VUCA & BANI
Almost no day goes by without something unexpected happening. We live in times of huge uncertainty and massive change. The two models VUCA and BANI now describe the challenges that can arise in such a rapidly changing and increasingly complex world.
VUCA stands for:
Volatility: Instability and rapid changes that are difficult to predict. Examples include economic fluctuations or sudden market developments.
Uncertainty: A lack of information or predictability about future events. Companies often have to make decisions without having all the necessary information.
Complexity: The multitude of factors and their interactions that complicate decision-making. Complex systems have many interconnected elements, making analysis difficult.
Ambiguity: Ambiguity and uncertainty in the interpretation of information or situations. This can lead to multiple possible interpretations or answers to a problem.
The BANI model is an evolution of VUCA and stands for:
Brittleness: Systems or organizations that can break under pressure or change. They are not adaptable and often respond poorly to unexpected events.
Anxiety: A state of uncertainty and stress caused by the complex and unpredictable challenges of the modern world. This can lead to paralyzing anxiety that affects decision-making.
Nonlinearity: Relationships between causes and effects are not always predictable. Small changes can have large, unexpected impacts, complicating planning and forecasting.
Incomprehensibility: The world is becoming increasingly complex and difficult to understand, making it challenging to find clear answers or solutions. Information can be ambiguous or contradictory.
Read more at
EXPONENTIAL INNOVATION
The concept of Exponential innovation refers to the rapid and accelerating pace of technological advancements that significantly impact business models, industries, and society as a whole. Unlike linear innovation, which follows a steady, incremental progression, exponential innovation grows at an accelerating rate, often leveraging digital technologies, networks, and new business paradigms.
Key Aspects of Exponential Innovationare:
• Technology-Driven Growth
• Network Effects
• Scalability
• Disruption of Traditional Models
• Agility and Adaptability
• Collaboration and Ecosystems
• Customer-Centricity
Exponential innovation challenges traditional business models and requires organizations to be proactive, flexible, and forward-thinking to leverage new opportunities and stay competitive in a fast-evolving landscape.
Read more and get help at
EFI-REPORT
The Commission of Experts for Research and Innovation (Expertenkommission Forschung und Innovation - EFI) advises the German Federal Government and presents an annual report on research, innovation and technological performance in Germany.
A key task is to provide a comprehensive analysis of the strengths and weaknesses of the German innovation system in an international and temporal comparison. Furthermore, Germany's perspectives as a location for research and innovation are evaluated on the basis of the latest research findings. The EFI presents proposals for national research and innovation policy (source: EFI).
The recent EFI Report is available at
EFFECTUATION MODEL
How had all the successful digital companies in USA been created? Were they all founded by visionary, extraordinary super-entrepreneurs? With a lot of talent and a big portion of luck? Professor Saras Sarasvathy at Darden School of Business, University of Virginia, explored this question. And lo and behold – the answer is: No. Rather, it was mostly a process of many iterative steps. What these entrepreneurs all had in common was the willingness to actively shape the future – while forgoing extensive analyses, forecasts, and lofty goals. They focused on their currently available resources – means, knowledge, skills, networks – and considered what they might achieve with them.
This turned the previously widespread causal logic upside down and defined a new concept of thinking and decision-making – Effectuation (Engl. „to effectuate something“ – influence something with impact).
Five principles coin this approach:
• Bird-in-hand principle (start with what you have)
• Affordable loss principle (risk little means, fail cheap)
• Patchwork quilt principle (form partnerships)
• Lemonade principle (leverage surprise)
• Pilot-in-the-plane principle (take control by relying on things you can directly influence, don’t rely on predictions)
Read more at Society for Effectual Action on
INTRAPRENEURSHIP
Intrapreneurship is derived from the term entrepreneurship, which has been significantly shaped by the work of economist Joseph Schumpeter and can be meaningfully translated as 'business ownership.’
Intrapreneurship combines the concepts of 'intracorporate' (within the company) and 'entrepreneurship' (as mentioned above) – thus referring to entrepreneurship within an established organization. Investopedia defines intrapreneurship as a system allowing an employee to act like an entrepreneur withan an organization (see here). In such a system, employees have the freedom to act entrepreneurially – they are motivated, think critically, act proactively, and take responsibility to develop and implement innovative ideas.
The concept of intrapreneurship was originally introduced in 1978 by Gifford Pinchot III (Bainbridge Graduate Institute, Seattle). However, the term became especially popular through Steve Jobs at Apple (then still Macintosh): in a 1985 interview with Newsweek, he stated, “The Macintosh team was what is commonly known as intrapreneurship.” He was referring to small, company-segregated teams that operated with complete freedom and often disregarded the rules to realize their vision. Today, intrapreneurship is not interpreted as freely, but a bit more freedom and flexibility would certainly be appropriate."
Read more on intrapreneurship here:
DOUGHNUT ECONOMICS
The doughnut economics model is a fresh approach to balancing economic development with social and environmental sustainability. Visualized as a doughnut, the inner circle represents the limits of our social foundation - minimum living standards everyone should have - like access to food, health care, and education. The outer circle defines the ecological limits we must respect to protect our planet, such as climate stability and biodiversity.
The doughnut economics model was created by Oxford University Economist Kate Raworth in 2017 with the aim of creating a realistic regenerative economy.
In this model, the goal for businesses is to operate within the "doughnut“ -meeting people’s needs without overshooting environmental boundaries and overstress societal capacity. This means companies now focus on creating value in ways that benefit society and the planet, rather than just only chasing profits.
By adopting the doughnut economics framework, businesses can identify opportunities for sustainable innovation and enhance their brand reputation. Companies that prioritize sustainable practices can also attract conscious consumers and investors who are increasingly looking for responsible and ethical choices. This concept has the power to encourage businesses to rethink their role in society and embrace a more holistic view of success - one that balances profit with purpose.
Read more at
PASTEUR'S QUADRANT
Pasteur's Quadrant is a conceptual framework developed by Donald E. Stokes and first published in 1997 in his book "Pasteur’s Quadrant: Basic Science and Technological Innovation." It categorizes scientific research based on two dimensions: the pursuit of fundamental understanding (basic research) and the consideration of practical applications (applied research) – resulting in four different quadrants:
• Pure Basic Research
• Pure Applied Research
• Use-inspired Research (Basic Research with Practical Benefits, Pasteur's Quadrant)
• Low Impact Research (Stokes left this quadrant unnamed originally)
These are the three key insights of his framework:
Interconnection: Stokes argues that many of the most impactful scientific endeavors fall into Quadrant III, where curiosity-driven research directly influences practical applications. This highlights the importance of research that serves both fundamental science and societal needs.
Innovation: The model emphasizes that (technological) breakthroughs often occur at the intersection of basic science and applied research, advocating for policies and funding that support this dual approach.
Policy Implications: Understanding where research fits in this framework can help policymakers prioritize funding and support for research that benefits both knowledge and society.
Read more at
UN SUSTAINABLE DEVELOPMENT GOALS
The United Nations Sustainable Development Goals (SDGs) are a set of 17 global goals established in 2015 to address urgent social, economic, and environmental challenges by 2030.
These goals aim to create a more sustainable, equitable, and prosperous world for everyone.
The SDGs build on decades of work by countries and the UN, including the UN Department of Economic and Social Affairs – going back as far as to 1992 when the Earth Summit took place in Rio de Janeiro, Brazil. More than 178 countries adopted the so-called ‘Agenda 21’ then, a comprehensive plan of action to build a global partnership for sustainable development to improve human lives and protect the environment.
You will find the goals and more information at
RESPONSIBLE AI AND HOW IT CAN HELP
Responsible AI refers to the development and use of artificial intelligence (AI) systems that are ethical, fair, transparent, and accountable. The goal is to ensure AI benefits individuals and society while minimizing risks, such as bias, discrimination, and harm. Key principles include fairness (avoiding bias), transparency (making AI decisions understandable), accountability (ensuring responsibility for AI outcomes), privacy (protecting user data), and safety (ensuring reliability and security). In essence, responsible AI aims to create AI technologies that promote positive societal impact and respect human rights at the same time. The most crucial factor in this context, however, is the quality of the data. There's a saying, "Garbage in – garbage out" – if the data used to train the AI is poor, the result will also be poor.
How Responsible AI Can Help
1. Reducing Bias and Discrimination
By designing AI systems that account for fairness and reduce bias, responsible AI can promote equal opportunities and treatment for marginalized groups. This can be particularly important in areas like hiring, lending, and law enforcement, where AI systems might otherwise perpetuate existing societal inequalities.
2. Increasing Trust in AI
Transparent and explainable AI builds public trust by allowing users and stakeholders to understand how decisions are made. While this is important anyway, it is critical in sensible areas such like healthcare, criminal justice, and finance, where people’s lives and livelihoods might directly be affected by AI-based decisions.
3. Improving Decision-Making
Responsible AI ensures that AI systems are used to enhance human decision-making rather than replace it entirely. For instance, AI can assist doctors in diagnosing diseases more accurately but should not replace the expertise of healthcare professionals. Similarly, in the judicial system, AI tools can aid judges but should not make final determinations.
4. Protecting Privacy and Security
AI systems designed with privacy and data protection in mind can safeguard individuals' personal information. In sectors like healthcare, finance, and insurance, this is essential for ensuring users' trust and complying with data protection laws.
5. Ensuring Accountability
Responsible AI provides mechanisms for accountability, ensuring that AI developers, organizations, and stakeholders can be held responsible for the outcomes of AI systems. This is crucial for answering the still intensively discussed question on liability.
Read more on responsible AI at
and
SUSTAINABLE INNOVATION
Sustainable innovation refers to the development of new products, services, or processes that not only drive business growth but also consider environmental and social impacts. It's about finding ways to meet customer needs while minimizing harm to the planet and promoting social responsibility.
In today’s market, consumers are increasingly looking for brands that prioritize sustainability. This means companies can gain a competitive edge by integrating eco-friendly practices into their operations. Sustainable innovation can e.g. involve using renewable materials, reducing waste, or creating energy-efficient products.
Moreover, it’s not just about compliance with regulations; it’s about creating value. Companies that invest in sustainable innovation often see cost savings through improved efficiency and in such can attract a loyal customer base that appreciates their commitment to a better future.
Ultimately, sustainable innovation isn’t just a trend—it’s a strategic approach that positions businesses for long-term success while contributing positively to society and the environment.
VUCA & BANI
Almost no day goes by without something unexpected happening. We live in times of huge uncertainty and massive change. The two models VUCA and BANI now describe the challenges that can arise in such a rapidly changing and increasingly complex world.
VUCA stands for:
Volatility: Instability and rapid changes that are difficult to predict. Examples include economic fluctuations or sudden market developments.
Uncertainty: A lack of information or predictability about future events. Companies often have to make decisions without having all the necessary information.
Complexity: The multitude of factors and their interactions that complicate decision-making. Complex systems have many interconnected elements, making analysis difficult.
Ambiguity: Ambiguity and uncertainty in the interpretation of information or situations. This can lead to multiple possible interpretations or answers to a problem.
The BANI model is an evolution of VUCA and stands for:
Brittleness: Systems or organizations that can break under pressure or change. They are not adaptable and often respond poorly to unexpected events.
Anxiety: A state of uncertainty and stress caused by the complex and unpredictable challenges of the modern world. This can lead to paralyzing anxiety that affects decision-making.
Nonlinearity: Relationships between causes and effects are not always predictable. Small changes can have large, unexpected impacts, complicating planning and forecasting.
Incomprehensibility: The world is becoming increasingly complex and difficult to understand, making it challenging to find clear answers or solutions. Information can be ambiguous or contradictory.
Read more at
EXPONENTIAL INNOVATION
The concept of Exponential innovation refers to the rapid and accelerating pace of technological advancements that significantly impact business models, industries, and society as a whole. Unlike linear innovation, which follows a steady, incremental progression, exponential innovation grows at an accelerating rate, often leveraging digital technologies, networks, and new business paradigms.
Key Aspects of Exponential Innovationare:
• Technology-Driven Growth
• Network Effects
• Scalability
• Disruption of Traditional Models
• Agility and Adaptability
• Collaboration and Ecosystems
• Customer-Centricity
Exponential innovation challenges traditional business models and requires organizations to be proactive, flexible, and forward-thinking to leverage new opportunities and stay competitive in a fast-evolving landscape.
Read more and get help at
EFI-REPORT
The Commission of Experts for Research and Innovation (Expertenkommission Forschung und Innovation - EFI) advises the German Federal Government and presents an annual report on research, innovation and technological performance in Germany.
A key task is to provide a comprehensive analysis of the strengths and weaknesses of the German innovation system in an international and temporal comparison. Furthermore, Germany's perspectives as a location for research and innovation are evaluated on the basis of the latest research findings. The EFI presents proposals for national research and innovation policy (source: EFI).
The recent EFI Report is available at
EFFECTUATION MODEL
How had all the successful digital companies in USA been created? Were they all founded by visionary, extraordinary super-entrepreneurs? With a lot of talent and a big portion of luck? Professor Saras Sarasvathy at Darden School of Business, University of Virginia, explored this question. And lo and behold – the answer is: No. Rather, it was mostly a process of many iterative steps. What these entrepreneurs all had in common was the willingness to actively shape the future – while forgoing extensive analyses, forecasts, and lofty goals. They focused on their currently available resources – means, knowledge, skills, networks – and considered what they might achieve with them.
This turned the previously widespread causal logic upside down and defined a new concept of thinking and decision-making – Effectuation (Engl. „to effectuate something“ – influence something with impact).
Five principles coin this approach:
• Bird-in-hand principle (start with what you have)
• Affordable loss principle (risk little means, fail cheap)
• Patchwork quilt principle (form partnerships)
• Lemonade principle (leverage surprise)
• Pilot-in-the-plane principle (take control by relying on things you can directly influence, don’t rely on predictions)
Read more at Society for Effectual Action on
INTRAPRENEURSHIP
Intrapreneurship is derived from the term entrepreneurship, which has been significantly shaped by the work of economist Joseph Schumpeter and can be meaningfully translated as 'business ownership.’
Intrapreneurship combines the concepts of 'intracorporate' (within the company) and 'entrepreneurship' (as mentioned above) – thus referring to entrepreneurship within an established organization. Investopedia defines intrapreneurship as a system allowing an employee to act like an entrepreneur withan an organization (see here). In such a system, employees have the freedom to act entrepreneurially – they are motivated, think critically, act proactively, and take responsibility to develop and implement innovative ideas.
The concept of intrapreneurship was originally introduced in 1978 by Gifford Pinchot III (Bainbridge Graduate Institute, Seattle). However, the term became especially popular through Steve Jobs at Apple (then still Macintosh): in a 1985 interview with Newsweek, he stated, “The Macintosh team was what is commonly known as intrapreneurship.” He was referring to small, company-segregated teams that operated with complete freedom and often disregarded the rules to realize their vision. Today, intrapreneurship is not interpreted as freely, but a bit more freedom and flexibility would certainly be appropriate."
Read more on intrapreneurship here:
DOUGHNUT ECONOMICS
The doughnut economics model is a fresh approach to balancing economic development with social and environmental sustainability. Visualized as a doughnut, the inner circle represents the limits of our social foundation - minimum living standards everyone should have - like access to food, health care, and education. The outer circle defines the ecological limits we must respect to protect our planet, such as climate stability and biodiversity.
The doughnut economics model was created by Oxford University Economist Kate Raworth in 2017 with the aim of creating a realistic regenerative economy.
In this model, the goal for businesses is to operate within the "doughnut“ -meeting people’s needs without overshooting environmental boundaries and overstress societal capacity. This means companies now focus on creating value in ways that benefit society and the planet, rather than just only chasing profits.
By adopting the doughnut economics framework, businesses can identify opportunities for sustainable innovation and enhance their brand reputation. Companies that prioritize sustainable practices can also attract conscious consumers and investors who are increasingly looking for responsible and ethical choices. This concept has the power to encourage businesses to rethink their role in society and embrace a more holistic view of success - one that balances profit with purpose.
Read more at
PASTEUR'S QUADRANT
Pasteur's Quadrant is a conceptual framework developed by Donald E. Stokes and first published in 1997 in his book "Pasteur’s Quadrant: Basic Science and Technological Innovation." It categorizes scientific research based on two dimensions: the pursuit of fundamental understanding (basic research) and the consideration of practical applications (applied research) – resulting in four different quadrants:
• Pure Basic Research
• Pure Applied Research
• Use-inspired Research (Basic Research with Practical Benefits, Pasteur's Quadrant)
• Low Impact Research (Stokes left this quadrant unnamed originally)
These are the three key insights of his framework:
Interconnection: Stokes argues that many of the most impactful scientific endeavors fall into Quadrant III, where curiosity-driven research directly influences practical applications. This highlights the importance of research that serves both fundamental science and societal needs.
Innovation: The model emphasizes that (technological) breakthroughs often occur at the intersection of basic science and applied research, advocating for policies and funding that support this dual approach.
Policy Implications: Understanding where research fits in this framework can help policymakers prioritize funding and support for research that benefits both knowledge and society.
Read more at
UN SUSTAINABLE DEVELOPMENT GOALS
The United Nations Sustainable Development Goals (SDGs) are a set of 17 global goals established in 2015 to address urgent social, economic, and environmental challenges by 2030.
These goals aim to create a more sustainable, equitable, and prosperous world for everyone.
The SDGs build on decades of work by countries and the UN, including the UN Department of Economic and Social Affairs – going back as far as to 1992 when the Earth Summit took place in Rio de Janeiro, Brazil. More than 178 countries adopted the so-called ‘Agenda 21’ then, a comprehensive plan of action to build a global partnership for sustainable development to improve human lives and protect the environment.
You will find the goals and more information at
RESPONSIBLE AI AND HOW IT CAN HELP
Responsible AI refers to the development and use of artificial intelligence (AI) systems that are ethical, fair, transparent, and accountable. The goal is to ensure AI benefits individuals and society while minimizing risks, such as bias, discrimination, and harm. Key principles include fairness (avoiding bias), transparency (making AI decisions understandable), accountability (ensuring responsibility for AI outcomes), privacy (protecting user data), and safety (ensuring reliability and security). In essence, responsible AI aims to create AI technologies that promote positive societal impact and respect human rights at the same time. The most crucial factor in this context, however, is the quality of the data. There's a saying, "Garbage in – garbage out" – if the data used to train the AI is poor, the result will also be poor.
How Responsible AI Can Help
1. Reducing Bias and Discrimination
By designing AI systems that account for fairness and reduce bias, responsible AI can promote equal opportunities and treatment for marginalized groups. This can be particularly important in areas like hiring, lending, and law enforcement, where AI systems might otherwise perpetuate existing societal inequalities.
2. Increasing Trust in AI
Transparent and explainable AI builds public trust by allowing users and stakeholders to understand how decisions are made. While this is important anyway, it is critical in sensible areas such like healthcare, criminal justice, and finance, where people’s lives and livelihoods might directly be affected by AI-based decisions.
3. Improving Decision-Making
Responsible AI ensures that AI systems are used to enhance human decision-making rather than replace it entirely. For instance, AI can assist doctors in diagnosing diseases more accurately but should not replace the expertise of healthcare professionals. Similarly, in the judicial system, AI tools can aid judges but should not make final determinations.
4. Protecting Privacy and Security
AI systems designed with privacy and data protection in mind can safeguard individuals' personal information. In sectors like healthcare, finance, and insurance, this is essential for ensuring users' trust and complying with data protection laws.
5. Ensuring Accountability
Responsible AI provides mechanisms for accountability, ensuring that AI developers, organizations, and stakeholders can be held responsible for the outcomes of AI systems. This is crucial for answering the still intensively discussed question on liability.
Read more on responsible AI at
and
SUSTAINABLE INNOVATION
Sustainable innovation refers to the development of new products, services, or processes that not only drive business growth but also consider environmental and social impacts. It's about finding ways to meet customer needs while minimizing harm to the planet and promoting social responsibility.
In today’s market, consumers are increasingly looking for brands that prioritize sustainability. This means companies can gain a competitive edge by integrating eco-friendly practices into their operations. Sustainable innovation can e.g. involve using renewable materials, reducing waste, or creating energy-efficient products.
Moreover, it’s not just about compliance with regulations; it’s about creating value. Companies that invest in sustainable innovation often see cost savings through improved efficiency and in such can attract a loyal customer base that appreciates their commitment to a better future.
Ultimately, sustainable innovation isn’t just a trend—it’s a strategic approach that positions businesses for long-term success while contributing positively to society and the environment.
Martin Mahn
Waldring 8, 21376 Luhmühlen
© 2025 Martin Mahn
