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