Technology as a Competitive Advantage: How AI Changes Work and the Skills We Need

Cover image: AI-generated conceptual image of lifelong learning with fictional people.

AI can draft a text, organize information or suggest a solution in seconds. That moves a task forward. Whether it produces good work depends on what happens next: can someone judge whether the result is correct, addresses the problem and can responsibly be used in that particular setting?

This question connects my work in technology, education and business practice. The occasion for this article is my contribution, “Technology as a Competitive Factor,” at NextWork Bavaria in Munich on October 29, 2026. Its program brings together technology, organization and people. For me, their interaction is where the important work begins.

I find little value in reducing the future of work to a bet on how many people will eventually still be needed. I want to understand what people will do, how they will work and how we can best prepare them. AI and automation can contribute a great deal. We need to design their use with the same care that we give to educating the people who will work with them.

My argument is that technology becomes a competitive advantage when people can use it well. That requires professional competence, a willingness to learn and organizations that develop both.

As of October 29, 2026. The article distinguishes research findings, demographic scenarios and my own conclusions where each is discussed.

How AI Changes Tasks and Occupations

A job contains many different activities. A professional searches for information, talks to other people, investigates a problem, documents the work and makes decisions. Technology can support or automate these activities to different degrees. Claims about replacing an occupation therefore deserve a closer look at the work involved.

The term AI also covers different tools. A language model can help with writing. A system trained on measurement data can flag anomalies. A robot can perform physical tasks within an appropriately designed working environment. These applications place very different demands on data, integration, safety and skills.

Technical feasibility alone tells us little about whether a solution will operate reliably and economically in everyday use. It must fit existing processes. Someone has to supply suitable data, establish access permissions, manage exceptions and determine whether the whole process has improved.

The ILO’s methodological brief on workers’ exposure to AI explains why measures of technical exposure do not, on their own, predict actual job losses or productivity gains. They initially describe which tasks could be affected.

This suggests a useful sequence for practice. Understand the task and its constraints, decide what technology should contribute, then assess which activities disappear, which emerge and which skills they require. I also discuss this attention to real processes in my article on robotics and automation at EDAY in Vienna.

What We Know About Productivity and Employment

There are good reasons for optimism. The history of automation shows how technology can complement human work and generate new demand. David Autor examines these mechanisms in his essay on the history and future of workplace automation. Technology also changes the activities employers need and what they pay for them. A positive employment balance is not automatic.

Negative effects have been documented too. Acemoglu and Restrepo’s study of industrial robots in US labor markets finds employment and wage losses in more exposed local markets. That finding concerns a particular technology and research setting. It is no more a universal rule than the claim that every new technology necessarily creates more jobs than it displaces.

Productivity gains can also be measured. In “Generative AI at Work,” a study of 5,172 customer support workers, AI assistance increased issues resolved per hour by an average of 15 percent. Less experienced or lower-skilled workers benefited particularly strongly. This is a finding from that application, rather than a general performance multiplier for every occupation.

My practical conclusion is to look for improvements where AI assistance helps a specific task, then examine the outcome across the entire process. A faster draft can be useful. If checking it takes longer than the work saved, or errors become expensive later, the calculation changes.

Productivity therefore means more to me than speed. Quality, reliability, resource use and the ability to handle exceptions matter too. Counting generated documents or interactions with a tool tells us little about whether a business is doing better work.

Why Demographic Change Adds Urgency

For Germany, Destatis projects 3.2 million fewer people aged 20–66 under high net immigration, or up to 4.9 million fewer under low net immigration, by the mid-2030s, relative to its baseline.

These population scenarios do not estimate unfilled jobs. Participation, migration, skills, working hours and demand influence the actual labor market.

The international picture also varies. The ILO’s Employment and Social Trends 2026 report distinguishes aging economies from countries with rapidly expanding labor supply that need to create enough good jobs. This does not imply a uniform global labor shortage beginning in a particular year.

For a business in Germany, the question remains pressing: how can it maintain its capabilities as experienced people leave and new recruits are difficult to find? Technology can help make better use of scarce working time and improve access to knowledge. People also need a realistic opportunity to develop the required skills. Recruitment difficulties and problems entering the labor market can coexist.

An Example: Troubleshooting With AI Assistance

Consider a hypothetical example in a technical workplace. A machine develops a fault. An AI assistant could search approved manuals, summarize earlier records and suggest possible causes. This might make the preparatory work easier.

A qualified professional still has to assess whether a proposed explanation fits the actual situation. Are the readings plausible? Does the instruction apply to the correct machine configuration? Which safety requirements apply? What testing is permitted, and when should someone else become involved?

Step	Possible AI contribution	Human responsibility
Define the problem	Search documentation and organize relevant information	Describe observations, operating conditions and constraints accurately
Investigate causes	Suggest hypotheses and relevant references	Check measurements and reject unsuitable suggestions
Choose an approach	Make relevant instructions accessible	Assess safety, responsibilities and permitted actions
Document the outcome	Structure a draft report	Record actual actions and findings in a traceable way

This example does not describe a client project or claim a measured saving. It illustrates the questions a company should answer before turning a technical possibility into a new workflow.

The training implications are particularly interesting. An early-career employee could first propose an explanation independently, then examine the AI suggestion and discuss the decision with an experienced colleague. That would combine tool use with the development of professional judgment. Learning becomes visible when the person can explain why a suggestion is appropriate or needs to be rejected.

Having an answer available does not replace that ability. A well-written fault report alone does not make someone an experienced maintenance professional. A tool can support a learning pathway. We have to design the pathway ourselves.

The Skills That Matter in an AI-Enabled Workplace

I would organize the discussion about future skills around six connected areas. This is a practical framework, rather than a permanent ranking. The OECD synthesis “Skills in the AI age” emphasizes strong foundations alongside digital and complementary skills. The depth of technical knowledge required depends on the work.

Domain knowledge and foundations. Understand relationships and recognize significant errors.
Problem and process understanding. Describe goals, constraints and interfaces.
AI and data literacy. Use suitable tools and account for their limits.
Judgment and responsibility. Evaluate outputs and justify decisions.
Collaboration and communication. Share knowledge and connect different perspectives.
Learning and independence. Explore new approaches and develop the ability to act competently.

Domain Knowledge Provides the Foundation

Asking a useful question often requires understanding the subject already. Assessing a technical proposal requires professional standards. Using a statistical claim responsibly requires an understanding of scale and uncertainty. I would therefore strengthen educational foundations and make their connection to practical application more explicit.

In the troubleshooting example, this means understanding how the system works and which observation makes a particular cause plausible. That knowledge enables a check that goes beyond the impression that the text sounds convincing.

Understand Problems and Processes

Technology is easier to use well when the purpose is clear. What should improve? Where does the task start and end? Which handovers to other people or systems are involved?

These questions connect technical and organizational skills. A locally faster solution can create extra work at the next interface. People should be able to recognize such relationships and explain their requirements clearly.

Learn to Use AI Appropriately

Not everyone needs to develop a model or write code. In many roles, the initial priorities are choosing a suitable tool, providing relevant context and evaluating the result. This includes recognizing which information may be used and when a different approach would be more appropriate.

Prompting belongs within this practical competence. Designing a good workflow also involves data, interfaces, quality and responsibility. Specialist technical roles need the corresponding depth of expertise.

Practice Judgment

A valuable learning task might involve spotting a plausible error. Why is the proposed calculation unsuitable? Which assumption is missing? What source needs checking?

I would deliberately include such exercises in education and professional development. What matters is the reasoned decision. A checkmark beneath an automatically generated answer is meaningful only if the person reviewing it can understand and challenge it.

Make Collaboration Understandable

New tools often affect several people’s work. Business functions, technology teams, management and people involved in the process need to communicate. That calls for understandable explanations and a willingness to take questions from another perspective seriously.

My proposal for an understandable one-page explanation of publicly funded technology in the New York and UN article follows the same conviction: people need to understand an issue if they are to help shape decisions about it. Inside a company, that principle can be applied to the goals, limits and responsibilities of an AI deployment.

Enable Learning and Preserve Independence

For me, a willingness to learn means trying new technologies, asking questions and developing one’s way of working. It also includes giving a reasoned explanation for stopping an experiment or declining to use a tool for a particular task.

This attitude is not determined by someone’s year of birth. My article “AI After 60” also addresses curiosity, critical evaluation and the opportunity to form an informed opinion. Professional development should offer the same access. People should also retain enough understanding of their work to respond sensibly when an output is unusable or a system fails.

Who Will Develop Tomorrow’s Experts?

In conversations with people in the market, I have noticed a more sober view of the idea that AI could simply make junior employees unnecessary. That personal impression is not a labor-market statistic. My case for strong early-career training does not depend on claiming that hiring has undergone a general reversal.

The current Stanford analysis “Canaries in the Coal Mine?” continues to show less favorable employment trends for 22–25-year-olds in AI-exposed US occupations than for less-exposed peers, primarily through reduced hiring. The authors describe the patterns as descriptive; they do not establish a definitive causal AI effect or describe all junior positions in Germany.

Organizations that need experienced professionals in a few years must create opportunities to learn today. As entry-level tasks change, the development of experience has to change with them. Automating tasks and subsequently lamenting a lack of experience is an inadequate approach.

I would assess competence-based education by what a person can do professionally and explain. This might be a justified diagnosis, a traceable analysis or a responsibly completed practical task. Such abilities require knowledge, practice and feedback. A course certificate or an impressive output alone does not tell us enough.

AI can be used within that learning: to offer alternative explanations, compare ways of solving a problem or provide exercises. I would use it at suitable points throughout a program. Learners should also find errors, encounter limits and explain their own decisions.

Mentors have a demanding role. They need to distinguish understanding from the ability to reproduce a plausible output. Follow-up questions, changed conditions and tasks that reveal the reasoning process can help. A work sample can certainly involve AI. The person should then be able to defend the decision and respond to new information.

Learning also continues beyond a first qualification. An experienced employee brings valuable knowledge and may need support with a new tool. A new recruit may be familiar with the interface while still developing professional depth. Both need appropriate opportunities to learn. The label “digital” tells us little about a complete competence profile.

The Responsibilities of Employers and Leaders

An employer can ask people to be willing to learn. Without time and appropriate conditions, that request may achieve little. I therefore consider competence development part of the investment in technology.

When introducing a system, an organization should establish who will perform which tasks and how those people will be prepared. People who do the work every day can offer important insights into exceptions, unnecessary handovers and practical obstacles. Their knowledge belongs in the design from an early stage.

The economics deserve a complete view too. Integration, operation, checking, error handling and training all belong alongside the tool itself. That applies to an AI assistant and to an automation system involving physical hardware. The purchase alone tells us little about the eventual benefit.

For a limited pilot, I would agree on a few understandable measures of success. Is the task performed reliably? Does quality improve? What effort does the whole process require? Can the people involved handle exceptions? How do they experience their work?

The use of any time saved also needs discussion. A company could provide more output, improve quality, relieve pressure on employees or develop new activities. The appropriate combination deserves a deliberate decision. Otherwise, a technical improvement could become another source of intensified work.

Responsible design also requires clear responsibilities and a way to report problems. My thinking on Robotic & AI Governance starts here: a system’s capabilities, the limits of its use and human responsibility need to fit together.

What Human in the Loop Should Mean in Practice

People remain important in many processes for setting goals, making decisions, operating systems and taking responsibility. Merely having a person present does not make a process well controlled. I would check four conditions: does the person understand the task, have the relevant information, have enough time, and have both the authority and ability to intervene?

If a condition is missing, oversight can become a formality. Someone expected simply to pass an output along without the expertise to assess it has not been given a meaningful control role.

This does not mean that every automated step needs manual approval. Human involvement should fit the task and its consequences. Nor can we calculate total job creation merely from the existence of maintenance and oversight work.

My optimism concerns what well-qualified people can achieve with suitable tools. AI can offer substantial assistance to experts. How particular tasks and positions change nevertheless remains a specific economic and organizational question. A blanket employment guarantee would be poor advice.

Where Organizations and Employees Can Start

A useful starting point is a manageable assignment. I would select an existing process and work through these steps together:

Understand the task. Describe the problem, the desired outcome and current difficulties.
Define technology’s contribution. Identify suitable assistance, limits and necessary human decisions.
Develop competence. Plan responsibilities, learning time, practice and professional feedback.
Run a limited trial. Examine quality, total effort and working conditions against agreed criteria.
Evaluate and decide. Improve the process, expand it or explain why the trial should end.

Employees can apply the same questions to their own work. Which recurring activity takes time? How do they recognize a good result? What knowledge do they need to check it? A small trial agreed with the organization may teach more than a collection of general predictions about the future.

For people entering a profession, my advice is to combine familiarity with tools and depth of subject knowledge. Experiment, verify the result, explain your approach and seek feedback. These activities make the development of professional competence visible.

The Future of Work Requires Education and Deliberate Design

I expect people to continue playing a central role in work as new technologies emerge. How well they can fulfill that role depends on our decisions. The name of the next technology changes little about the responsibility to prepare people to use it well.

That is why I want lifelong learning to mean something concrete: time to practice, access to appropriate tools, strong foundations and feedback from people who understand the subject. For me, competence-based education translates these conditions into professional action that can be demonstrated and explained.

Employers, educational institutions and employees each have a part to play. I want us to use the technical possibilities while preserving the ability to understand, decide and take responsibility. That is where I see technology becoming a competitive advantage.

Frequently Asked Questions About AI and the Future of Work

What does the future of work mean in the context of AI?

Here, the future of work refers to changes in tasks, collaboration, skills and working conditions brought about by new technologies. For organizations, it involves designing specific processes. For employees, it concerns the activities they will perform and the abilities they need to develop. The term does not describe a single predetermined future.

Will AI replace people and their jobs?

AI can take over activities and consequently change or displace positions. It can also support human work and enable new tasks. Outcomes depend on the application, economic demand and organizational decisions. A prediction of technical capability is therefore not a measured number of future job losses. The research sources in the article explain this distinction.

Which skills matter when working with AI?

I would develop domain knowledge, problem understanding, AI and data literacy, judgment, collaboration and learning ability together. The appropriate combination depends on the role. A useful learning task combines tool use with professional evaluation: the person should be able to explain what they accepted, changed or rejected, and why.

Will everyone need to code or become an AI expert?

Many roles require practical competence suited to the task. People should be able to use appropriate tools, evaluate results and recognize limits. Those developing, integrating or safely operating systems need greater technical depth. AI’s broad relevance does not imply the same educational objective for every occupation.

Why should companies continue to train junior employees and apprentices?

Experience develops through learning and supervised practice. Organizations that will need experienced professionals should make that development possible. Changing entry-level tasks require adapted learning pathways, including independent attempts, AI-assisted comparisons and professional feedback. This is a strategic argument for education and staff development, rather than a guarantee about hiring in every occupation.

What does competence-based education mean in practice?

It focuses on whether a person can perform a professional task competently and explain the decisions involved. Knowledge, practice and feedback are essential. One possible assessment is a work sample followed by questions and changed conditions. AI can be part of the exercise; assessment should also examine personal understanding and the ability to act.

How can employers support lifelong learning?

I recommend dependable learning time, suitable tools, realistic tasks and accessible professional guidance. Development should connect to actual work and accommodate different starting points. A culture that welcomes questions and reasoned objections matters too. These arrangements make shared responsibility for learning practical for both employers and employees.

What does human in the loop mean for AI at work?

The term describes human participation in a partly automated process. It is meaningful when someone understands the task, has relevant information and sufficient time, and can actually intervene. The necessary involvement depends on the activity. A formal confirmation without an independent assessment does not establish reliable control.

Will AI solve Germany’s shortage of skilled workers?

AI can help make more effective use of available working time. It does not automatically solve problems involving skills, access or staffing. A shrinking working-age population increases pressure to act, but does not directly predict unfilled positions. Training, good working conditions and appropriate process design remain responsibilities in their own right.

How can I start using AI sensibly in my area of work?

Choose a clearly bounded task and agree on the trial with the people responsible. Define a good result, suitable data and necessary checks. Compare quality and total effort with the existing approach. Record what you learned and which abilities you still need for reliable use.

Sources and Further Reading

One Page That Gives People a Say: My Proposal at the UN

Cover image: AI-generated illustration of dialogue about technology with fictional people; not a photograph of the event.

Anyone receiving public funding to research or develop new technology should explain to the public, in understandable language, what they are working on. For me, one page would be the minimum. What problem is the technology meant to solve? What can it demonstrably do, where are its limits, and how can the people affected have a say?

I have been making this proposal for years. At the UN Digital Cooperation Day in New York, I raised it again. For me, it captures a practical task of Robotic & AI Governance: people need to be able to assess technology and express what they want while important decisions are still open.

An understandable text is the starting point. Participation begins when questions and objections receive an accountable response: who made the decision, and what changed as a result of the feedback?

In this article: first, the documented outcomes from New York and the original recordings; then my personal commentary and a practical proposal for publicly funded technology.

Go to the outcomes · Go to my one-page proposal · Go to the questions and answers

Event: 21 September 2026 · Research and source review: 18 October 2026

Part 1 · Report: What the UN Digital Cooperation Day Produced

On 21 September 2026, the UN Office for Digital and Emerging Technologies (ODET) held the second UN Digital Cooperation Day in New York. During the high-level week of the UN General Assembly, the event brought together three tasks: assessing scientific evidence, informing policy decisions, and building the capacity for wider participation in AI. The official programme documents the sessions and recordings.

The outcomes at a glance

Topic	What is available	Status and significance
National AI strategies	Introduction of the UN Blueprint for National AI and Data Strategies, drawing on experience from Lesotho and Guinea	A practical guide to implementing national strategies. Lesotho had already piloted it in August.
Scientific risk assessment	The first thematic brief from the Independent International Scientific Panel on AI	Released on 21 September as an advance unedited version; its focus is AI agents, misalignment and risks of losing human control.
AI and disaster risk reduction	A new partnership between ODET and the UN Office for Disaster Risk Reduction (UNDRR)	An announced collaboration on using AI to reduce disaster risk.
Work, open AI and cross-border testing	Discussions of skills, open-source AI and shared sandboxes for AI governance	Topics for exchange; a session alone does not amount to an adopted international standard.

Sources for the overview: UN event report, ODET report on Lesotho's pilot, and the AI Panel's first thematic brief.

National AI strategies: from policy ambition to practical planning

The Blueprint starts with a practical question: how can a national AI strategy become something a country can actually implement? In Lesotho, the work covered computing capacity, energy, connectivity, data, skills and financing. The August workshop produced a draft national roadmap for computing capacity. The New York launch brought together Lesotho's experience and Guinea as the next country to apply the approach. The ODET report describes that process.

AI agents: a scientific brief on risks and questions of control

The new thematic brief examines how the behaviour of capable AI agents can diverge from human intentions. It takes a documented incident during the safety evaluation of such systems as its starting point. The Panel places the incident in its research context and considers safeguards used in other fields with demanding safety requirements.

The limits of the assessment matter: the brief estimates neither the probability nor the timing of a severe future loss of control. It does not prescribe binding policy measures. The original publication is labelled an “advance unedited version”.

The preliminary overall report, released on 1 July 2026, and the new thematic brief serve different purposes. The overall report assesses the field more broadly; the September brief explores a particular risk question. The scientific Panel's FAQ explain this sequence of publications. My earlier assessment of the preliminary UN AI report remains available as a contribution written at that time.

Work, education and a shared evidence base

The session on the future of work focused on education, skills and the readiness of people and institutions. This included the question of who can participate in an AI-enabled economy. Recording: work and education.

The “Panel of Panels” compared international scientific assessments: where do they agree, where are the gaps in knowledge, and which perspectives are missing? Experiences from the Global South formed part of that discussion. The focus was on improving the evidence available for governance. Recording: Panel of Panels.

What can be recorded as an outcome

The day brought together a practical guide, a new scientific publication and an announced partnership. Other sessions provided opportunities to exchange experience on implementation and cooperation. The official event report places these steps in the context of implementing the Global Digital Compact. It does not document a decision adopting my one-page proposal.

Watch the official recordings

The UN programme links to three parallel recordings. The timestamps provide starting points for the main topics in the Oasis stream; the room links also give access to the other sessions.

Four starting points in the Oasis stream:

Topic	Starting point in the recording
National AI strategies and the Blueprint	from 01:11:44
AI, work, education and skills	from 02:47:32
Introduction of the first scientific thematic brief	from 05:42:11
Building a shared scientific evidence base: Panel of Panels	from 06:07:11

The timestamps refer to elapsed video time and mark the beginning of each topic.

My initiative in the UN Portal: Robotics & AI 2050+

My Delphi study is now also listed as an initiative in the UN Digital Cooperation Portal: Robotics & AI 2050+. The direct entry in the MUDT/TUM profile describes a global, independent study of the future of robotics and artificial intelligence. It brings together expert assessments and covers six enabling technological capabilities and nine application domains.

The Portal maps the initiative to areas including international AI governance and inclusion in the digital economy. The study remains an independent research undertaking; the listing makes it discoverable in the international cooperation directory.

For me, these two tasks complement each other. The study aims to develop a systematic understanding of experts' views of the future. The one-page proposal aims to enable people outside the specialist community to contribute their own questions and expectations.

Part 2 · Personal Commentary: One Page That Gives People a Say

The following reflections express my own position. I raised the proposal at the event and want to put it up for discussion.

One proposal, several opportunities to raise it

I attended the UN Digital Cooperation Day in New York as an expert.

The visit included many interesting conversations with people who help shape the AI debate. I raised my one-page proposal in several settings: in conversations and in a contribution framed as a question to a panel.

My concern is a practical connection between research and the public. If we develop technologies that may change many people's everyday lives, those people need an understandable basis for forming a judgement. They also need to be able to raise their questions.

My proposal starts with something we can take responsibility for as researchers and developers: explaining what we do, what we know and what remains uncertain.

Dominik Bösl (left) and Gary Marcus in a selfie together in New York. — Guess who I bumped into in New York — and promptly roped into a selfie. Gary Marcus was a little caught off guard, but we had a lovely chat afterwards.

A scientific paper serves a different purpose from a public explanation

Scientific publications primarily address fellow specialists. They should make methods, results and conclusions open to scrutiny. That requires technical precision and the terminology of the field.

People outside that field need an understandable way in as well. They help fund research through their taxes. They encounter its applications in everyday life. Some benefit; others have to deal with consequences they did not choose.

That is why the proposal explicitly includes us as professors. We should be able to explain the problem we are working on, which expectations we consider justified and where we still have questions ourselves.

Writing one understandable page requires choices. What do people need to know to make sense of the project? Which terms need explaining? Which limitation must I avoid hiding behind an impressive demonstration?

Six questions for publicly funded technology

For me, the page should answer six questions.

Problem & people affectedWhat are we trying to solve – and for whom?
Technology & alternativesWhy this approach? What else could work?
Demonstrated capabilitiesWhat already works – and under which conditions?
Limits & what is unknownWhat can we not do, or do not yet know?
Risks & responsibilityWho decides on safeguards and when to stop?
Questions & participationHow can the people affected influence decisions?

1. What problem are we trying to solve, and who would benefit?

Start with the need. Who is meant to benefit from the project? What would an improvement look like? The explanation should also address who might face disadvantages or additional demands.

2. Why do we need this technology? What alternatives are available?

A technical possibility does not, by itself, justify its use. There may be a simpler way to solve the problem. A combination of technical and organisational changes may be needed. The public should be able to understand why a particular approach was chosen.

3. What demonstrably works already, and under what conditions was it tested?

A promise needs a basis. What has actually been tested? Under which conditions? How much of that can already be transferred to the intended application? Understanding the difference between an experiment and reliable operation helps people assess expectations.

4. What can we not do yet, and what do we not yet know?

An honest “We do not know yet” is a useful answer. It makes uncertainty visible. This includes technical limitations and open questions about the consequences of a possible deployment.

5. What risks do we see? Who decides on safeguards or stopping the project?

Identifying risks needs to affect the design. Who is responsible? What safeguards are planned? Who can intervene if assumptions no longer hold or problems arise? These responsibilities should be understandable too.

6. How can affected people ask questions and influence further development?

A contact address can be a starting point. What matters is what happens next: when can people contribute? Who answers their questions? How will they find out whether an objection changed a decision?

I also summarised the proposal and the six questions in my LinkedIn video post of 30 September 2026.

What this might look like in practice

Imagine a publicly funded research project developing a service robot for public buildings. Its purpose is to help visitors find their way. This is a hypothetical example to illustrate the proposal.

The public explanation could first identify the navigation problem being studied and the people the support is intended to help. It would then need to explain why a robot is being tested for this purpose. Better signs, an accessible building map or a staffed information point would be obvious alternatives to compare.

To explain its capabilities, the project would need to describe what works so far and under which conditions. Does the system recognise differently worded questions? Can it handle several languages? What happens when it does not understand a question? Which tasks will a person continue to handle?

If the project needs sensors or personal data, the explanation should describe why they are needed and which questions remain open. It should also make clear how people can reach their destination without using the robot.

Finally, the project needs feedback from everyday use: from visitors, employees and people whose needs may have been overlooked in the first design. Their suggestions should still be able to influence the design.

That turns a general announcement into a project people can discuss in practical terms.

Basic research needs room for what we do not know

For basic research, the honest answer to the question of practical benefit may be: we do not know yet. Advancing knowledge is a legitimate aim. I do not want every scientific question to have to justify itself prematurely with a supposedly certain business case or an immediately measurable everyday benefit.

Even then, it is possible to explain the question being studied, why it is scientifically interesting and what uncertainties remain.

My proposal does not require publishing trade secrets or security-sensitive details either. A public explanation must respect those boundaries. Anyone promising a benefit should, however, be able to explain the basis for that promise and what is still an expectation.

The page opens the conversation

One page alone does not create participation. I therefore want it to come with an accessible contact person and an accountable way of handling feedback.

What questions were asked? What objections were raised? Who decided how to respond? What changed, what was deliberately retained, and why?

The point is not to accept every suggestion. Participation becomes credible when decisions are explained and people can see what their contribution achieved.

This approach connects my thinking on Robotic & AI Governance with the practical approach in AI After 60: explain clearly, make independent checking possible and help people form an informed opinion.

The same question arises in social media regulation for young people: how can protection, education and self-determination work together? Support must fit the people and their circumstances. Understandable information and meaningful opportunities to have a say remain fundamental tasks.

A pilot measured by how well people understand

The objection “more bureaucracy” is justified. My proposal is therefore to integrate the page into existing project reports and update it at important milestones. I do not want an additional form that ends up circulating only between the project and its funding body.

I would start by piloting the requirement in one funding programme. For me, that includes testing the explanation with people outside the field. Can they describe the purpose in their own words after reading it? Can they tell what already works and what is still uncertain? Can they find the right route for asking a question?

It would be equally important to assess whether feedback arrives in time to influence further development. Those are the questions by which I would judge the pilot.

International scientific cooperation remains important. My assessment of the UN's scientific AI Panel considers that wider level. An understandable explanation of a particular project gives people access to decisions that affect them directly.

I would like us to start. Anyone receiving public funding should be able to give the public an understandable account of their work. One page is a manageable first step. Its value will depend on the conversation and participation we build around it.

Frequently Asked Questions About the UN Outcomes and the One-Page Proposal

About the meeting and its outcomes

What was the UN Digital Cooperation Day 2026?

The UN Digital Cooperation Day 2026 was an event organised by the UN Office for Digital and Emerging Technologies on 21 September in New York. It focused on science, policy and capacity-building for a global AI future. Governments, researchers, businesses and civil society came together to exchange perspectives. The official programme sets out the context and individual sessions.

What concrete outcomes did the UN Digital Cooperation Day produce?

Documented outcomes include the launch of the UN Blueprint for National AI Strategies, the scientific AI Panel's first thematic brief, and a new ODET-UNDRR partnership on AI and disaster risk reduction. Topics discussed also included work, open AI and cross-border sandboxes. The UN event report and the thematic brief's publication page document these different kinds of outcomes.

Were new binding global AI rules adopted there?

The official materials reviewed here document practical guidance, scientific assessment, cooperation and discussion. They do not record the adoption of a binding global AI law or of my one-page proposal. The precise document type and its status therefore matter. A scientific assessment informs policy decisions; my proposal for publicly funded technology remains clearly marked as personal commentary. Sources: UN event report, the Panel's mandate and publications.

How does the September thematic brief differ from the preliminary UN AI report?

The preliminary report of 1 July 2026 assesses AI's opportunities, risks and impacts more broadly. The first thematic brief, released on 21 September, focuses on AI agents, misalignment and risks of losing human control. It explores one question in greater depth rather than simply replacing the overall report. The Panel explains its publication sequence in its own FAQ. My earlier blog post about the overall report retains its historical context.

What does the listing of Robotics & AI 2050+ in the UN Portal mean?

The UN Digital Cooperation Portal lists the global, independent Delphi study “Robotics & AI 2050+” as an active initiative in the MUDT/TUM profile. The entry describes its research approach and maps it to objectives of digital cooperation. This makes the initiative discoverable in the cooperation directory. The listing alone does not establish particular findings or the completion of a new survey round. The details are available in the direct UN Portal entry.

Where can I watch the UN event recordings?

Official recordings are available for Oasis, Bamboo North and Bamboo South. In the Oasis stream, the timestamps above lead directly to national AI strategies, work and education, the scientific thematic brief, and the Panel of Panels. To hear individual statements in context, viewers can continue watching the full stream from those points.

About my proposal for technology funding

What is a one-pager for publicly funded technology?

I mean one understandable public page about a research or technology project. It explains the problem being investigated, why the approach was chosen, what demonstrably works, where the limits and risks lie, and how affected people can have a say. After reading it, people outside the field should be able to ask informed questions. The page complements scientific publications by providing an accessible way in for the public. I also explain the proposal in my own video post.

Who should the one-page requirement apply to, and what should the page cover?

My proposal addresses anyone receiving public funding to research or develop technology. That explicitly includes universities and us as professors. I propose six answers: the problem and the people affected; the technology and its alternatives; demonstrated capabilities; limitations and what is not known; risks and responsibility; and opportunities to ask questions and influence decisions. This gives the public an understandable basis for judgement without requiring every reader to complete a specialist degree first.

How can basic research be explained when its eventual benefit is still unknown?

Basic research can explain its scientific question, why it matters and what remains open. A later practical benefit may be unknown. That is precisely what the page should then say in understandable language. I do not want to force a supposedly certain everyday benefit that the research cannot yet demonstrate. An honest account of what is not known helps the public distinguish new knowledge, expectations and substantiated results.

Would this require publishing trade secrets or security-sensitive details?

My proposal calls for an understandable explanation within justified limits of confidentiality. Goals, expected benefits, demonstrated capabilities, open questions and responsibilities can often be described without disclosing protected technical details. Where information has to be withheld, the public explanation should make that boundary understandable. The aim remains to enable people to assess a project and ask questions about it; the page is not a complete technical disclosure.

How could the one-pager be piloted without creating more bureaucracy?

I would initially integrate it into existing reports in one funding programme and update it at important milestones. What matters for the pilot is whether people outside the field can subsequently explain the project, recognise its limitations and direct a question to the right person. It should also assess whether feedback reaches the development process in time. For me, success means understandable information and workable routes to participation.

When does understandable information become meaningful participation?

Participation begins when questions and objections can influence decisions that are still open. That requires accessible contacts, suitable opportunities for dialogue and accountable responses. People should be able to see what changed because of their feedback, or why a decision was retained. Both technically informed objections and questions from people without specialist knowledge need a place. My articles on AI After 60 and Robotic & AI Governance develop this thinking further.

4 Robotic Revolutions: 10 Years On

First published: 11 August 2024 (German original); this English version was published on 25 July 2026. Factual corrections and update: 4 October 2026.

Update, 4 October 2026: The following market data and FAQs reflect the July version. In World Robotics 2026, IFR reports a worldwide stock of five million industrial robots for 2025. The 2026 service robotics report has also been released. The AI Act omnibus amendment entered into force on 27 July 2026; the later legal position is explained in the dated update to the AI Act article.

Ten years after I first proposed the „4 Robotic Revolutions", the fourth revolution has stopped being a keynote promise. It has become an operations problem. I proposed the four phases in 2016 to bring order into a debate that treated every product launch as a revolution. On July 21, 2026, I used my opening keynote at the A3 Technology Transfer Congress in Augsburg to draw a first anniversary balance sheet. This article is the long-form version of that balance sheet. It is written not from the perspective of a demo lab, but from the perspective of the factories, hospitals, warehouses and lecture halls in which the four revolutions are actually competing for oxygen.

Stand in a workshop where a cobot works next to a human operator without a fence for the first time, and you understand immediately why categories matter. The cobot is not the event. The operator is not the event. The event is the process decision behind them: who is liable when the sensor misreads. Who stops the cell when the AI path planner picks a trajectory that looks unfamiliar to the human. Who trains maintenance to read adaptivity as designed behaviour rather than as a defect. These are the questions that I baked into the four phases of robotics a decade ago, because I had learned in KUKA innovation management, later at Festo, at Deutsches Museum and across many factory visits that technical readiness collapses on contact if organisational readiness is missing.

Why four revolutions in the first place

The idea was constructed by analogy with the evolution of the computer. Computers became smaller, then mobile, then ubiquitous, then invisible inside processes. Miniaturization, mobilization, ubiquity and pervasiveness described the move from the data centre to the smartphone to the embedded sensor. Robotics repeats those moves, in a different sequence: first the cage, then sensitivity, then mobility, then cognition. Together with Bernd Liepert, I proposed the four-phase model at IEEE IROS 2016 in Daejeon. The paper is titled „4 Robotic Revolutions - proposing a holistic phase model describing future disruptions in the evolution of robotics and automation and the rise of a new Generation R of Robotic Natives". The A3 region press release for the 2026 congress captured the core thesis in one sentence: four robotic revolutions, and the fourth is only just beginning (region-a3.com).

The phases are not a strict chronology. They overlap inside every plant, sometimes inside the same cell. Walk into a large factory today and you will find the first revolution in the body shop, the second in final assembly, the third in intralogistics, and the fourth in quality inspection and testing. The map is a maturity structure, not a timeline. To assess where an organisation stands, ask how the four phases relate to each other, which of them dominates, which is missing, and which is being underestimated.

Four robotic revolutions: caged automation, cobotics, mobile robotics and perceptive-cognitive systems over ten years

First revolution: the caged robot

The first revolution starts with a safety philosophy, not with a model. The robot goes behind a fence because its motion is too fast, too strong and too deterministic to accommodate human exceptions. In 2016 I described the core of this phase as „how to bolt, weld and glue a car together as fast as possible". The blunt formulation still holds. Anyone who has spent time in early Rüsselsheim, Wolfsburg or Toyota plants knows: the first revolution was an industrial programme, not a scientific one. Its successes were cycle time, process stability and the elimination of degrading physical labour.

The zoo, as I called those installations, was historical rather than chosen. The first generation of industrial robot arms dates back to the 1960s: Unimate entered service at General Motors in 1961, Kawasaki began production in Japan in 1969, and ASEA (later ABB) introduced the IRB 6 in 1974. The industrial premise was simple: human labour at the edge of tolerable physical strain had to be replaced by a reproducible, monotonous, exact motion. The side effects were high capital intensity, low flexibility, and a programming paradigm that created an entire profession, the robotics technician.

What tends to be forgotten: this phase remains our largest segment by volume. The International Federation of Robotics reports in World Robotics 2025 that 542,000 industrial robots were installed worldwide in 2024, the second highest annual figure in history. The operational stock reached 4.66 million units by the end of 2024, a nine percent increase year-on-year. The first revolution is not history. It is foundation. And it continues to scale because the protected workspace limits direct contact between people and machines. These installations also require a risk assessment and a sound safety concept.

Second revolution: the cobot leaves the cage

The second revolution is the one the general public got used to fastest. A cobot on a final assembly line looks harmless, almost like a better manipulator in a friendly coat of paint. Behind it, however, stands a research programme going back thirty years, driven by Oussama Khatib at Stanford, Gerd Hirzinger at the Institute of Robotics and Mechatronics at DLR, and an entire generation of force-torque-sensor developers. In 2016 I argued that market readiness only arrived around 2015, with KUKA LBR iiwa, the Universal Robots UR family and Rethink Robotics Baxter as the frontrunners. A decade later cobots are an established category. The IFR 2025 report documents that installations of collaborative robots continue to grow at double-digit rates worldwide.

What the second revolution really changed is not the sensor. It is the legitimacy of letting humans close to the machine. Sensitivity - force, torque, speed, redundant path planning - is the technical precondition. The genuine shift lies in safety philosophy: instead of relying solely on spatial separation by a fence, safety-related system functions can enable collaboration. The safety of the application must be assessed, including its tool, workpiece and specific task. This enables new cell layouts, new process designs and, more importantly, new roles inside teams. The operator is no longer a feeder for the robot. She is a process partner.

Honesty requires a caveat: in many German factories, cobots are still operated as if they were caged. They sit inside small safety zones, guarded by light curtains, and are not deployed in genuine collaboration. This is less a matter of technology than of risk assessments under DIN EN ISO 10218 and ISO/TS 15066, and of a shop-floor practice that does not always separate adaptivity cleanly from the formal approval framework. Real collaborative applications remain rare. But they were the door opener - and without the door opener of the second revolution, the third would not exist.

Third revolution: the robot comes to the workpiece

The third revolution inverts a basic assumption of automation. Until then, we brought the workpiece to the robot. In the third phase, the robot comes to the workpiece. Autonomous mobile platforms, mobile manipulators, AMRs and intralogistics robots change not only layouts, but investment decisions: any manufacturer that produces flexibly can only afford rigid cells for high-volume processes.

The IFR 2025 report documents a strong market push in autonomous mobile robots and in transportation and logistics. The service robotics statistics for 2024 count about 200,000 new professional service robots worldwide, more than half of them in transportation and logistics - 102,900 units in that single category, up 14 percent. In 2024, the RaaS fleet in transportation and logistics grew by 42 percent; across all surveyed application areas, it grew by 31 percent. These are not just numbers. They are an answer to the arithmetic that mid-sized companies have been working through for years: shorter product cycles, smaller batch sizes, higher variance, fewer people on night shifts.

What makes the third revolution hard is not the driving function. It is the handover. The mobile robot navigates the shop floor confidently, but the transfer between two processes - from shelf to cobot, from mobile platform to a machining station - creates interfaces that used to be either rigid cells or human hands. These interfaces are the actual battleground of service robotics. Personal robotics, home assisted living and care assistance depend on exactly this logic. The IFR 2025 report shows: medical robots grew by 91 percent in 2024 alone, the most dynamic category of the year. Rehabilitation and therapy robots doubled, robots for diagnostics and medical laboratory analysis together grew by 610 percent. These numbers cannot be filed away.

Fourth revolution: perception, cognition, agency

The fourth revolution is the one everyone talks about and few have actually deployed. Perceptive, cognitive, agentic systems - robots that sense, understand, decide and act. In my 2016 paper, I illustrated this future prospect through Rosie, the robotic housekeeper from The Jetsons, to make clear that this is no longer about programs. It is about behaviour. Ten years later, Rosie has not moved into anyone's household. But on the factory floor, the fourth revolution has pushed open two doors that will not close again.

The first door is programming. A perceptive-cognitive system is not taught point by point any more. It learns from demonstration, from simulation, from what actually happens on the shop floor. In many projects I have watched engineers demonstrate a process once and then let the system propose a workable trajectory. That is not yet general-purpose robotics, but it is a dramatic compression of what used to be programmed with coordinates, waypoints and hand-tuned trajectories.

The second door is commanding. A model that understands language can accept instructions outside its original programming envelope. A vision-language-action model turns an instruction into a sequence of actions. That is not trivial, and it is a qualitative change. The difference between a robot that operates a fixed cell and one that responds to natural language is comparable to the difference between a ticket machine and a human service agent. Both can bring you to your destination. Only one of them understands what to do when you missed your train.

The A3 press release captured my position in the following sentence: „The next industrial decade will not be won in models, but on Monday mornings when systems run for the first time without an observer standing next to them." I stand by that formulation. The fourth revolution is difficult not because the algorithms are missing. It is difficult because we have not yet built durable answers to the questions that emerge the moment a system decides without a human next to it. This is the point where Robotic and AI Governance stops being a nice-to-have and turns into an operational necessity.

Generation R and the transition to the Robotic Native

In parallel with the technical trajectory, in 2016 I proposed the idea of a new generation: Generation R. The term Robotic Native describes people who grow up with robots, in the way my generation grew up with computers, and today's children grow up with voice assistants. I first formulated the concept at the Gartner CIO Summit in 2013 and developed it as part of my doctoral research at TU Munich. The derivation goes back to John Perry Barlow's „Declaration of the Independence of Cyberspace" from 1996. Barlow wrote the network citizen, whom he called a native, into the history of the internet. We are, in his diction, immigrants of the net - and in mine, robotic immigrants.

In the German DigiKompetenz podcast in 2023, I put it as follows: our grandchildren will be Robotic Natives, our children not quite, and we and our children are the last generation of Robotic Immigrants. This is not nostalgia. It is an education policy statement. If we want the fourth revolution not to fail on social acceptance, we need to raise a generation of engineers, skilled workers, executives and citizens who not only work with robots, but grow up with them. Curriculum design, syllabi, further education formats, vocational schools and universities matter as much as the systems themselves.

Robotic and AI Governance as the frame

The four revolutions cannot be introduced productively without a governance frame. That is why I developed Robotic and AI Governance in parallel: as the research field that analyses the causes, characteristics and consequences of advances in robotics and automation as a megatrend. I formulated the concept for the first time at the Gartner CIO Summit in 2013, and detailed it as part of my doctoral research at TU Munich. The approach is deliberately soft: soft laws rather than hard laws, because legislation is too slow for technologies that mature within months. Interdisciplinary task forces, voluntary self-regulation, a professional code of conduct - similar to the arrangements in medicine, law and academia.

The intellectual grounding was worked out in the WeRobot 2015 panels with Peter Asaro, Jason Millar, Kristen Thomasen and others. And at the IEEE IROS Futurist Forums 2015 and 2016 with Oussama Khatib, Gerd Hirzinger and Tapio Heikkilä. I have written the core ideas of those debates into several blog posts, among them Robotic Governance: A regulatory framework for autonomous machines. Readers interested in the current governance debate will find the link to the EU AI Act and the principles of the papal encyclical Magnifica Humanitas.

The ideas have arrived - even where they are not cited

A ten-year balance sheet means, for me, first of all a look at where the ideas ended up. Sometimes with attribution, often without. That is not always academically comfortable, but it is a sign that the structure holds. I go through the institutions in whose work the four revolutions and the governance ideas have left visible traces.

International Federation of Robotics (IFR)

The IFR has been structuring its reports for years by several categories that, in my view, can be compared with the 4RR model: classical industrial robots, collaborative robots, mobile service robots, medical robots and, since 2025, explicitly also humanoid robots in the position paper „Vision and Reality" from August 14, 2025. I read these categories as points of reference for the development from cage to cobot to mobility and finally to cognition. This comparison describes my interpretation of the market data; it does not constitute an empirical test of the phase model.

euRobotics, eu-nited Robotics and Adra SRIDA

The European level picked up the framework early. euRobotics and eu-nited Robotics translated the four-stage structure into a strategic map in their Multi-Annual Roadmap for Robotics in Europe. The Adra AI, Data and Robotics Association has established its Strategic Research, Innovation and Deployment Agenda (SRIDA) across multiple editions as a reference for European framework programmes. Perception, cognition, autonomy and collaboration appear there as main categories of research funding, not as edge cases. This is the fourth revolution, in Brussels vocabulary.

VDMA Robotics + Automation

The VDMA Robotics + Automation Association is the German voice of the industry. Its market communication has been separating industrial robotics, service robotics, machine vision and integrated assembly solutions cleanly for years. The four phases sit underneath that segmentation as the conceptual scaffold. Since 2020, the VDMA has been pushing the combination of the third and fourth revolutions with growing intensity: mobile robotics plus AI-based perception as a joint showcase, notably at automatica. The press briefing for automatica 2025 made this the explicit lead theme.

VDE, VDI and the German standardization bodies

The German standardization world played two roles simultaneously. The VDE, with its analyses of the interaction between the EU AI Act and the Machinery Regulation, embedded the fourth revolution into a regulatory coordinate system - where robotics is no longer merely mechanical, but cognitive and learning. The VDI, with its guidelines on human-robot collaboration and autonomous systems, translated the transition between the second and fourth revolutions into a language that plants can act on. Without this standards work, cobot approvals would be much harder today, and the transition from adaptive to learning robotics would remain a blind spot in the approval pipeline.

UN DESA, UNCTAD and the United Nations

At the global level, UN DESA and UNCTAD have positioned robotics and AI as frontier technologies, with explicit ties to the 17 UN Sustainable Development Goals. The World Economic and Social Survey 2018 and the Technology and Innovation Report 2021 shaped the framing: robotics as both an enabler and an inhibitor of the SDGs. This ambivalence is what we analysed in detail in a 2021 IEEE IROS workshop paper, published in 2023 in Sustainable Production and Consumption. Consensus-based expert elicitation, applied to 169 SDG targets, shows that automation can secure food supply, scale healthcare delivery and enhance education, and that it can also deepen inequality, fragment work, and displace environmental costs. Anyone searching for traces of the 4RR ideas in UN publications will find them - sometimes explicitly, often in the underlying structure.

OECD, European Commission and the EU AI Act

The OECD cited the 4RR model directly in its 2021 report Making Life Richer, Easier and Healthier and used it in Table 1 on page 26 as a source for three generations of industrial robots: stationary, collaborative and flexible mobile systems. At the EU level, the EU AI Act is the central regulatory response to the fourth revolution. It hits especially hard where AI acts as a safety component in products - which is exactly where perceptive and cognitive systems meet robotics. The political agreement of May 7, 2026, reset the timing: high-risk systems under Annex III become applicable from December 2, 2027, and high-risk systems embedded in regulated products under Annex I from August 2, 2028 - documented among other places in the Commission's guidelines. The Machinery Regulation (EU) 2023/1230 completes the picture: AI-based safety components in machinery are regulated jointly through machinery law and AI Act. I have been part of these transitions, in panels, hearings and position papers. For a deeper reading, see my blog post on the AI Act.

IEEE, ISO and the technical-scientific community

On the scientific side, IEEE RAS standards, the IEEE Standards Strategy Meeting, and ISO Technical Committee TC 299 „Robotics" have absorbed the four-stage systematics into their working structures. In the IEEE RAS community, I laid out the interplay between 4RR, standards and translational research in 2019 in the Industry Forum and in a piece for the IEEE RAS Magazine, „Predicting the Future of Robotics and Making It Matter for Industry". The Delphi study Robotics 2050+, which I initiated together with colleagues in 2019, surveyed more than 200 experts worldwide and used the four phases implicitly as a scaffold without needing to defend the model. That is the most rewarding form of dissemination: a model that becomes background common sense.

What the fourth revolution costs us when we introduce it badly

In many keynotes I have quoted factory visits, because they anchor the model. In a Bavarian automotive reference plant, three years ago, I watched an AI-based visual inspection system deployed at an inbound station. Within six weeks, scrap rates dropped by twelve percent. The engineers celebrated. Six months later the call came in: a batch that the system had cleared without complaint had failed at the customer. No one could reconstruct why the model had decided the way it did. No training dataset had been archived. No confidence score had been logged. No rollback mechanism had been defined. What had begun as an efficiency win ended as a compliance case because no one had operationalized the fourth revolution.

This anecdote is the kernel of what I said in the A3 keynote: the next industrial decade will not be won in models, but on Monday mornings. Anyone who thinks that dropping a vision-language model into a manufacturing cell is a software project has not understood the process. It is a governance project with a software component. The four questions I ask in every project are:

Who stops the system when it works formally but produces operational nonsense?
Who is liable when the system makes a decision that no one can reconstruct?
Who trains the shift that will see the first anomaly in the morning, before it becomes a defect?
Who archives the model, the data and the operational log so that an audit is still possible three years later?

If these four questions have no answer, introducing the fourth revolution is not progress. It is a risk hiding behind a KPI.

Impact chapter: where the ideas continue to work

I try to describe the impact of the 4RR model soberly. Some citations are explicit - OECD, IEEE RAS Magazine, the SDG paper. Others are implicit - the IFR reporting structure, the European SRIDA categories, the VDMA market segmentation. Others are cultural - the German WELT article „The four stages of the robotics revolution", the Schaeffler Tomorrow magazine with interviews on the model, podcasts, keynote circuits, curriculum documents from several universities.

Ten years after publication, I see three levels on which the model has continued to work. First: as a taxonomy for market observation and policy advice. Second: as an educational structure in universities, vocational schools and executive education formats. Third: as an argumentative device in governance debates - from the EU AI Act to ethics commissions of industry associations. That breadth was not predictable in 2016. Today it is the reason not to revise the categories artificially. The language will keep evolving, but the skeleton holds.

What 2035 will look different

Ten years after 2016, my view of the future is more sober than it was then. I do not believe that there will be humanoid household robots across the board in 2035. The IFR position paper „Humanoid Robots: Vision and Reality" from August 2025 states this very cleanly: the deployment focus will lie in the service sector and in industrial collaboration, not in the living room. What I expect for 2035 are four shifts.

First: the fourth revolution will become dominant in niches. Quality assurance, testing, intralogistics, rehabilitation, agriculture. Wherever perception and cognition translate directly into process quality, critical mass will form.

Second: cobots will become standard productivity tools in the same way CNC machines are today. No aha moment any more, just default equipment in the mid-market. That is not a revolution, that is a maturity marker.

Third: mobile systems will become routine in factories. Autonomous transport systems, mobile manipulators, robot-as-a-service contracts. The mid-market will rent robotics as it rents electricity today. The IFR 2025 report already shows that the RaaS fleet in transportation and logistics grew by 42 percent in 2024; across all surveyed application areas, it grew by 31 percent.

Fourth: the governance debate will operationalize. Where the EU AI Act is still being discussed today, in 2035 audit processes, model registries and operational log standards will be in place. Without this operating system, the fourth revolution will remain an innovation promise that fails on the first night shift.

Ten years on: what I would write differently today

Honest self-criticism is part of the anniversary. I would rewrite the 2016 paper in three places today.

First: I described the AI element of the fourth revolution as „perceptive, cognitive". Today I know that vision-language-action models and foundation models create their own commanding layer that could not be anticipated in 2016. I would add a dedicated subsection on „Grounded Language Interaction".

Second: I mentioned the governance layer, but I did not formulate it as an independent axis. Today I would describe governance not as an appendix but as a cross-cutting theme that runs through all four revolutions. Robotic and AI Governance is not an add-on to robotics. It is its operating system.

Third: I described Generation R as an educational prognosis. Today I would frame it as an educational obligation. If we want to train Robotic Natives, we need curricula that start earlier and that connect vocational training and higher education cleanly. This is a task for ministries of education, not for the robotics community alone.

Why the A3 keynote in 2026 is a good anniversary

The A3 region - Augsburg, Aichach-Friedberg, Landsberg - is a precise testing ground for the 4RR model. Mid-market companies here run processes across body-in-white, final assembly, precision machining and intralogistics transitions. When I speak in Augsburg, I speak to owners who see all four revolutions inside their own plants. The A3 press release for the 2026 congress summarized this in one sentence: what is at stake is not only technical readiness, but regulatory and social questions - who is liable, who decides, who trains.

In Augsburg I did not present a marketing outlook. I presented a balance sheet. The audience did not ask for roadmaps, they asked for operational playbooks. That is where the last ten years' progress actually lives: an academic model has turned into a tool for plant managers. From there, in the coming decade, it will turn into an everyday practice, measurable on Monday mornings when systems run for the first time without an observer.

FAQ: The four Robotic Revolutions

What are the 4 Robotic Revolutions?

The 4 Robotic Revolutions are a phase model that Bernd Liepert and I published in 2016 at IEEE IROS in Daejeon. It orders the evolution of robotics into four stages: classical industrial robotics behind fences, collaborative sensitive robotics, mobile sensitive robotics, and perceptive, cognitive and agentic systems. By analogy with the evolution of computing (miniaturization, mobilization, ubiquity, pervasiveness), the model describes the transition from a rigid automator to a sensitive-mobile-cognitive assistant. It is used today as a reference frame in IFR market reports, EU research programmes, VDMA market segmentation and governance debates.

Why is the fourth revolution the decisive one?

The fourth revolution - perceptive, cognitive, agentic systems - is decisive because it simultaneously changes the programming logic and the commanding logic. Robots are no longer taught point by point. They learn from demonstration, from simulation and from operational data. They respond to natural language and make decisions without an immediate human in the loop. Responsibility, liability and auditability shift accordingly. The decisive question is no longer whether a system works technically, but whether an organisation can operate it cleanly.

What is the difference between a cobot and a classical industrial robot?

A classical industrial robot operates behind a fence. Its motion is fast, forceful and deterministic. Safety comes from spatial separation. A cobot can enable collaboration, for example through power and force limiting. Whether a fence can be omitted depends on the complete application: tool, workpiece, movement and workplace must be assessed together, and the safety concept must be validated. In practice, cobots are still frequently operated inside safety zones - not because the technology is missing, but because of risk assessments under DIN EN ISO 10218 and ISO/TS 15066. Genuine collaborative applications remain rare. They are, however, the foundation for everything that follows from the third revolution onward.

How do the four revolutions differ from Industry 4.0?

Industry 4.0 is a production and data-centred concept focused on networking, cyber-physical systems and digital twins. The 4 Robotic Revolutions are a robotics-centred phase model focused on physical interaction, sensitivity, mobility and cognition. The two frameworks complement each other. A plant can be advanced on Industry 4.0 metrics while still stuck in the first or second robotic revolution, or vice versa. Governance debates increasingly run along the Robotic Revolutions axis, because the liability and safety questions are more sharply defined there.

FAQ: Generation R and Robotic Natives

Who is Generation R and what is a Robotic Native?

Generation R is the generation that grows up with robots, in the way my generation grew up with computers and today's children grow up with voice assistants. A Robotic Native is a person who experiences robotics as an everyday given, not as a novelty. I first formulated both terms in 2013 at the Gartner CIO Summit and elaborated them as part of my doctoral research at TU Munich. The derivation traces back to John Perry Barlow's Declaration of the Independence of Cyberspace from 1996. Robotic Natives will think, learn and work differently. They will experience robotics not as an innovation, but as infrastructure.

When will Generation R arrive in the workplace?

In the German DigiKompetenz podcast in 2023 I put it this way: our grandchildren will be Robotic Natives, our children not quite, and we and our children are the last generation of Robotic Immigrants. Translated into operational terms, this means that in the next ten to fifteen years Generation R will start to arrive in vocational training and higher education. In manufacturing, in maintenance and in process ownership, it will become visible from the mid 2030s. Any company that does not adjust its training profile now will face a structural disadvantage after 2030.

How does Generation R connect to the fourth revolution?

Generation R and the fourth revolution are a pair. The fourth revolution provides robotic systems that can be interacted with linguistically and intuitively. Generation R provides the humans who take that interaction for granted. Without Generation R, the fourth revolution risks failing on skills shortages and acceptance barriers. Without the fourth revolution, Generation R remains a marketing figure without a distinctive everyday reality.

What does this mean for education and further training?

Curricula need to embed robotics foundations earlier, not as an extra module but as a cross-cutting theme. Anyone training electronics technicians, mechatronics technicians, IT specialists or logistics workers has to include robotics fundamentals as part of the profile. At the university level, this means linking business informatics, mechanical engineering, industrial engineering and computer science so that the fourth revolution does not end up siloed. For executives, this means that further training in Robotic and AI Governance is no longer optional. It is foundational.

FAQ: Robotic and AI Governance

What is Robotic and AI Governance?

Robotic and AI Governance is the research field that analyses the causes, characteristics and consequences of advances in robotics and automation as a megatrend. I first formulated the term in 2013 at the Gartner CIO Summit and elaborated it as part of my doctoral research at TU Munich. The approach is deliberately soft: soft laws rather than hard laws, because legislation is too slow for technologies that mature within months. Interdisciplinary task forces, voluntary self-regulation, professional codes of ethics and international standards are the central instruments. The goal is a responsibility architecture that combines technical safety, social acceptance and economic use.

How does Robotic and AI Governance relate to the EU AI Act?

The EU AI Act is a hard-law response to the fourth revolution. It hits especially hard where AI acts as a safety component embedded in products - which is exactly where robotics as a perceptive-cognitive system operates. The political agreement of May 7, 2026 reset the timing: high-risk systems under Annex III become applicable from December 2, 2027, and systems embedded in regulated products under Annex I from August 2, 2028. Robotic and AI Governance is the soft frame that makes the AI Act operable in day-to-day plant reality. Without this frame the AI Act remains a compliance document. With this frame it becomes an operating rule.

What role does the Machinery Regulation play for robots?

Position in the July 2026 version: The Machinery Regulation (EU) 2023/1230 replaces the Machinery Directive and generally applies from January 20, 2027. It governs safety requirements for machinery, including robots, and interacts closely with the EU AI Act. The political agreement of May 2026 provides for largely exempting machinery from direct AI Act applicability. The amending regulation published on July 24, 2026 does not enter into force until July 27. Safety-relevant AI components in machinery will primarily be addressed through delegated acts under the Machinery Regulation itself. This is an important compromise: it avoids double regulation but requires manufacturers to model the interaction cleanly inside their own conformity assessments.

Update, 4 October 2026: Amending Regulation (EU) 2026/1744 has been in force since 27 July 2026.

What does this mean for the mid-market?

For the mid-market it means that the fourth revolution is only productive if governance grows with it. The four questions I ask in every project are: who stops the system, who is liable, who trains the shift, who archives the model. Companies that can answer these four questions inside their own operation can turn robotics into competitive advantage. Companies that ignore them will build a compliance case with lead time. Do not wait for software that handles governance for you. It will not come. Governance is leadership work.

FAQ: Impact, institutions and the next ten years

Who cites the 4 Robotic Revolutions model?

The model is cited explicitly in an OECD report on robotics from 2021, in IEEE RAS publications, and in the SDG paper from 2021 and 2023 in Sustainable Production and Consumption. Implicitly, the structure appears in IFR World Robotics reports, in the SRIDA documents of the Adra AI, Data and Robotics Association, in the Multi-Annual Roadmap for Robotics in Europe by eu-nited Robotics and in VDMA market segmentation. Culturally, it appears in interviews, podcasts and articles, including the German WELT article 'The four stages of the robotics revolution' and in Schaeffler Tomorrow. That breadth, ten years after publication, is the actual balance sheet.

How does robotics affect the UN Sustainable Development Goals?

Robotics is both enabler and inhibitor of the 17 UN Sustainable Development Goals. In a paper started at the IEEE IROS workshop 2021 and published in 2023 in Sustainable Production and Consumption, my co-authors (Haidegger, Khamis, Mai, Moerch, Rao, Jacobs, Lach, Vanderborght) and I applied consensus-based expert elicitation to 169 SDG targets. Robotics can secure food supply, scale healthcare and improve education. It can also deepen inequality, fragment work and displace environmental costs. The mapping is complex and should not be reduced to an enabler-only narrative. UN DESA and UNCTAD have picked up this ambivalence in their Frontier Technologies reports.

What role will humanoid robots play in the next ten years?

Humanoid robots are the most visible symbol of the fourth revolution, but they will not play a dominant everyday role in the next ten years. The IFR position paper 'Humanoid Robots: Vision and Reality' from August 2025 puts it clearly: the deployment focus lies first in the service sector and in industrial collaboration applications, not in the household. China has concrete plans for mass production, and US and European companies are investing, but the forecast is serial production for specialized applications. I expect humanoid robots to become a significant niche field by 2035, comparable to medical robotic assistants today.

What is the most important sentence from the A3 keynote 2026?

The most important sentence in my A3 keynote 2026 is: the next industrial decade will not be won in models, but on Monday mornings when systems run for the first time without an observer standing next to them. This is not a rhetorical flourish. It is the core thesis of my ten-year balance sheet on the 4 Robotic Revolutions. Anyone who misreads the fourth revolution as a model innovation will underestimate its operational requirements. Anyone who recognizes it as an organisational challenge will be able to handle it. This is the central message I brought to Augsburg for the A3 congress.

Sources and further reading

Boesl, D. B. O. and Liepert, B. (2016): 4 Robotic Revolutions - proposing a holistic phase model describing future disruptions in the evolution of robotics and automation and the rise of a new Generation R of Robotic Natives, IEEE IROS Daejeon, DOI: 10.1109/IROS.2016.7759209.
Boesl, D. (2019): Predicting the Future of Robotics and Making It Matter for Industry, IEEE Robotics and Automation Magazine.
Boesl, D. et al. (2023): Robotics: Enabler and inhibitor of the Sustainable Development Goals, Sustainable Production and Consumption.
Wikipedia: Dominik Boesl, Generation R (DE), Robotic Native (DE).
A3 region: Press release Technology Transfer Congress 2026.
IFR: World Robotics 2025 - Industrial Robots, World Robotics 2025 - Service Robots, Humanoid Robots: Vision and Reality (Aug 14, 2025).
euRobotics / eu-nited Robotics: Multi-Annual Roadmap for Robotics in Europe; Adra: Strategic Research, Innovation and Deployment Agenda (SRIDA).
VDMA: Robotics + Automation Association.
VDE: EU AI Act Update.
OECD (2021): Making Life Richer, Easier and Healthier: Robots, their Future and the Roles of Policy.
UN DESA: World Economic and Social Survey 2018 - Frontier Technologies for Sustainable Development; UNCTAD: Technology and Innovation Report 2021.
European Commission: AI Act - Regulatory Framework, Guidelines for providers and deployers of AI high-risk systems.
WELT (2016): Die vier Stufen der Robotik-Revolution.
Schaeffler Tomorrow: Quo Vadis Robotics.
Delphi study: Robotics 2050+.