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”.

In Alan Turing’s seminal paper “Computing Machinery and Intelligence”, he posed the now-famous question, “Can machines think?”—an inquiry that laid the groundwork for exploring the cognitive potential of machines.

Today, as we witness AI systems moving beyond narrow tasks into increasingly autonomous roles, this question evolves into a new, urgent line of inquiry: “Can machines act with integrity?” 

Just as Turing’s work invited us to ponder the boundaries of machine cognition, the rise of advanced AI compels us to ask if machines can be equipped to uncompromisingly act ethically and responsibly.

The need for Artificial Integrity—an AI’s ability to consistently recognize the moral implications of its influence and outcomes, learning both ex-ante and ex-post from experience to guide decisions and actions that reflect integrity-driven behavior in.a context-sensitive manner—is an increasingly critical imperative as the sophistication of AI systems evolves.

As these systems exhibit increasing autonomy and decision-making power, they begin to impact critical areas of human life, from healthcare and finance to security and personal relationships. 

The stakes are high: an AI capable of high-level autonomy without a framework of integrity could make decisions that, while efficient or logical, conflict with societal or ethical values.

Moreover, as we approach the theoretical thresholds of superintelligence or Artificial General Intelligence (AGI), the potential consequences of AI decisions become even more significant. In these advanced stages, AI would not only execute tasks but might also initiate actions and adapt independently. 

Artificial Integrity thus serves as a safeguard, guiding AI systems to act in alignment with human values, to respect boundaries in crisis management, and to maintain transparency where decision-making directly affects human well-being.

Ultimately, as Turing’s inquiry transitioned from “thinking” to “acting” with increasing autonomy, it reveals a profound necessity: for society to leverage AI’s full potential safely and ethically, its development must be grounded in Artificial Integrity—a framework that upholds ethical principles as steadfastly as it demonstrates intelligence.

For AI systems to be capable of Artificial Integrity, ethical considerations should be built in as the core aspect of their reasoning, prioritizing Integrity over Intelligence.

Concrete applications include:

Hiring and recruitment

• Case: AI-powered hiring tools risk replicating biases if they are purely data-driven without considering fairness and inclusivity.
• Artificial Integrity systems would proactively address potential biases (ex-ante) and evaluate the fairness of its outcomes (ex-post), making fair, inclusive hiring recommendations that respect diversity and equal opportunity values.

Ethical product recommendations and consumer protection

• Case: E-commerce AI systems often push products based on profit margins or user profiles, potentially promoting unnecessary or harmful items to vulnerable customers.
• Artificial Integrity systems would assess the suitability and ethics of recommended products, avoiding manipulation, and considering consumer well-being, particularly for vulnerable demographics.

Insurance claims processing risk assessment

• Case: AI systems in insurance might prioritize cost-saving measures, potentially denying fair claims or overcharging based on demographic assumptions.
• Artificial Integrity systems would consider the fairness of its risk assessments and claims decisions, adjusting for ethical standards and treating clients equitably, with ongoing ex-post analysis of claims outcomes to refine future assessments.

Supply chain ethical sourcing and sustainability

• Case: AI systems in supply chain management may optimize costs but overlook ethical concerns around sourcing, labor practices, and environmental impact.
• Artificial Integrity systems would prioritize suppliers that meet ethical labor standards and environmental sustainability criteria, even if they are not the lowest-cost option. It would conduct ex-ante ethical evaluations of sourcing decisions and track outcomes ex-post to assess long-term sustainability.

Content moderation and recommendation algorithms

• Case: AI systems on social platforms often prioritize engagement, which can lead to the spread of misinformation or harmful content.
• Artificial Integrity systems would prioritize user well-being and community safety over engagement metrics. It would preemptively filter content that could be harmful or misleading (ex-ante) and continually learn from flagged or removed content to improve its ethical filtering (ex-post).

Self-Harm detection and prevention

• Case: AI systems may encounter users expressing signs of distress or crisis, where insensitive or poorly chosen responses could exacerbate the situation. Some users may express thoughts or plans of self-harm in interactions with AI, where a standard system might lack the ability to recognize or appropriately escalate these red flags.
• Artificial Integrity systems would be equipped to recognize such red-flag reactions, taking proactive steps to alert human supervisors or direct the user to crisis intervention resources, such as helplines or mental health professionals. Ex-post data reviews would be critical to improve the AI’s sensitivity in recognizing distress cues and responding safely.

Intelligence alone, without a guiding moral framework, can easily stray off course, risking harm or unintended consequences. 

Artificial Integrity over Intelligence has become crucial since the moment AI systems, like Google DeepMind’s AlphaGo, demonstrated capabilities that far exceed human prediction or control. 

When AlphaGo learned to play Go at superhuman levels, it achieved this by training autonomously, playing against itself millions of times, and generating an almost infinite array of strategies, moves, and counter-moves. This development highlighted a profound truth: AI can learn and adapt in ways that even its developers cannot fully anticipate. Such power brings both immense potential and significant risks, as AI systems, when acting independently, may develop approaches or behaviors that were neither foreseen nor intended by their creators.

This unpredictability underscores that, as AI continues to grow more autonomous and capable, having a foundation of integrity becomes essential—not just as a feature, and certainly not as a replacement for human integrity (if we do not embody integrity ourselves, how could we hope to design AI capable of mirroring it?)—but as the core safeguard guiding intelligent machines toward responsible and ethical outcomes in all circumstances.

About the author:

Hamilton Mann is an authority on digital and AI for good, tech executive and author of Artificial Integrity (Wiley)

When we talk about the future of the knowledge economy, the first question that comes to mind is whether we should still call it that. Relatively soon, all knowledge, expertise, and the know-how to apply them – accelerated by the combined explosion of AI and robotics – will reside with machines.

We tend to downplay the impact of AI on work, focusing on the automation of rote tasks while humans would focus on those that demand “human qualities” like creativity and empathy. That’s comforting; it helps us overcome the fear of the potential loss of jobs. Fear is a natural reaction.

But AI is more than automation. It shifts our perception of what it means to be human and erodes our sense of human singularity. Copernicus showed us that Earth, and by extension humanity, wasn’t the center of the universe. Darwin that humans were merely evolved animals. Freud that our thoughts and behaviors are governed in part by unconscious drives. Now we have AI, a force capable of challenging what we thought was uniquely human – intelligence, creativity, even empathy.

But AI is already highly empathetic, very creative, and in many ways, very human. It even makes mistakes and can sometimes be a little lazy – contrary to our ingrained view of machines as perfect and predictable. Down the road, AI will be even more powerful, and friction in our interactions with it will go away. Voice, memory of all our interactions, maybe reading of our body language, or even of our brain waves – AI will become a seamless extension of our “knowledge.”

The productivity gains from this AI and robotics transformation will be massive. Today, work is what gives most of us the means to survive. But the productivity gains and the reduction of the need for work as we know it will force societies to rethink how most people acquire the means to live with dignity, while they focus instead on how to live a fulfilling life. People will still engage in activities, some even in rigorous work, but it will be because they want to, not because they need it to survive.

Meanwhile, we face another massive challenge that will shape our lives: the future of our planet. To secure a livable world, we must accelerate innovation to make the breakthroughs that can regenerate the planet, while embracing drastic lifestyle changes.

Among these changes, I don’t believe that traditional fixed offices, empty most of the time, are the future. Their carbon costs (including the commuting) are too high, and they create an artificial physical constraint that hampers our ability to build the diverse teams that foster innovation. Eliminating fixed offices doesn’t mean eliminating in-person connection. It means doing it differently, less frequently, but far more purposefully and meaningfully. It’s more challenging, but we can learn to excel at it.

So, what does this leave to humans? I think the reinvention of the knowledge economy – maybe soon called the purpose economy or the hyper-innovation economy – will mean that humans will find their identity in a triptych: Accountability, Ownership, and Choice. Each element is essential, and they work together as a response to the seismic shifts reshaping what it means to work, to create, to contribute. It is the responsibility of leadership to nurture them.

Accountability is not something we can escape from. No matter how advanced AI becomes, or how much we lean on it. Because in the end, it’s still on us to ensure AI is used responsibly and aligns with human values. Even deciding how much we leverage AI, or whether to use it at all, is a choice that requires accountability. Our responsibility to guide, validate, and oversee what AI produces isn’t something we can hand off. It will always come back to us. This is true for a CEO, a surgeon, a journalist – for anyone: Humans remain accountable for AI.

Ownership is about being fully invested. It makes us up our game. It derives from a sense of purpose, and gives a pride in outcomes. With ownership, people see the outcome of what they do as a reflection of who they are and what they stand for.

Choice is the fair counterpart to accountability and ownership. It is the freedom to choose our approach, our tools, the environment and the pace that works for us. Choice lets any task we engage in become an extension of ourselves, rather than something imposed. It is the power to say, ‘This is how I can best contribute.”

Accountability, Ownership, and Choice together need to be nurtured by the leaders of the post-knowledge economy. They will give humans the power to thrive, not just survive, in a radically different world – one where purpose has become what is distinctively human.

Because at the end of the day, it won’t be just about knowing more. It will be about being more.

About the author:

Pierre Le Manh is President and CEO, PMI Project Management Institute

“Managers probably need financial and accounting, marketing and strategic skills more today than they ever have done. But they also need something else. They need grounding in the messy realities of the human condition, an understanding of politics and culture and an awareness of the historical forces that have shaped the world in which we live. …and they need role models from whom to learn.”

(John Hendry)

“The Western tradition will still have to be at the core, if only to enable the Educated Person to come to grips with the present, let alone with the future. The future may be ‘post-Western‘. It may be ‘anti-Western‘. It cannot be ‘non-Western. Its material civilisation and its knowledge rest on Western foundations: science, tools and technology, production, economics, money, finance and banking.”

(Peter Drucker)

To function in the long term, the modern knowledge society, according to Drucker, needs the educated person to be the determining social type. Before I go into definitions, in order to avoid misunderstandings, I should explain the term “post-capitalist society” used by Drucker to describe the modern knowledge society. In his book of the same name, he defines it as follows: “The new society – and it is already here – is a post capitalist society. It surely … will use the free market as the one proven mechanism of economic integration. It will not be an ‘anti-capitalist’ society. It will not even be a ‘non-capitalist’ society; the institutions of capitalism will survive. … but the center of gravity in the post-capitalist society … is and will be knowledge”. Knowledge has become a crucial social resource. 

The structural change that is taking place in all areas of the post-capitalist or knowledge society requires, as already mentioned, the educated person as predominant social type – someone who is not only a carrier of knowledge, but is able to use this knowledge to effectively contribute to shaping the economy and society. I would like to describe Drucker’s ideal Educated Person as a cognitive-formative social type.  

Drucker believed that modern knowledge society needs this social type like no other society before. This applies in particular to the elites, foremost to management as society’s major leadership group. And this leads Drucker to his understanding of management: “Management is more than a bag of skills, competence and tricks … Management cannot be concerned solely with results and performance. Precisely because the object of Management is a human community held together by the work bond for a common purpose. Management always deals with the Nature of Man … Management will increasingly be the discipline and the practice through and in which the humanities will again acquire recognition, impact and relevance“.

For Drucker, management is not a humanity, but the humanities and social sciences are an indispensable foundation for management education and for the managerial profession. In this sense, management is a liberal art in itself, an interweaving of “art” or “techne” with the humanities. According to Drucker, “Management is thus what tradition used to call a liberal art: ‘liberal’ because it deals with the fundamentals of knowledge, “art” because it is practice and application. Managers draw on all the knowledge of the humanities and the social sciences, … But they have to focus this knowledge on effectiveness and results … In Management, however, the Liberal Arts become what they have always been when they flourished: kinetic energy and guidance to action”.

Based on this insight, Drucker defines the integration of the liberal arts as a central task of management education. Because the society of knowledge workers, to which the managers – the knowledge executives – belong as a “society’s major leadership group”, is a society in which the central social resource is neither physical labor nor capital: “… we do not know precisely how to link Liberal Arts and Management. We do not yet know whether it is going to be a marriage of convenience or a love match – although we do know that they have to have mutual respect. We do not yet know what impact this will have on either party … But what we in Claremont are pioneering is making the Liberal Arts an organic part – indeed a key resource – in the teaching of Management, and Management into a ‘growth market’ for the Liberal Arts.”

Drucker sees another challenge facing education to become an “educated person” in the need to interlink the various areas of knowledge, since these are all of equal value in a knowledge society: “General education which does not integrate the knowledge into one universe of knowledge is neither liberal nor educated. They fall down in their first task: to create mutual understanding – that universe of discourse without which there can be no civilization. Instead of uniting, such liberal arts fragment”.

Under the title Inner Contradictions of the German Universities, the philosopher and sociologist Max Scheler wrote more than 100 years ago: “Rich in knowledge, poor in being able to take a stand and poor in the sense of responsibility and shared responsibility for this stand – the impression is that which our academic youth leaving university so often exposes to the objective eye”.

The goal of a university education for managers, including managers of the economy, in the spirit of Peter Drucker, would be precisely the sense of social responsibility that Scheler missed. Of course, in designing the curriculum, care must always be taken to ensure that the business manager’s first social responsibility is the long-term economic success of the company, something Drucker always insisted on. 

Scheler’s criticism has lost none of its relevance today. For example, in his book Theory of Uneducation the Austrian philosopher Konrad Paul Liessmann complains that lacking a well-founded educational concept, the universities have let themselves be “… sold as the latest trend in the leadership industry” and impart purely operational skills. “The flexible person who puts his cognitive abilities at the disposal of rapidly changing markets throughout his life. Despite everything that people need and can know today – and that is not a small amount – this knowledge lacks any synthesizing power. It remains…piecemeal, quick to produce, quick to acquire and easy to forget.”

Similarly uncompromising sentiments are expressed by US philosopher and management consultant in his book The Management Myth: “I interviewed, hired, and worked with hundreds of business school graduates, and the impression I formed of the MBA experience was that it involved taking two years out of your life and going deeply into debt, all for the sake of learning how to keep a straight face while using phrases like ‘out of the box thinking‘, ‘win-win situation‘ and ‘core competencies’“. 

According to Stewart, the same thing is true of management management development post university, with the result that “many of today‘s Managers are well trained enough, but their training does little to improve their level of education. If Henry Adams came back to life, he would recognize them instantly. ‘They are a crowd of men … who seem … ignorant that there is a thing called ignorance‘. This lack of education does more harm to society and individuals than do any deficiencies in training“.

To get to the point, it sometimes makes sense to use the ‘Going beyond the goal’ method. This is shown by the very pointed quotes from Liessmann and Stewart from 2006. How can the current state of management education be summarized in the spirit of Peter Drucker? My impression is that awareness is there, theory and practice are also slowly gaining momentum. I can share an example from my own experience: at the Technical University of Munich, rightly nicknamed the ‘’entrepreneurial university’, where I have lectured in the department of knowledge and research management for 10 years, flexible teaching programs give students plenty of room for creativity. This goes too for formal and content-related integration of the humanities into degree programs. Thus coursework achievements in politics or philosophy from other departments can be credited to the TU. These freedoms are based on far-sighted decisions made 20 or more years ago that students are increasingly making use of today. The growing popularity in my seminars, in which the work of Peter Drucker is the focus, is part of the same phenomenon. 

Similar changes in curriculum frameworks are now on the agenda at many universities and business schools. For this reason, there is hope that the Stewartian “straight face” of the business school graduate will increasingly be guided by a “reflected head”. With regard to management development, there is also a positive shift towards the Druckerian ideal of management education, albeit not quite as marked as that of higher education: The awareness is there, theory and practice still have to get going! I will comment on this in detail in my next post.

About the author:

Management consultant for more than 40 years, Peter Paschek shared a close two-decade friendship with Peter Drucker. His book: Peter F. Drucker – Memories of a Conservative Christian Anarchist – was published in 2020.

Join Peter Paschek at the Forum on November 14 at panel 2: Did Drucker Get It Right about Knowledge Work? It’s been twenty-five years since Peter Drucker outlined what he called the greatest challenge facing management in the twenty-first century: improving the productivity of knowledge work. What did he predict would prove hardest, and what did he never imagine?