and Process Innovation as distinct innovation patterns.
• Product innovation focuses on "What to make", while process innovation focuses on "How to make".
• The technological innovation process evolves over time as industries develop.

• Fluid Phase
• When a new product is introduced, various competing designs emerge until a standardized dominant design becomes established.
• Once a dominant design gains traction, innovation shifts toward incremental improvements based on the standardized product.

• Transitional Phase
• As the dominant design spreads and demand increases,
• The focus shifts from product improvements to enhancing production efficiency and solving process-related challenges, leading to a surge in process innovation.

• Specific Phase
• Process innovation becomes more stabilized and refined.
• The type of innovation varies depending on the product category.

Differences in Innovation by Product Type

1️⃣ Assembled Products

Assembled products involve the combination of multiple components through an integrated production process. These industries focus primarily on product innovation, as competition is driven by improvements in design, functionality, and performance. Examples include mainframe computers, aircraft, and automobiles.

In these industries, component manufacturers maintain distinct product characteristics, allowing for continuous differentiation. While process innovation plays a role, the primary emphasis is on enhancing product features to meet market demands and improve competitiveness.

2️⃣ Non-Assembled Products

Non-assembled products, in contrast, rely on raw material transformation processes, where the final product remains largely unchanged. The key focus in these industries is process innovation, aimed at improving efficiency, reducing costs, and optimizing production techniques. Examples include glass manufacturing, steel production, textiles, and petroleum processing.

Since these products do not undergo significant design changes, companies concentrate on refining manufacturing processes rather than developing entirely new product variations. Process advancements, such as adopting new refining methods or automation technologies, drive competitiveness in these industries.

Overall, assembled product industries prioritize product innovation to enhance functionality and differentiation, whereas non-assembled product industries emphasize process innovation to improve production efficiency and cost-effectiveness.

Technology Forecasting vs. Technology Foresight

1. Introduction

As technology continues to evolve at an unprecedented pace, both businesses and nations must anticipate and prepare for future technological advancements. There are two primary approaches to predicting the future of technology: Technology Forecasting and Technology Foresight. Although these terms may seem similar, they differ in their approach and purpose.

2. Technology Forecasting vs. Technology Foresight

Technology Forecasting

Technology Forecasting is the process of analyzing past and present data to quantitatively predict the future development of technology. It focuses on estimating when and to what extent a particular technology will advance using statistical techniques, trend analysis, and lifecycle models.

Technology forecasting is a trend analysis method that predicts the future based on past data, assuming that the trends observed from the past to the present will continue into the future.

It involves identifying the function that best represents the trend in past data and using this function to derive predicted values for future points in time.

Technology forecasting has traditionally been widely used because it provides clear logical reasoning and produces quantitative results.

This approach is based on a linear prediction model, which assumes that the future can be anticipated by extrapolating from past experiences.

• Key Methodologies:
  • Trend Extrapolation: Predicting future trends based on historical technological advancements.
  • Technology Life Cycle Analysis: Analyzing the introduction, growth, maturity, and decline stages of a technology.
  • Delphi Method: Gathering and refining expert opinions through iterative surveys to predict technological progress.
  • Patent Analysis: Examining patent application data to track technology development trends.

Technology Foresight

Technology Foresight is not merely about predicting technological advancements but also about preparing for the future by considering the broader impact of technology on society, the economy, and the environment. Unlike Technology Forecasting, it emphasizes qualitative analysis over quantitative predictions and requires the participation of various stakeholders, including governments, businesses, research institutions, and the general public.

The future is determined not only by technology but also by various factors such as the economy, society, and institutions. Therefore, a backcasting approach is used, where a future vision is first established, and then a step-by-step plan is devised in reverse to achieve it.

Peter Drucker famously said, "The best way to predict the future is to invent it."

This approach involves deriving a future vision by analyzing science and technology, market needs, and socio-economic issues, followed by the development of a phased strategic plan.

• Key Methodologies:
  • Scenario Planning: Developing multiple possible future scenarios and formulating corresponding strategies.
  • Roadmapping: Identifying gaps between current and future technologies and outlining pathways for development.
  • Morphological Analysis: Exploring possible futures by combining various influencing factors.
  • Cross-Impact Analysis: Analyzing how different technologies and societal factors interact and influence each other.

3. Conclusion

In the past, predicting the future relied heavily on Technology Forecasting. However, as technology becomes increasingly intertwined with society, the economy, and the environment, the role of Technology Foresight is becoming more critical.

With the integration of cutting-edge technologies like artificial intelligence, biotechnology, and renewable energy into traditional industries, simply forecasting technological progress is no longer sufficient. Modern Technology Foresight adopts a hybrid approach that combines data analysis with expert insights, utilizing methodologies such as scenario planning and roadmapping. Additionally, AI and big data-driven predictive techniques have significantly improved the accuracy of future projections.

Ultimately, the most effective way to prepare for the future is to leverage both Technology Forecasting and Technology Foresight. This allows for a comprehensive analysis of technological advancements while also considering their societal implications.

Most importantly, the increasing focus on Technology Foresight underscores that true progress happens when communities come together to drive sustainable innovation and actively shape the future they envision.

source : https://commons.wikimedia.org/wiki/File:Bundesarchiv_Bild_183-1990-1126-500,_Kraftdroschke.jpg

The Electric Vehicle Market

In the early 1900s, the U.S. automobile market was divided among steam-powered cars (40%), electric vehicles (38%), and internal combustion engine (ICE) cars (22%). By 1917, The Wall Street Journal predicted that the emergence of affordable electric cars would transform the auto industry. However, the 1920s ushered in the era of oil, fundamentally shifting the landscape.

Gasoline prices plummeted, and advances in internal combustion engines improved range and efficiency, driving electric vehicles out of the mainstream market. For nearly a century, EVs remained a relic of the past—until Tesla launched the Roadster in 2006, sparking the resurgence of electric cars.

As of 2025, the EV market faces significant challenges, including high costs, limited charging infrastructure, and range anxiety. On top of that, battery manufacturers betting on EV adoption are struggling with financial difficulties. The road to EV dominance is paved with obstacles, and the ICE car still holds its ground, bolstered by oil-focused policies and lower production costs.

The Revival of "Obsolete" Technologies

One of the greatest pitfalls in predicting the future is underestimating the potential comeback of technologies once deemed obsolete. Take IBM’s mainframe computers as an example: once thought to be outdated, they continue to play critical roles in specific industries.

The transition from steamships to internal combustion-powered vessels took over 60 years. Similarly, when Steve Jobs unveiled the iPad, he confidently declared that tablets would replace laptops. Yet, by 2023, global laptop shipments stood firm at 166 million units annually, compared to 135.3 million tablets—a 10% year-over-year decline for tablets.

In the Trump 2.0 era, policies aimed at reducing oil prices may extend the life of ICE vehicles even further. Lower fuel costs could reinforce the competitiveness of ICE cars, making it more challenging for EVs to achieve widespread adoption. Instead of dismissing traditional markets and past technologies, we need to examine the broader context of change itself.

Interactions Between Innovation and Market Dynamics

When disruptive technologies reconfigure markets, incumbent technologies rarely disappear overnight. For instance, internet banking made its debut in the U.S. in October 1995, with the launch of Security First Network Bank, the world’s first internet-only bank (Clark & Lee, 1998).

Despite the rapid rise of digital financial services, U.S. bank branch numbers continued to increase until 2019. According to the Federal Reserve Bank of Philadelphia, branch numbers fell only recently, from 96,104 in 2019 to 90,691 in 2023—a modest 5.6% decline.

This trend highlights that the expansion of digital banking doesn’t necessarily lead to the immediate decline of physical branches or ATMs. Customer behavior changes more slowly than anticipated, and a balanced approach is essential to meet diverse needs. Innovation in the marketplace is rarely just about the technology—it’s the result of a complex interplay of factors, from consumer behavior to economic policies.

Revisiting Nokia's Fall

When Apple launched the iPhone in 2007, Nokia still dominated the global mobile phone market. In 2008, Nokia’s Symbian operating system held a 48% market share, far ahead of competitors. But then came Android. By 2012, Android’s market share had skyrocketed to 74%, Apple’s iOS held 18.2%, and Symbian plummeted to just 0.6%.

Fast-forward to 2023: Android remains the dominant player with a 75% market share, followed by iOS at 23%, while other operating systems account for a mere 2%. Android owes its dominance to its open ecosystem and wide array of hardware manufacturers, giving customers more choices and fostering a robust developer environment. Apple, on the other hand, leverages its premium strategy and tightly integrated ecosystem to build loyalty and maintain its stronghold.

Nokia’s downfall illustrates a critical lesson: market success depends not only on technological superiority but also on the strength of an ecosystem and business model. Companies overly focused on short-term financial performance, without the agility to adapt to shifting customer demands and market conditions, are at risk of rapid decline. Even the largest incumbents must remain vigilant; size may buy time, but it doesn’t guarantee survival.

Conclusion
Innovation doesn’t exist in isolation—it’s shaped by the interplay of technology, market forces, and consumer preferences. The lessons from electric vehicles, internet banking, and Nokia remind us that staying ahead requires more than just great technology; it demands a strategic balance of adaptability, foresight, and resilience in a constantly changing world.

Lewin, K. (1951). Field Theory in Social Science: A Comprehensive Analysis

Kurt Lewin’s Field Theory in Social Science (1951) provides a theoretical framework to explain the complex processes of social, psychological, and organizational change. Lewin described change as a three-step process consisting of "Unfreezing," "Moving," and "Refreezing." Through this process, individuals and systems transition from a static state to a new equilibrium, maintained by a balance of forces known as the "force field."

In this book, Lewin emphasized that human behavior does not occur in isolation but through continuous interaction with the environment. This interaction occurs within the "psychological field," a dynamic space influenced by environmental and social factors.

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Key Concepts

Psychological Field : The psychological field encompasses all environmental and social factors affecting an individual’s behavior. It is dynamic and fluid, essential for understanding behavior in specific contexts.

Lewin defined this space as the "life space," which constantly evolves through the interaction between individuals and their environments. 

Dynamics of Forces : Lewin explained behavior as a balance between "driving forces" and "resisting forces." Driving forces promote change, while resisting forces hinder it.

Three-Step Change Model : Lewin conceptualized change as a three-step process

1. Unfreezing : Breaking existing behaviors or practices and preparing for change.

2. Moving : Introducing and implementing new behaviors, ideas, or structures.

3. Refreezing : Stabilizing the new behaviors as the standard within the organization.

Group Dynamics : Lewin highlighted the importance of group interactions, noting that group norms and roles significantly influence individual behavior.

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Applications in Organizations

Lewin’s field theory and three-step model offer valuable frameworks for managing organizational change and designing effective processes. Key applications include:

Organizational Change Management : Organizations can systematically manage each phase of change to minimize resistance and ensure successful transitions. For example, when adopting new technologies, the unfreezing phase involves reviewing existing systems, the moving phase includes implementing the technology, and the refreezing phase ensures optimized usage.

Conflict Resolution : Organizational conflicts can be analyzed as imbalances between driving and resisting forces. Understanding this dynamic helps develop strategies to resolve conflicts effectively.

Leadership Development : Leaders act as driving forces for change. Lewin’s theory assists leaders in persuading stakeholders of the need for change and engaging them in the process. (However, in some scenarios, ineffective leadership may act as resisting forces while employees become the driving forces.)

Education and Training : Lewin’s group dynamics concept can be used to analyze the roles and interactions within groups to design effective training programs and foster collaborative learning environments.

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Case Studies

GE’s Change Management : General Electric systematically utilized Lewin’s model to manage organizational change. By reassessing (unfreezing), experimenting with new strategies (moving), and standardizing successful practices (refreezing), GE ensured effective implementation.

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Limitations and Criticisms 

Simplification Risk : The three-step model tends to oversimplify change processes. In modern, complex organizations, change is often non-linear, with simultaneous occurrences of unfreezing, moving, and refreezing.

Limitations in Dynamic Environments : Lewin’s model assumes relatively stable environments. However, modern organizations face rapidly changing market and technological landscapes.

Ambiguity in Psychological Field Definition : The concept of the psychological field is broad, making practical applications challenging in some contexts.

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Conclusion 

Lewin’s Field Theory in Social Science provides a foundational framework for understanding and managing change. His concepts of the psychological field, force dynamics, and the three-step change model remain relevant across organizational change management, leadership development, education, and conflict resolution. To ensure successful application, organizations must adapt Lewin’s theories to contemporary and dynamic contexts.

In the year 2000, Hilti famously shifted its business model from simply manufacturing and selling power tools to offering equipment management services. This change is summed up in a well-known saying:

"What customers really want is not the drill, but the hole."

This means that customers are more interested in the results that tools can achieve rather than the tools themselves.

As with all innovations, moving from selling equipment to renting it was not an easy task. It involved a fundamental change in how the business operated. Selling products involves managing inventory, but it also means getting paid upfront. 

This method is relatively simple and has been the standard for most companies. On the other hand, "equipment rental" requires ongoing management and maintenance of the rental items. It also involves recovering the initial large investment over time, which can pose cash flow risks.

Shifting from focusing on the 'drill' to the 'hole' is challenging in any industry. Changing a business model is difficult because it must overcome internal resistance and unexpected challenges. Therefore, even the smartest people in a company, who understand that customers want the 'hole' and not the 'drill,' often avoid talking about the 'hole.' Instead, they focus on improving the 'drill.'

 1. Introduction

Despite heavy investments, previous AI adoptions, including Big Data Lakes, often fell short of expectations. Although infrastructure and funding were in place, they failed to generate substantial business value. However, 2025 is set to be the turning point, with B2B-specific AI agents progressively meeting expectations. Companies will prioritize technologies that deliver financial results, focusing on reducing labor costs and enhancing efficiency through automation. In this regard, AI agents will play a pivotal role in organizational streamlining.

2. Key Differences Between Traditional Language Models and AI Agents

• Scope of Use
• Traditional Models: Primarily used for text generation and summarization with limited applications.
• AI Agents: Designed for complex tasks and goal achievement, enabling automation across diverse business processes.
• Planning and Execution
• Traditional Models: Limited capability to independently plan and execute tasks.
• AI Agents: Capable of independently planning and executing tasks to achieve goals with minimal human oversight.
• Autonomy
• Traditional Models: Operate reactively based on user input without decision-making capabilities.
• AI Agents: Possess autonomy to independently determine and execute actions required to achieve predefined goals.
• Integration with Tools and Systems
• Traditional Models: Limited ability to integrate with external tools or systems.
• AI Agents: Seamlessly integrate with various tools and systems to automate complex tasks and enhance efficiency.
• Learning and Adaptation
• Traditional Models: Operate based on pre-trained data with limited adaptability to new situations.
• AI Agents: Continuously learn from new data and adapt to environmental changes, improving performance over time.

These distinctions position AI agents as transformative solutions across areas such as business process automation, customer service enhancement, and data analytics.

3. The Rise of AI Agents in 2025

Gartner defines AI agents as "Agentic AI" in its "Top 10 Strategic Technology Trends for 2025." These agents autonomously plan and act to achieve user-defined goals, automating tasks traditionally performed by humans or existing applications.

Capgemini's "2025 Technology Trends" report predicts that by 2025, 82% of companies will adopt AI agents. The market size is expected to grow from $5.1 billion in 2024 to $47.1 billion by 2030 (Source).

• 51% of companies are expected to partially or fully implement AI agents by 2025.
• Among large enterprises with annual revenues over $10 billion, 45% have already adopted AI agents, compared to 21% of smaller companies.

AI agents are likely to become specialized platforms tailored to industry knowledge or services, creating a robust B2B ecosystem. However, their widespread adoption could lead to workforce restructuring, potentially contributing to job displacement and economic polarization.

4. Industry Use Cases

Microsoft: Magentic-One Framework

• Overview: Microsoft introduced Magentic-One, an open-source multi-agent framework based on Microsoft AutoGen. It allows for the easy development of multi-agent applications.

• Key Features:
• Includes an orchestrator coordinating multiple support agents like WebSurfer (web exploration), FileSurfer (local file navigation), and Coder (code generation and execution).
• The orchestrator plans tasks, monitors progress, and adjusts strategies as needed.
  (PDF : Microsoft Magentic-One Framework)

Salesforce: Agentforce 2.0

• Upgrading its generative AI platform for enterprises, Agentforce 2.0, to offer enhanced customization and scalability.

Stripe: AI SDK for Financial Transactions

• Overview: Stripe introduced an AI SDK enabling LLMs to handle financial transactions such as payments and invoicing.
• Key Features:
• Integration with platforms like Vercel and LangChainAI, allowing simplified access to APIs.

Dell: AI Factory with NVIDIA

• Overview: Dell, in collaboration with NVIDIA, launched the Dell AI Factory, a comprehensive solution for developing, training, and deploying large-scale machine learning models.
• Key Features:
• Supports high-performance computing (HPC) and provides enterprise-grade infrastructure for AI adoption.

Morgan Stanley: AI for Wealth Management

• Through its partnership with OpenAI, Morgan Stanley provides high-value clients with personalized financial advisory services powered by AI.

LinkedIn: GPT-4 Career Coach

• Leverages OpenAI's GPT-4 to offer career coaching, resume analysis, and job matching services.

Intel: CRM AI for Customer Engagement

• Overview: Intel’s AI solutions analyze customer behavior and emotions, delivering personalized support through CRM tools.

Oracle: AI-Powered HCM Solutions

• Overview: Oracle’s AI-powered HCM systems streamline HR processes like hiring and employee management while ensuring data security.

Strategy for the Hypercompetitive Era: You Must Overturn the Status Quo to Survive!

In the era of hypercompetition, businesses cannot simply wait for a "home run." Instead, they must focus on getting the leadoff hitter on base, followed by sacrifice bunts, stolen bases, and consistent hits to advance their position.

Success comes from disrupting the status quo and shaking the foundations of the existing game. By doing so, companies can create temporary advantages, adapt to rapid changes, and ultimately win in a constantly evolving competitive environment.

 1. User-Driven Innovation

"User-driven innovation" originates from the research of Eric von Hippel, a professor at MIT's Sloan School of Management and the founder of the user-driven innovation theory. His notable works include The Sources of Innovation (1988) and Democratizing Innovation (2005). Von Hippel has explored the impact of user innovation on businesses and the broader economy, introducing a groundbreaking paradigm in innovation studies.

• You can download his books here. (link)

Source : https://evhippel.mit.edu/

He argued that key ideas and technical solutions for innovation often originate not within companies but from users who directly interact with the product or service. This stands apart from traditional innovation theories by emphasizing that users, as "Lead Users," actively recognize needs and voluntarily attempt to innovate to address them.

"Lead Users" are those who encounter problems earlier than the average consumer and explore innovative solutions to address these issues. In doing so, they often generate creative ideas that companies may overlook or fail to imagine.

 Open Innovation: The Case of P&G

source : https://www.pgconnectdevelop.com/

In today’s economy, “innovation” has become the most critical driving force for sustained corporate growth. Among various types of innovation, Open Innovation—a model that actively leverages external resources and ideas—has been increasingly highlighted for its significance.

In an era where it is challenging to keep pace with the speed and scope of technological advancements, how can organizations effectively utilize external resources for innovation? The answer lies in open innovation.

To summarize, open innovation is a methodology that accelerates the innovation process and disperses risk by integrating external knowledge, technology, and creative ideas, rather than relying solely on internal research and development (R&D).

Henry Chesbrough

source : https://haas.berkeley.edu/faculty/chesbrough-henry/

The Definition and Core Concepts of Open Innovation

Traditional innovation models typically involved companies developing new products or technologies through internal R&D and maintaining exclusive control over them. However, as information and technology became globalized and knowledge flows accelerated, it became increasingly difficult for companies to maintain competitiveness with internal resources alone. Open innovation emerged as a response, encouraging businesses to adopt creative ideas and technologies from outside, while also sharing their own technologies with others.

The core of open innovation is the flow of ideas and technologies beyond corporate boundaries. Companies must collaborate not only within their internal structures but also with external research institutes, startups, universities, and even competitors. This enables organizations to identify innovative technologies early and quickly commercialize them. Additionally, open innovation allows businesses to commercialize ideas and technologies in various ways, not limited to their own product development.

P&G’s Open Innovation Model

P&G (Procter & Gamble) serves as a prime example of a company that successfully implemented open innovation through its “Connect and Develop” program.

Connect + Develop Platform

P&G launched its Connect + Develop program in 2001 to enhance its global R&D network and actively incorporate external ideas and technologies. This platform integrates innovative external technologies or ideas into P&G’s products and processes. P&G collaborates with diverse external sources, including universities, research institutes, startups, corporations, and individual inventors.

By doing so, P&G accelerates the pace of innovation, reduces reliance on internal development models, and responds swiftly to market changes.

Major Programs within Connect + Develop

source : https://www.pgconnectdevelop.com/innovation-roadmap

1. Technology Partnerships
   P&G collaborates with companies and research institutions across various industries to drive technological innovation. These partnerships enable the company to quickly bridge technological gaps and apply cutting-edge advancements to its products.

2. Open Idea and Technology Sharing
   The Connect + Develop platform provides an online portal for external innovators to share their ideas or technologies with P&G. This allows P&G to evaluate diverse external ideas and incorporate suitable technologies into its R&D efforts.

3. Innovation Competitions
   P&G regularly hosts innovation competitions to gather creative ideas and technologies from around the world for product or service improvements. Selected ideas are directly reflected in P&G’s product development and commercialization processes.

4. Joint Ventures
   Through joint ventures, P&G partners with external organizations to co-invest in innovation projects. This model helps distribute risks and efficiently utilize resources.

5. Licensing Proprietary Technologies
   P&G also licenses its innovative technologies and product ideas to other companies, generating additional revenue while enabling its technologies to be applied across various industries.

Examples of P&G’s Open Innovation Successes

1. Olay Regenerist
   Olay Regenerist, an innovative skincare product with regenerative properties, was developed in collaboration with external research institutes.

   • At a European conference, P&G discovered peptide technology from a small French cosmetics company and integrated it into its product. This resulted in a $2 billion brand.
     
     
     
     

2. Swiffer
   

   Swiffer cleaning products were born from collaboration under the Connect + Develop program, introducing revolutionary changes in cleaning methods.
   
   
   • P&G worked with the design firm Continuum to develop the Swiffer product line. Launched in 1999 with the principle of “quick cleaning,” the Swiffer achieved first-year sales of $100 million and became a household favorite..
     
     

3. Tide Pods
   Tide Pods, a flagship laundry detergent product, were created by adopting innovative technology through Connect + Develop.
   

   
   
   
   
   
   
   • P&G collaborated with MonoSol to develop dissolvable film technology, enabling the creation of capsule-based detergents. From 2013 to 2018, Tide Pods sales grew by 136%, contributing to 25% of P&G’s total laundry detergent revenue.

Impact of Connect + Develop

The program has accelerated innovation, reduced costs, and strengthened P&G’s competitive edge. By effectively leveraging external resources and rapidly commercializing new ideas, P&G has been able to innovate its products and business models, meeting diverse consumer demands and maintaining market leadership.

The Benefits of Open Innovation: Competitiveness and Risk Management

1. Enhanced Competitiveness
   By collaborating with external resources, companies can accelerate innovation, adopt diverse ideas and technologies, and respond flexibly to rapid market changes.

2. Risk Management
   Open innovation reduces the risks associated with traditional in-house innovation. Collaborative projects distribute risks among partners, minimizing financial losses even in case of failure.

The Future of Open Innovation

The importance of open innovation will continue to grow, driven by three major trends: digitalization, globalization, and sustainability.

1. Digitalization
   Technologies like AI, big data, and cloud computing offer opportunities for rapid integration of external data and technologies.

2. Globalization
   Competing in diverse international markets requires embracing external ideas and technologies to stay ahead.

3. Sustainability
   To address environmental and societal challenges, companies must develop sustainable products and services, which often require collaboration with external partners.

Conclusion

Open innovation is no longer optional—it is essential. Companies must adopt an open mindset, actively embrace external ideas and technologies, and collaborate to thrive in a competitive landscape. Open innovation not only enhances competitiveness but also provides effective risk management strategies, ensuring long-term success.

Disruptive Innovation Theory

The theory of disruptive innovation was first introduced by Harvard Business School professor Clayton Christensen in his 1997 book, "The Innovator's Dilemma."

I first encountered this theory during a breakfast lecture at the Chosun Hotel, where I was so moved by its ideas that I immediately went to a bookstore and purchased The Innovator's Dilemma.

The core of disruptive innovation lies in its ability to replace leading companies or technologies in existing markets while forming new value networks or innovations. Established companies focus on maintaining their market dominance through sustaining innovations, whereas new entrants, often with lower-quality and lower-cost solutions, disrupt existing businesses and drive significant market changes.

Key Characteristics

• Explains failures of growing companies: Disruptive innovation provides insights into why seemingly successful companies may fail under specific market conditions.
• Sustaining vs. Disruptive Innovation: Sustaining innovation improves performance to meet the demands of high-end markets, while disruptive innovation targets lower performance but achieves success through affordability and unique functionalities.
• Establishment in underserved markets: Disruptive innovations initially take root in low-end or entirely new markets before evolving and challenging mainstream markets.

Features of Disruptive Innovation

1. Low Cost and Simplicity: In the initial stages, disruptive innovation products are of lower quality compared to those from leading companies but are highly cost-competitive and easy to use.
2. Creation of New Consumer Segments: Disruptive innovations target consumers who have been underserved or excluded from existing markets, using low-cost products or alternative performance metrics.
3. Incremental Quality Improvements: Over time, the initially lower quality of products or services improves, reaching or exceeding the quality of existing offerings.

Types

1. Low-End Disruptive Innovation: This form replaces premium products or services with more affordable alternatives. Example: Southwest Airlines' low-cost flight model.
2. New-Market Disruptive Innovation: This innovation creates entirely new markets, targeting non-customers of existing markets where there is no competition. Example: Smartphones creating a new customer base in areas with limited internet access.

Success Stories

1. Netflix: Initially offering DVD rental services via mail, Netflix avoided direct competition with traditional video rental stores. Later, it introduced streaming services, transforming the broadcast and film industries.
2. Tesla: Tesla pioneered the electric vehicle market, initially targeting the high-end market and gradually lowering costs to popularize EVs.
3. Uber: Uber disrupted the traditional taxi industry by providing flexible transportation services using a platform that utilized personal vehicles.

Limitations and Criticisms

1. Limited Applicability: Disruptive innovation theory does not apply to all types of innovations or market changes. High-quality technologies entering the market from the outset are difficult to explain with this framework. Innovations in competitive red-ocean markets are especially challenging.
2. Response from Leading Companies: The theory fails when incumbent firms effectively respond to or absorb disruptive innovations. For example, IBM successfully adapted to disruptive innovations like cloud computing.
3. Difficulty in Accurate Prediction: Predicting the market where disruptive innovation will occur and its impact is challenging due to the complexity and numerous variables in play.
4. Conflict with Existing Industries: Disruptive innovation can negatively impact workers in established industries. For instance, Uber and autonomous vehicles pose threats to traditional taxi drivers and logistics industry workers.

Strategies for Managing Disruptive Innovation

1. Proactive Response Strategies: Incumbent companies must identify early signals of disruptive innovation and develop corresponding strategies. Tailored approaches can be employed for different market segments.
2. Cultural Change: Fostering a flexible and innovative organizational culture enables companies to drive internal innovation.
3. Collaboration and Partnerships: Open innovation, such as collaborations with startups or adopting new technologies, is crucial for absorbing innovation.

Conclusion

Disruptive innovation theory offers a valuable framework for understanding market changes and technological advancements. However, recognizing its limitations and complementing it with tailored strategies is equally critical. While disruptive innovations bring about positive social and economic changes, they also pose significant challenges to existing systems and structures. Understanding and managing these dynamics is essential for leveraging disruptive innovation's full potential.

The Era of Stories

Rolf Jensen, a prominent futurist from Denmark and former director of the Copenhagen Institute for Futures Studies, predicted that after the information age, we would enter the era of emotions and stories. (The Dream Society, 1999) He argued that in this new era, the emotional connection and the stories within products and services would become more important than their functional value or brand. He believed that the key to future business would be experience economy, where providing experiences and emotional engagement to consumers becomes the focal point.

Daniel Pink, known for works like A Whole New Mind (2005) and High Concept, High Touch, makes a similar claim about the future society. “High Concept” refers to the ability to create new ideas and stories based on creativity and imagination, and to communicate vision and meaning in simple, appealing, and engaging ways. Pink argued that the power of original thinking would grow and shift away from conventional frameworks. “High Touch” involves creating human relationships based on empathy and emotion, through interaction and understanding others, and designing experiences that put humans at the center. He foresaw a transformation from the analytical, productivity-driven industrial age to a society that values creativity and emotions. The focus would shift from efficiency and productivity to storytelling and emotional connection.

Though their predictions about the future society made in 1999 and 2005 may not have been entirely accurate, many industries still balance efficiency with imagination, stories, and emotions. However, as AI now excels at tasks that humans were previously better at—like efficiency and productivity—businesses will increasingly rely on more human-centered, emotional, and relational aspects, especially storytelling.

One of the most inspiring and insightful sources of ideas and techniques for storytelling is PIXAR Storytelling by Matthew Luhn, which provides valuable lessons on how to create engaging and emotional stories. Let’s look at some key takeaways from the book.