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