The story of productivity in industrialized countries started as a story of huge successes and then turned into one of equally huge disappointments. Up until the 2000s work productivity still grew with an annual mean of 2,5%. Since then it began to stall around 1,1%. Why?

Peter Drucker mused that today’s corporations would only need half as many hierarchical layers and only a third of the managers that firms in the 1980s had. We couldn’t be further away from fulfilling this. Bureaucracy is virulent especially in large firms that dominate today’s economy: More than a third of US employees work in firms with more than 5000 employees, where its frontline employees are buried under an average of 8 management layers. Numbers in Europe are even more dramatic: In some of the large firms I work with, the ratio between white collar and blue collar is strongly tilted towards the first group, even industrial companies. Some German automotive companies have more white than blue collar workers. Especially when considering the technological advances since the 2000s, productivity growth has been very meagre. Technological advances have not compensated for managerial shortcomings.

Will AI lead us out of the productivity debacle?

Hopes are tied to the advance of artificial intelligence. AI will change the workplace, there is no doubt. But how will AI make our workplace more productive?

The future of AI is often compared to the innovation path that the internet took, or even further back, the railway. Rarely, however, AI is compared with the invention of the telegraph. In an article in the Economist, mathematician and historian Andrew Odlyzko^[1] argues that the latter comparison is much more insightful and the comparisons with the internet and the railway are quite misguided.

Railway companies as well as telecoms pioneers knew from the start where productivity growth is coming from, and where the money will be made: In the first case old and cumbersome modes of transport were replaced, in the other case it was old and cumbersome forms of communication. More efficient mobility through trains or more efficient communication through e-mails undoubtedly led to more productivity.

Productivity in the 21^st century means solving the most urgent problems

Studying the history of the telegraph is more insightful because it mirrors how large tech companies approach AI today. In the beginning telegraphs were overwhelmingly used by rail companies to help make trains run more smoothly. Railway tycoons monopolized that technology and built telegraph networks alongside their rails so that communication of track disruption could be made quickly. Their massive investments in the telegraph led to price wars, as they tried to use the technology to protect their business models. Equally today tech giants are using AI as co-pilots in existing cloud-offerings, argues Odlyzko.

With today’s AI applications nobody knows yet where significant and lasting productivity boosts across sectors and specific use cases will come from. Nobody knows its killer app. Nobody knows where huge sums of money to justify its current hype could be generated. This is why we need an open and purposeful culture of experimentation in the workplace.

The real boom of the telegraph came much later, when more ambitious, dynamic and independent companies built business models around the new technology alone and created a much more holistic and productive market for using telegraphs beyond the railway network.

Coming back to the present, this analogy can help us to ask better questions for our organisation:

• If AI is the answer, what is the question?
• Which productivity problem is AI supposed to solve (formulating an email in the style of Shakespeare or creating images of unicorns and rainbows is not an urgent issue)?
• What is keeping your organisation from becoming more productive? Are we really looking at technological problems, or rather managerial problems?
• What routines would employees wish to see automated to really be able to get more done?

These questions might help to bring the hype around AI from promise to profit.

Intelligence that is artificial vs. intelligence that is human

Working more productively with AI might start with overcoming what we call it in the first place: artificial intelligence. There is nothing artificial about it. It’s all human-made, which we can already see by the ways biases are baked into the human-made data that LLMs use. It is not intelligent, nor does it aim to be intelligent in the way humans can be intelligent, i.e. empathetic, moral, purpose-driven, problem-solving. In that sense, GenAI might offer intelligent sounding answers but it never really understood the question. Researchers in the field therefore call for using “mechanical utility”, or “stochastic communication” as more helpful terms. Because that would help us to find renewed appreciation for another form of intelligence, too. Call it HI, Human Intelligence.

About the author:
The economist and philosopher Dr. Hans Rusinek teaches at the University of St. Gallen on the future of work, works as an independent management consultant and is a Fellow of the Club of Rome Germany. Prior to that, he worked at the Boston Consulting Group.

^[1] https://www.economist.com/business/2024/08/06/a-history-lovers-guide-to-the-market-panic-over-ai

The Renaissance as antiquity reborn

The Renaissance, marking a rebirth of antiquity after a “dark” Middle Ages and before our current modern era, is considered a golden age in culture, economics, and science. The Renaissance shows how flourishing and rebirth of old strengths are possible even after long periods of decline.

Germany, as a country of the economic miracle and as a leading industrial nation, like Europe as a whole, could use a rebirth of former strength. However, this shouldn’t be through simply returning to past success models, but through a new paradigm of success. We see digitalization, especially through AI, as a unique opportunity.

We therefore speak of both a “digital renaissance” in the case of Germany and Europe, and a “company renaissance”, in which these rebirths encompass not only technological dimensions but also economic, cultural, and societal changes.

As Nobel economics laureate Daron Acemoglu makes clear in his works “Why Nations Fail” and especially “Power and Progress”, we stand at a crossroads in terms of our future viability. Our success and the quality of the future do not depend only on technology, and they are not predetermined. Scenarios varying between dystopia and utopia are possible. If we use technology as derived from a digital renaissance scenario, there is an opportunity for a new human orientation and renewed future viability of our often leading companies, new enterprises, and even at the economic and societal level. The key factor is the right digital paradigm.

The organization of the future

The organization of the future is less a well-organized machine with human resources, but rather resembles a living organism: highly adaptable, decentrally organized, and continuously learning. Rigid hierarchies and silos are replaced by flexible network structures that dynamically adapt to market needs. This “Adaptive Organization” is characterized by three core features:

1. Intelligent decentralization through company rebuilding. Classical pyramid structures are replaced by networks of autonomous, customer-centric units. These “cells” have or can access all necessary competencies comprehensively within the network, while AI handles coordination and reduces communication complexity.
2. Human-AI symbiosis iInstead of automation. AI is primarily used not as an automation tool to substitute human intelligence but as a cognitive partner in hybrid human-machine teams. These co-creative “Augmented Intelligence Teams” combine human creativity and judgment with AI’s analytical power, resulting in a level of problem-solving and innovation beyond the capacity of mere generative AI.
3. Leadership instead of management. Leaders of the future are enablers and coaches able to empower teams rather than control them, creating frameworks for self-organization and promoting entrepreneurial thinking at all levels of the organization.

The path to digital renaissance

The transformation to an adaptive enterprise combines technological innovation with cultural change in four interlinked phases:

1. Digital foundations
   • Building scalable digital and data infrastructure
   • Implementing modern collaboration platforms
   • Establishing data governance and AI ethics frameworks
   • Building data lakes and analytics capabilities
   • Initial AI pilot projects in selected areas
2. Organizational realignment
   • Analyzing and redesigning value chains
   • Identifying and forming autonomous business units
   • Developing AI-supported coordination mechanisms
   • Establishing agile working models and DevOps practices
3. Cultural transformation
   • Developing new leadership guidelines for the AI age
   • Building hybrid competency profiles (Human+AI Skills)
   • Establishing a culture of continuous learning
   • Promoting innovation and experimentation
   • New incentive systems for entrepreneurial action
4. Scaling and evolution
   • Complete transformation to cellular structures
   • Integration of AI into all core processes
   • Establishing an ecosystem of internal and external partners
   • Continuous evolution of the organization
   • Building new business models.

In this enterprise, critical success factors include: clear vision and leadership commitment; a balance between transformation and stability (cf Peter Drucker’s “continuity and change”); a focus on employee development and empowerment; an iterative approach with rapid learning experiences; and open communication combined with active change management

The time is now

The technological possibilities for this renaissance exist today. AI systems have reached a maturity level that makes the vision of the adaptive enterprise a reality. The crucial question is no longer whether, but how quickly companies will tackle this transformation.

Digital renaissance offers companies a historic opportunity to reinvent themselves in a form that is more human, intelligent, and successful than ever before. But like the original Renaissance, this transformation requires courage, vision, and the will to shape the future.

As Drucker famously put it: “The best way to predict the future is to create it.”

This article was created in a co-creative process between Lucke Co-CEO Sven Henke, Dr. Winfried Felser, and in dialogue with various generative AIs.

About the authors:

Marc Wagner is a proven expert on digital transformation, employee experience & new work and was voted Top HR Influencer (Personalmagazin) and among the Top 25 New Workers (Workpath) in 2018 / 2020 / 2022 / 2024. As Senior Vice President People & Organization, he has helped to position Atruvia AG as one of the most attractive IT employers in Germany and also to establish a holistic employee experience approach. In the future, he will scale this know-how as Senior Executive Advisor in the cooperative network and, as Co-CEO, position Lucke GmbH as a digitalization and technology consultancy to bring the #CompanyRenaissance to life.

Sven Henke serves as a digital transformation leader at Lucke EDV, where he combines strategic vision with human-centric innovation. His leadership approach is shaped by his analytical mindset and commitment to lifelong learning, driving technological advancement that empowers people and organizations. With a focus on trust and sustainable progress, he leads initiatives that bridge technology with human potential, fostering environments where digital transformation catalyzes both business excellence and individual growth. 

Dr Winfried Felser is an entrepreneur, future designer and author. As a management consultant and co-founder and deputy head of a Fraunhofer application centre, he has worked on transformation through the network economy and digitalisation. Today, he is the initiator and managing director of NetSkill Solutions GmbH.

Cultural anthropology is one of the least recognised – and most derided – academic disciplines today. No wonder: if non-academics know what the word means, they tend to view it as the academic version of Indiana Jones, namely a department where intrepid academics travel to weird places, to understand what it means to be human, by studying “exotic’ cultures. It does not seem to have much value in the modern business world, markets or C-suite.

But after a career spent in financial journalism, after a PhD in anthropology, I passionately believe that this vision of anthropology is quite wrong; it is one of the best tools that exist to help you make sense of the modern world, particularly at a time of accelerating tech innovation and profound social and political change. 

One reason is that 21st century anthropologists now study Western societies as much as non-Western ones, in companies, government Institutions, non-profit groups – and everything in between. Microsoft, to name one example, is a big employer of anthropologists, who are studying the interface of tech and culture.   

The other issue is that the core methodological journey that anthropologists use can be copied by anyone. This essentially has three core components. The first is a desire to “make the strange familiar”, to use the anthropological tag – meaning that anthropologists deliberately like to immerse themselves in the lives and minds of people who seem alien to them, in order to appreciate why these different mindsets exist and how they can be valuable. This teaches humility and promotes better understanding of others, which is essential in a globalized world. 

However, the second stage of this journey involves going beyond just trying to understand the “other” – or someone who seems alien – and entails using that lens to look back at yourself, too. Anthropologists call this second step “making the familiar strange”, or stepping back from what seems familiar to see it with fresh eyes, as if you were an alien. This is crucial since a “fish cannot see water”, as the Chinese proverb says, meaning that nobody can understand the world they live in if they only ever swim in those waters; getting an insider-outsider perspective enables us to look at ourselves objectively.

That leads to.a third step: using an insider-outsider vision to look at the half-hidden assumptions that we normally ignore because they are so familiar. Anthropologists sometimes call this listening to “social silence`’ – or studying the issues we never talk about, not just noise. This is crucial for making sense of how any company or office or other enterprise works. Indeed, I would argue that failing to hear social silence is one of the biggest mistakes that a manager can make, since what we don’t talk about is often far more important than what we do.

To understand this, consider by way of one example what happened when some anthropologists worked at General Motors two decades ago. At that time, the company was trying to create a jointly designed car, after GM merged with the German Opel group, but the efforts kept going badly wrong. Initially the GM managers blamed this disaster on linguistic gaps, or engineering rows. But when the engineers engaged in classic “ethnography” in the company – or the process of quietly observing what people actually do, rather than just what they say – they realised the issue was more subtle and complex; different teams had different attitudes about what a “meeting” was supposed to do. 

Most notably, some engineers turned up expecting meetings to be a place where decisions were made and/or imposed in a top-down manner. Others thought that meetings were a way of airing disagreements in a democratic manner. And still others viewed it as a rubber-stamping exercise with the decisions presumed to have been taken beforehand.

There is nothing inherently wrong with any of these assumptions; each can work in a different context. But in this case the engineers kept calling meetings to defuse the fight, without realising that since everyone assumed they knew what a meeting meant, they never realised they had widely differing ideas. The “noise” about the engineering differences was visible; the “silence” was the fact that nobody ever discussed what they expected from meetings in the first place. And that is just a tiny, somewhat trivial example of a much bigger problem. 

So what can managers learn? One obvious point is to recognise that your way of looking at the world is just one approach, amid a multitude of perspectives, and we can all learn from each other. A second is to learn to listen to social silence, by focusing on what people do not talk about, as well as what they do. And the third big lesson is to acknowledge that problems in companies rarely arise because of any dastardly plot, deep conspiracy or malfeasance, but are down to that social silence. What is hidden in plain sight matters enormously – and somehow we have to start looking at what we don’t see, and listening to silence.

About the author:

Gillian Tett is Provost of King’s College, Cambridge, and columnist and chairman of the editorial board at The Financial Times

More and more of the innovation opportunities and challenges in management today, from sustainability through to leveraging the potential of AI, require a range of capabilities and knowledge that no company has in-house today. As Frank Walter Steinmeier, President of Germany put it during the Covid pandemic: “No single entity covers the medical, economic, and political elements required to produce a vaccine for all.” Likewise, no single company has all the knowledge in-house to make buildings sustainable, enable the whole spectrum of industry to economically shift to renewable energy, move from vehicles to mobility solutions, or to integrate AI effectively into the lifeblood of organisations, to name just a few of today’s opportunities for both profit growth and societal benefit.

Ecosystems of partners

The key to managing for new levels of value creation and innovation, therefore, will be for organisation to build and lead vibrant and diverse ecosystem of partners. Innovative responses will require access to the capabilities and knowledge of an ecosystem of partners, drawing on know-how and capacity in a wide variety of related industries.

I was struck that as far back as the 1960s Drucker highlighted the importance of knowledge. Today diverse know-how and capabilities need to be brought together in shared value creation. Working together the aim is to jointly discover innovative new solutions and then implement them. To do this, leaders will find that while they can no longer reply on command and control, but they can nudge, incentivise, and lead ecosystem partners to help them achieve their joint goals. It also means working more closely with customers. That doesn’t necessarily mean today’s largest customers. They must be customers with a need that existing market offerings can’t satisfy. They must be willing to invest time and resources to co-develop innovative solutions.

 A new dominant logic

These shifts are part of the critical shift from a dominant logic that views success as depending on individual firms acting alone to innovation and value creation as driven by collaborative ecosystems working toward shared aspirations and collective success. Creating the conditions for the rights flows of knowledge, not through the bottleneck of a single company, but across a network of partners who interact with each other will be a key task in what Richard Straub of the Drucker Global Forum has called “The Next Knowledge Work”.

Who should be ecosystem partners

At our panel “Value Creation in Knowledge Ecosystems” during the Forum we concluded that this networked ecosystem needs to involve institutions, non-profit organisations and individual activists and entrepreneurs, not just the “usual suspects” of established suppliers, customers, and familiar alliance partners. It must also be a global network. The new knowledge economy won’t be effective unless it has global reach. Despite the growing political rhetoric about rolling back globalisation, mobilising the diversity of knowledge from around the world will be essential for step-change innovation.

About the author:

Peter Williamson is Professor of International Management at the University of Cambridge, Judge Business School and Fellow of Jesus College. He is also Chairman of the digital process automation cloud services company Bizagi Group Inc.

Allegiance to Artificial Intelligence lies in the code we have crafted. This is both its strength and its peril.

AI as we know it is mimicking a form of intelligence, but it is hollow—it lacks a moral core. Indeed, for decades, we’ve conceived and trained machines to calculate outcomes, not to uphold principles. Leadership in the next era demands that we fix this.

Integrity is the foundation of any functioning system.

This is why the next era of leadership must embrace what I term Artificial Integrity—the fusion of intelligence with principled reasoning.

If history teaches us anything, it is that leadership falters without moral clarity.

It is no longer enough to create systems that compute value; we must create systems that comprehend values.

Intelligence unmoored from values is no better than a ship without a keel, charting courses without regard to consequence.

Integrity, not Intelligence, is the missing piece. Intelligence without integrity is a story without a moral.

Integrity is the factor that ensures decisions—whether made by humans or algorithms or both—are not merely logical but ethical, moral, socially acceptable and accountable. 

This must become the north star of every AI algorithm, a fundamental requirement that ensures AI technologies don’t just serve us but serve uswell. That means a simple thing: Integrity isn’t a feature; it’s the architecture foundation of any functioning system, whether it is about, society system, organization system, human system, AI system.

Machines must act with human values

Now that machines accompany humans not merely as tools but as cognitive agents, capable of decision-making, adaptation, and action—now that we’re entering a time when machines don’t just assist us but influence us, learn from us, and evolve alongside us—it is not enough to ask whether such a system works. We must ask whether it works justly.

The question is no longer can we build intelligent machines? We know the answer to that. The question now is can we ensure they are machines of integrity?

The next era demands more than just intelligent machines; it requires machines capable of integrity-led reasoning. It involves embedding ethical, moral, and social reasoning into AI systems.

Leadership must champion this redefinition.

Today’s leaders for tomorrow are the ones who see not just what AI can do, but what AI should do.

Artificial Integrity over Intelligence is a leadership challenge

The question is how we can ensure AI exhibitsIntegrity—a built-in capacity to function with integrity, aligned with human values, and guided by principles that prioritise fairness, safety, and social health, ensuring that its outputs and outcomes are integrity-led first, and intelligent second. 

With the interdisciplinary dimensions it implies, such a question is not just a technological one.

It demands relentless focus, unyielding commitment, and above all, a willingness to challenge the status quo.

This is not about control; this is about stewardship. This is about ensuring that the systems we create uplift humanity rather than diminish it.

This is about Artificial Integrity over Artificial Intelligence as no amount of the latter will ever replace the need for the former.

This is the principle that transforms AI leadership from an exercise of power into an act of service. 

This is the ultimate test for leadership in the next era.

Artificial Integrity oversight to guide AI is anything but artificial

Imagine a future where governments use AI to craft policies that balance economic growth with environmental sustainability. These systems don’t just analyze data; they assess the long-term ethical, moral and social implications of policy decisions.

Envision AI-driven educational platforms that not only adapt to each student’s learning style but actively address systemic biases. These systems advocate for equal opportunities, providing underprivileged students with tailored resources to bridge educational gaps.

Picture a logistics AI that doesn’t just optimize routes for efficiency but also prioritizes carbon-neutral delivery methods, recommending shifts to renewable energy usage and fostering local supply chains to reduce global carbon footprints.   

Think of an AI-powered investment advisor that doesn’t solely focus on maximizing profits but evaluates the societal and environmental impact of investments, steering clients toward sustainable and equitable economic growth.

Imagine urban planning AIs that don’t just optimize infrastructure for population density but advocate for resilience against natural disasters, ensuring equitable access to resources during crises and rebuilding efforts. The question isn’t just how we lead this to happen, but what leads us forward.

Artificial Integrity is the new AI frontier.

Although the journey to Artificial Integrity might seem ambitious, the seeds of this transformation are already being planted.

Anthropic’s approach to aligning AI systems with ethical principles is encapsulated in its concept of Constitutional AI. By embedding these principles directly into its AI development process, Anthropic is advancing a vision of AI that not only serves human needs but does so in a manner that consistently aligned with values.

Ilya Sutskever, a co-founder and former chief scientist of OpenAI, has launched a new venture named Safe Superintelligence Inc. (SSI). In September 2024, the company secured $1 billion in funding from prominent investors. SSI is dedicated to developing superintelligent AI systems with a primary focus on safety and ethical alignment.

More recently, the somewhat controversial OpenAI, in collaboration with Duke University, is advancing the integration of morality into AI systems through a three-year, $1 million project titled Research AI Morality, to develop algorithms that predict human moral judgments, making them integrity-aware and aligned with human values.

This reflects the necessary change in leadership to address the ethical, moral and social intelligence implications and challenges of AI technologies, ensuring that AI systems operate not only with Intelligence but with Integrity. 

Artificial integrity upholds the only code that matters, Integrity—not Intelligence

This code calls for more to done in computational coding and much more leadership to embrace this change, as the shift toward Artificial Integrity over Intelligence will arise only through the power of distribution, not through that of concentration.

True leadership readiness for the next era must ensure that machines don’t just work for us—but work with us, while being aligned with our highest ideals.

To lead this journey, we need visionaries, creators, and leaders who understand that the greatest achievements are those built on values and a shared sense of purpose. This is not merely about building smarter machines; it is about building a better world.

There is so much work, so many challenges to overcome, to get there; but it’s clear—Integrity, not Intelligence, is the new black.

About the author:

Hamilton Mann is Group Vice President of Digital Transformation at Thales, lecturer at INSEAD and HEC Paris, and the originator of the concept of Artificial Integrity. He is a globally recognized expert in Digital and AI for Good and was inducted into the Thinkers50 Radar as one of the Top 30 most prominent rising business thinkers. Mann is the author of Artificial Integrity (Wiley 2024).

On Wednesday November 13, 2024, “The India Way” workshop took place in Vienna as part of the Drucker Forum and in collaboration with the Living Machine Institute and Invest India. 

Echoing Peter Drucker’s vision of the “Next Society”, Richard Straub, founder of the Global Peter Drucker Forum, has launched an ambitious initiative called “The Next Management”, aimed at reframing management for the 21st century. 

The so-called India way offers a tangible and intriguing example of a Next Management pathway. Ratan Tata, former chairman of Tata Trusts, said: ” I believe it’s important to have companies survive over the long term. I hate to see major corporations disappearing from the scene because someone has cashed out, because managers have been unable to escape their comfort zone, or because boards have not been sufficiently nimble to change with the times. When these things happen, decades of effort and innovation go to waste for the company and the society.” 

The India Way is grounded in a set of core principles, drawn from deep scientific and philosophical insights, to reimagine corporate success at a time of increasing global instability, multiplying corporate failures and stagnating value creation from the largest corporations. 

In small groups, participants from all over the world had an opportunity to discuss, debate and present their thoughts on the six core principles under the leadership of Adrian Wooldridge, global business columnist at Bloomberg, Living Machine Institute founder Unni Krishna, and Ramabadran Gopalakrishnan, retired executive and award-winning author, among other things. 

The principles are:

1 – Growth that serves longevity: Longevity and compounding economic returns are superior markers of success, to which growth and profitability are duty bound. By prioritizing longevity, organizations ensure that they continue to serve society meaningfully and remain resilient in the face of change.

2 – Understanding the life-force of organizations beyond intangibles: the central concept of life-force represents the creative energy that drives all living systems, enabling organizations to adapt and evolve in response to change. When life-force is nurtured, organizations flourish. To do so, they need to understand that mindsets, habits and decisions are part of the larger tapestry of the living. To succeed, leaders must recognize and cultivate this holistic understanding.

3 – Perpetual value is earned through the living: lasting value cannot be generated through linear financial models and short-term strategies. Modern finance and accounting measures lead companies to privilege expansion and extraction by direct force rather than success earned by cultivating the living elements within the organization. This means that in a regenerative context, perpetual value arises from the ongoing health and vitality of the organization’s living elements, sustaining its success over decades. 

4 – Building an institution as an infinite game: True value is only created through a sincere commitment to the long haul. Companies perform differently, depending on their perspective of business as either a finite or open-ended game. Management has the choice of either complying with market-based short-term incentives or maximizing value creation for the long term. 

5 – Mastering the paradox: organizations cannot thrive over the long run if they solely focus on financial performance or market share. Instead, they should additionally consider how human capabilities have a significant influence on how value is created. The golden path integrates both, and the ability to navigate the space between opposites is essential for unlocking potential. 

6 – Learning from failure: Why are companies that are built to last, failing at a spectacular rate? Failing companies inadvertently start to overly focus on quarterly earnings and market valuation as the ultimate metrics for success. Executive incentives and compensation closely tied to these metrics only intensify the short-term focus, eroding the human and living components of the organization’s potential. In addition, such compensation schemes often significantly widen the gap between executive pay and the pay of average employees, eroding trust, motivation and loyalty.

In summary: The India way is a movement that recasts the foundational assumptions of management theory and practice by blending wisdom with contemporary business models. It emphasizes the role of compassion, self-awareness and interconnectedness, challenging the well-established western “extraction machine” model. It embodies wholeness, integrating the living and machine elements for long-term, self-renewing success and collective prosperity. The India Way can be thought of as an ongoing contribution to the Global Drucker Forum movement’s initiative to generate the Next Management, in the spirit of one of Peter Drucker’s most frequently quoted sayings: “The best way to predict the future is to create it”. 

About the author:

Business leader and public speaker Guillaume Alvarez was for 13 years Senior VP for Europe, Middle East & Africa at Steelcase. He is now Director of Corporate Development for the Global Peter Drucker Forum.

As a guest at the 16th Global Peter Drucker Forum, I had the honor of talking about recent developments in the global political landscape and their effects on society and the economy. The era of globalization as we have known it has ended and, unfortunately, we are not only being confronted with a new geopolitical situation, we must also simultaneously face the challenges of climate change and increasing biodiversity loss. In other words, globalization is over; global problems, however, will become ever more pressing. Against this background it is clear that we will only be able to feed and provide health care for a growing world population if we find new, uncommon ways to collaborate.

With our mission “Health for All, Hunger for None,” Bayer has set itself the clear goal of focusing all its efforts on the development of new and innovative products for health and nutrition. What’s more, we are also transforming ourselves into an enterprise where people are able to collaborate with a keen sense of ownership.

I will discuss the great opportunities that arise at the intersection of AI, biology and chemistry later. But before I do so, let me make clear that we can only leverage the full potential of the biorevolution if we reimagine how our operating systems work. One of the biggest enemies of adaptability and innovation is bureaucracy. Bureaucracy was largely an enabling factor for companies in the first Industrial Revolutions, but in the transformation to come, it paralyzes organizations at every turn. To exploit the potential of innovation, we need to work on new organizational models that can handle unprecedented complexity, foster cross-disciplinary collaboration and accelerate the translation of discoveries into solutions. Traditional command-and-control management approaches no longer offer the right answers to this.

Our approach at Bayer is built around dynamic shared ownership, where small teams work independently on mission-focused tasks. With 95% of decision-making placed at team level, the model emphasizes autonomy, empowering teams to act quickly and decisively. We are creating a completely new system of collaboration with as little hierarchy and bureaucracy as possible. Terms like “span of control” are being replaced with “span of coaching,” signaling a manager’s role as a mentor and enabler rather than a supervisor and controller. This reimagined managerial function fosters a culture of empowerment and self-governance, reducing hierarchical constraints and facilitating a mission-driven atmosphere across the company. In this way, we are establishing the conditions needed to reduce bureaucracy and create more space for precisely those innovations that seemed unthinkable just a few years ago. Let me now explain why this is so important.

This year’s Nobel Laureates were announced a few weeks ago, with John Hopfield and Geoffrey Hinton recognized for their foundational work in machine learning, and David Baker, Demis Hassabis, and John Jumper awarded the Nobel Prize in Chemistry for computational protein design. So in effect AI won the Nobel Prize in chemistry. But this significant achievement was almost overlooked given all the AI related hype. After all, pundits might have predicted OpenAI to be awarded the Nobel Prize for literature, but chemistry?

Currently, work in laboratories around the world has been significantly more transformed by the confluence of AI, biology and chemistry (ABC), than anything we see in the office environments we all experience. The AI discussion should look beyond the dramatic changes in workplaces for knowledge workers and pay more attention to life sciences. The McKinsey Global Institute deserves credit for coining the term “biorevolution” in its May 2020 paper opening our minds to what will be possible [mgi_the bio revolution_executive summary_may 2020.pdf] . This process is transforming medicine, agriculture, and material sciences. Here are 10 examples: 

1. Protein Folding: Advances in understanding and predicting protein structures, such as those achieved by DeepMind’s AlphaFold, are revolutionizing biology by enabling scientists to predict the 3D structure of proteins from their amino acid sequences.
2. Gene Sequencing: Gene sequencing, and new ways of doing it, even in living cells, have dramatically decreased in cost, making it more accessible and opening up new possibilities for research and personalized medicine.  
3. Gene Editing: Technologies like CRISPR-Cas9 and base editing have revolutionized gene editing, allowing precise modifications to DNA and enabling new treatments for genetic disorders.
4. RNA Technology: For example, the development of mRNA vaccines, particularly for COVID-19, has demonstrated the potential of RNA-based technologies in rapidly developing effective vaccines.
5. Synthetic Biology: Innovations in synthetic biology are enabling the design and construction of new biological parts, devices, and systems, which can be used in medicine, agriculture, and industry.
6. Biocomputing: Advances in biocomputing are integrating biological data with computational tools, enhancing our ability to analyze and interpret complex biological systems.
7. Cell Engineering: Techniques in cell engineering allow scientists to modify cells for therapeutic purposes, such as CAR-T cell therapy for cancer treatment.
8. “Omics” Sciences: The integration of genomics, proteomics, metabolomics, epigenomics and other “omics” sciences provides a comprehensive understanding of biological systems and their interactions.
9. Biological Engineering: Innovations in biological engineering are enabling the development of new materials, biofuels and bioproducts, contributing to sustainability and environmental protection.
10. Microbiome Research: Advances in microbiome research are uncovering the crucial roles that microbial communities play in human health, agriculture, and the environment.

Growing up, we became proficient in programming computers. Today, sophisticated capabilities are programming minds beyond the influence of human storytelling or any mass media before. The future will be shaped by programming cells at breakneck speed. In 2020, when COVID-19 developed into a pandemic, none of the experts predicted we would be able to develop vaccines in a matter of months. However, the understanding of the ribosome and mRNA, the new possibilities made possible by gene editing, progress in chemistry in synthesizing nano lipids, and the coinciding victory of AlphaFold in the CASP 14 protein folding competition, allowed new vaccines around the world to loosen the grip of the pandemic on all our lives. 

Today, inserting cells into the brains of Parkinson’s patients can stop and reverse the disease’s progression, with promising clinical trials ongoing. This may allow us to really treat Parkinson’s disease for the first time since its discovery over 200 years ago. Genetic disorders causing diseases like Sickle Cell or Huntington’s might be successfully treated by editing genes. Treatments reversing blindness are also in the cards. Today, daily nutrition of more than 4 billion humans relies on fertilizers synthesized through the Haber-Bosch-Process. Modifying plant genomes allows crops like maize, wheat, or rice to form symbiotic relationships with microorganisms, reducing fertilizer dependency and lowering global greenhouse gas emissions by up to 3%. New crops allowing for unparalleled crop rotations are in the works which would improve soil health, agricultural productivity and deliver plant-based feedstocks for the chemical industry and biofuels that no longer compete with food. They are essential for climate-smart regenerative agriculture.

While there is reason to be excited about the biorevolution, we also need to ensure that the new possibilities of synthetic biology are regulated in ways that prevent harm, avoid weaponization, secure intellectual property as well as access benefit sharing, and avoid political block confrontations similar to what companies like Huawei or ASML are already facing in the telecommunications- and microchip-production sectors. As far as patient populations are concerned, we need to innovate how healthcare systems reward outcomes such as reversing diseases, instead of paying upfront therapeutic inputs. This will help to make them accessible. Most notably, the uneven distribution of vaccines has taught us to ensure that all regions, especially the baby-boomer generation of our time currently growing up in Africa, have access to the new tools undergirding the age of biology.

We are entering a world of ever-accelerating biorevolution. In order to take advantage of its opportunities, we must do our homework at all levels. Bayer has long since started doing this.

About the author:

Matthias Berninger heads Public Affairs, Sustainability & Safety for the Bayer Group. In his role, he is responsible for the company’s global public affairs activities and has developed Bayer’s global sustainability strategy, anchoring it into the company’s business strategy.

Thinking across different time horizons is a crucial skill for driving impact and sustainable value creation. We can choose our own perception of time to exercise our long-term thinking muscles, to bring our future vision into focus, and to spot opportunities.

Our expanding liminal present

Today, few can focus beyond the next news cycle. But looking farther into the future is necessary for survival. As the world will be radically transformed over the coming years, there is no alternative but to understand what key features to look out for, what fragments of the future are emerging today – sometimes prematurely and unannounced. Thinking across different time horizons provides an opportunity to explore these possibilities.

According to Zen Buddhism, neither the past nor the future exist – there is only nowness. But even so, truly experiencing the present is compatible with the future. Embracing nowness allows one to emerge with more clarity for the futures. 

However, technology today is blurring the lines of the past, present, and future. With AI-negotiated legal contracts, bioprinted organs, and flying cars, is the present spilling prematurely into the future? Or is the future encroaching on the present with dystopian pandemics, climate-driven wildfires, and bad actors hacking military infrastructure?

In our UNknown, Volatile, Intersecting, Complex, and Exponential (UN-VICE) world, the lines between the present and future are becoming blurred. The liminal states between time periods are themselves growing, as present and future realities intersect. The choices we make today about how we will engage with the future intimately affect the present.

Why we miss inflection points

The future does not exist today, so we have the opportunity to imagine it, shape it, and navigate towards it. However, our current maps of the future are limited, so we need to develop early warning systems to identify inflection points before they arrive. 

We miss inflection points for two contradictory reasons. We call this the “Inflection Paradox”:

1. Amara’s Law: In the early stages, one may be tempted to dismiss overhyped emerging technology. Then, after the prolonged wait, we underestimate its long-term impacts.
2. The shape of exponential change: Despite the noise, early developments are barely perceptible. Even explosive growth only becomes apparent after some time. Longer-term, we completely underestimate the dramatic effects of exponential change.

An Inflection Paradox describes these conflicting drivers and cognitive biases that contribute to missing inflection points.

Pace layers: The interplay of timescales

To gain strength from disruption is to have a system that can operate at different rates of change. Thinking in different timeframes allows an interplay between change and constant, stable and unstable, while sustaining through shocks.

Stewart Brand, founder of The Long Now Foundation and Global Business Network, developed the Pace Layer model to provide different levels of corrective feedback.

Brand proposes six layers, from slowest to fastest: Nature (planet), Culture (social, religion), Governance (rule of law, government), Infrastructure (transportation, communication systems, education, science), Commerce (business, industry), Fashion (art, creative, experimental).

In a healthy society, each layer operates at its own pace while respecting the others:

• Fast layers learn; slow layers remember.
• Fast layers propose; slow layers dispose.
• Fast layers absorb shocks; slow layers integrate shocks and ensure they don’t reoccur.
• Fast layers are discontinuous; slow layers are continuous.
• Fast layers innovate; slow layers constrain.

Innovation is a dialog between layers. The first moon landing in 1969 coincided with the first generation of microprocessors that enabled the use of computers in space – an example of the intersection between infrastructure and governance.

However, today’s innovations often create tomorrow’s challenges. Thomas Midgley Jr., one of the most respected engineers of his time, solved the problem of premature combustion in engines by adding lead to gasoline. He was also the father of modern refrigeration, inventing the freon gas used in fluorocarbon refrigerants. Both inventions were harmful to humans and the environment, and were later banned.

Fast-moving projects that solve immediate problems are exciting, but we need to think about the consequences.

Chronos and Kairos: Time concepts as a superpower

Thinking of time in decades instead of years allows you to zoom out to a long-term view, then zoom in to the present, to see how it fits into a broader, transformational longer-term vision. You can thus focus on two time horizons in parallel.

The agility to zoom in and the foresight to zoom out is a rare capability in our short-termist world. Here, you can benefit from both Chronos and Kairos, which are different concepts of time in ancient Greece. Chronos is the objective understanding of time passing; the chronological idea of time. Kairos is a nonlinear, dynamic, and subjective orientation of time; this represents a specific opportunity.

We must develop the agility to use both Chronos and Kairos to reconcile longer term visions with windows of opportunity. Imagining these futures with curiosity will help you see windows as they emerge. These windows may not last long, but Kairos offers the opportunity to anticipate the future at any time.

Agility to reconcile the long-term vision with the present

Figure 1: Both Short-Term & Long-Term Decision-Making Needed Simultaneously Today

Leadership roles must evolve as we reconcile different time horizons with decisions today. We imagine a role called Chief Bridging Officer (CBO) – defined by the agility to bridge the organization’s vision within constantly updating environments.

The CBO’s role is a journey of discovery, with the agility to constantly respond to changes in the external environment. They initiate needed changes with anticipatory vision, consistent with long-term aspirations. The agility of a CBO drives our preparations, mitigations, and our responses to the many possible emergencies that might arise.

The CBO builds and crosses bridges, including connecting present strategic imperatives and bets with long-term futures. We can thus proactively imagine possible futures and inform decision-making today.

Figure 2: In Comes the Chief Bridging Officer (CBO)

Long-term thinking for short-term opportunities

There are many benefits to living and breathing with longer time horizons in our UN-VICE world:

• Less competition: Most of the world tends to focus on the short term. Longer-term horizons allow a multiplier effect of small initial initiatives that grow over time.
• Easier to prioritize: Focus on relevant innovation and initiatives needed for the real transformations ahead, not short-term hype.
• Visioning: Imagine impossible futures with the audacity to make them possible.

As we bridge the present to the futures, leadership and governance roles must evolve. We need to enhance mental agility for extreme (but plausible) changes, while rewiring how our systems are programmed. 

Reconciling short-term priorities with longer-term aspirations can be tricky where change is the norm. This requires us to build agile muscles for changing environments, which can represent major departures from the world we know. The purpose lies in preparation, not prediction.

About the author:

Roger Spitz is President of Techistential (Climate & Strategic Foresight), Chair of Disruptive Futures Institute in San Francisco, and expert adviser to the World Economic Forum’s Global Foresight Network. His latest book is “Disrupt With Impact: Achieve Business Success in an Unpredictable World”.