<![CDATA[Professor Nils Urbach - News]]>Tue, 03 Mar 2026 08:44:18 +0100Weebly<![CDATA[Veränderungen auf dem Arbeitsmarkt durch KI]]>Mon, 02 Mar 2026 19:47:18 GMThttps://www.nils-urbach.de/news/veranderungen-auf-dem-arbeitsmarkt-durch-kunstliche-intelligenz
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]]><![CDATA[Artificial intelligence and the future of work]]>Thu, 19 Feb 2026 23:00:00 GMThttps://www.nils-urbach.de/news/artificial-intelligence-and-the-future-of-workAt this week's event “AI and the Future of Work – Job Security, Meaning, Participation” at the Evangelische Akademie in Frankfurt, I had the privilege of giving the opening lecture entitled “Artificial Intelligence and the Future of Work.”

In my keynote speech, I began by putting the current dynamics of AI development into context. The systems are rapidly becoming more powerful and are already surpassing human capabilities in certain areas of application. At the same time, their use in companies is increasing significantly: more and more organizations are integrating AI into their processes, and its use in the workplace is growing noticeably.

This reveals a nuanced picture of the effects. At the individual level, AI is increasing employee productivity, in some cases significantly. At the company level, however, these efficiency gains have not yet been reflected across the board in measurable productivity increases. In addition, there are increasing indications of ambivalent effects, such as quality risks or technostress.

At the same time, the increasing use of AI is having a substantial impact on the labor market. Existing job profiles will change fundamentally in some cases, tasks will be redesigned, and requirements will be redefined. It will therefore be crucial to systematically build new skills – from a sound understanding of AI systems and data-based decision-making abilities to critical reflection and control skills in dealing with algorithmic results.
]]><![CDATA[Digital innovation in the public sector]]>Tue, 17 Feb 2026 09:09:18 GMThttps://www.nils-urbach.de/news/digital-innovation-in-the-public-sectorThe public sector faces increasing pressure to drive digital innovation to meet modern societies’ demands. Existing literature calls for systematic approaches to digital innovation in the public sector and a better understanding thereof. However, the public sector still struggles to foster digital innovation and often fails to promote explorative initiatives as a basis for innovation.

In a recently published research study, using a case study of a German consortium project, we investigated how public-private partnerships can promote digital innovation in the public sector. We did so by adopting the resourcing perspective and building on recent conceptual work on network resourcing.

Our findings revealed that the unique characteristics of digital innovation, as opposed to traditional forms of innovation, influence how public-private partnerships can effectively drive digital innovation. We identified decentralized, cross-sector digital innovation clusters as a critical factor for the emergence of digital innovation in public-private partnerships and theorized dissemination practices as an extension to network resourcing in cases of distributed innovation agency within networks of public-private partnerships.

I am happy that our paper “Digital Innovation in the Public Sector: A Resourcing Perspective on How the Public Sector Collaborates with the Private Sector” has been accepted for publication in Information and Organization and is now available online.
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]]><![CDATA[Artificial intelligence and the future of work: Between productivity boost and organizational disillusionment]]>Wed, 04 Feb 2026 09:05:48 GMThttps://www.nils-urbach.de/news/artificial-intelligence-and-the-future-of-work-between-productivity-boost-and-organizational-disillusionment
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Artificial intelligence (AI) has made its way into the everyday work of many organizations. Hardly any other technological development in recent decades has been adopted so quickly and at the same time been the subject of such controversial debate. While proponents expect enormous productivity gains and new forms of value creation, critics warn of job losses, de-skilling, and increasing stress for employees. As is so often the case, the reality lies somewhere in between.

In this article, I classify current empirical findings on the performance and use of AI, analyze its effects on individuals, organizations, and the labor market, and derive implications for companies and employees.
1. AI is rapidly becoming more powerful

The performance of modern AI systems has increased at a remarkable pace in recent years. Current analyses show that the duration of tasks that AI agents can perform autonomously with a 50% probability of success has doubled every seven months for about six years. This means that the limits of what can be automated are constantly shifting upward, especially for knowledge-intensive activities.

AI systems already outperform humans in many text, analysis, and knowledge tasks, often even experts – and at a fraction of the cost. This development largely explains why AI is no longer perceived solely as an assistive technology, but increasingly as an independent “player” in work processes.

2. AI use in companies is increasing significantly

Parallel to its increasing performance, the use of AI in companies is also growing. Current Eurostat data shows that almost all EU member states have recorded an increase in the proportion of companies using AI compared to the previous year. This growth is particularly strong in Northern Europe, for example in Denmark, Finland, and Lithuania.

AI is currently most commonly used to analyze written language, followed by the generation of text, images, audio, or video content. These usage patterns illustrate that generative AI is no longer a niche topic, but is permeating central office, communication, and knowledge processes.

3. From “nice-to-have” to strategic partner in everyday work

With increasing availability, the nature of AI use has also changed qualitatively. Studies show that daily use of AI tools has nearly doubled within a year. At the same time, the proportion of those who consider AI useless has fallen dramatically. More and more employees no longer view AI as just a tool, but as a strategic cooperation partner.

This development can be interpreted as a maturation process: from occasional use to simple assistance functions to collaborative integration into decision-making, analysis, and creative processes. Organizations are thus faced with the challenge of actively shaping this new human-machine interaction instead of leaving it to individual experimentation.

4. Significant efficiency gains – but mainly at the individual level

At the individual level, the effects of AI use are clearly measurable. On average, employees report productivity increases of around one-third and time savings of around 1.3 hours per working day. Managers also say that AI enables them to make more efficient decisions and improve their leadership skills.

What is interesting here is that the picture is nuanced: less experienced or less qualified individuals benefit particularly strongly, as AI can partially compensate for a lack of experience and knowledge. Experienced employees also achieve productivity gains, but only if AI meaningfully complements their existing expertise rather than replacing it. In this case, AI acts as a complement, not a substitute.

5. The productivity paradox at the organizational level

As clear as the effects are at the individual level, the picture at the organizational level is sobering. Despite massive investments, amounting to between $30 billion and $40 billion worldwide, up to 95% of companies report no measurable return on their GenAI initiatives. The results are highly polarized: a few organizations achieve significant effects, while the vast majority hardly benefit at all. This phenomenon is therefore also referred to as the “GenAI divide.”

A key reason for this is that efficiency gains made by individual employees do not automatically translate into organizational value creation. Without adjustments to processes, roles, control mechanisms, and incentive systems, much of the potential is wasted.

6. Side effects: loss of motivation and technostress

In addition to efficiency gains, there is growing evidence of undesirable side effects. Empirical studies show that AI increases productivity, but at the same time can reduce intrinsic motivation. Added to this is a phenomenon that is increasingly being discussed as “workslop”: low-quality AI-generated content that requires additional checking and correction, thus creating new inefficiencies.

More and more employees are also reporting increasing stress. Although simple, repetitive tasks are being automated, the resulting free time is often filled with new demands. The result is not relief, but technostress.

7. Impact on the labor market: change rather than sudden displacement

The question of whether AI will destroy jobs on a massive scale is controversial. However, empirical evidence paints a more nuanced picture. AI is replacing individual tasks rather than entire professions. Nevertheless, job profiles are undergoing profound changes as a result.

Current analyses show that job advertisements for activities with a high AI impact are becoming less common. Entry-level positions are particularly affected, which is already reflected in declining employment figures for young people, for example in the US. In Germany, around a quarter of companies now expect job cuts as a result of AI.

Productivity gains often lead to rationalization measures, especially in economically challenging times. AI thus acts as an amplifier of existing economic dynamics, not as their sole cause.

8. Which skills will count in the future?

Against this backdrop, the question of future skills is becoming increasingly important. Technical knowledge alone will not be enough. Instead, skills that are difficult to automate will be in demand: emotional intelligence, creativity, empathy, critical thinking, and the ability to distinguish relevant from irrelevant information.

Judgment, decision-making ability, and the competence to classify AI results and use them responsibly will become key differentiators in the labor market. Paradoxically, this increases the value of genuinely human skills, especially in an increasingly AI-driven world of work.

9. Conclusion: Design rather than technological optimism

Artificial intelligence is profoundly changing the world of work. It increases individual productivity, is widely used, and is developing rapidly. At the same time, organizational efficiency gains remain the exception, and negative side effects cannot be overlooked.

The decisive factor is therefore not whether AI is used, but how. Companies must understand AI as a socio-technical system that affects organization, culture, leadership, and skills in equal measure. Only then can the potential of AI be leveraged sustainably and responsibly.

References:

https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/

https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2

https://atlassianblog.wpengine.com/wp-content/uploads/2025/09/atlassian-ai-collaboration-report-2025.pdf

https://www.oecd.org/en/blogs/2025/07/unlocking-productivity-with-generative-ai-evidence-from-experimental-studies.html

https://www.heise.de/news/KI-steigert-Produktivitaet-aber-Unternehmen-profitieren-kaum-11067833.html

https://www.artificialintelligence-news.com/wp-content/uploads/2025/08/ai_report_2025.pdf

https://hbr.org/2025/05/research-gen-ai-makes-people-more-productive-and-less-motivated

https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity

https://www.faz.net/pro/digitalwirtschaft/zukunft-der-arbeit/technostress-durch-ki-warum-junge-mitarbeiter-leiden-accg-200362716.html

https://www.spiegel.de/wirtschaft/ki-draengt-auf-den-arbeitsmarkt-die-digitalen-agenten-kommen-a-3f7d2647-7820-4f09-96f4-d82e16daf3ad

https://www.businessinsider.de/wirtschaft/ki-forscher-warnt-80-prozent-arbeitslosigkeit-ist-keine-science-fiction/

https://www.faz.net/pro/digitalwirtschaft/zukunft-der-arbeit/chatgpt-und-ki-weniger-stellenanzeigen-in-deutschland-accg-200455613.html

https://digitaleconomy.stanford.edu/wp-content/uploads/2025/11/CanariesintheCoalMine_Nov25.pdf

https://www.ifo.de/fakten/2025-06-05/ein-viertel-der-unternehmen-rechnet-mit-stellenabbau-durch-kuenstliche

https://www.spiegel.de/wirtschaft/ki-wird-alle-jobs-veraendern-und-das-radikal-a-96c12c01-9a49-4929-aa1f-61d62d34cf6b

https://www.faz.net/pro/digitalwirtschaft/zukunft-der-arbeit/wenn-unternehmen-einstellen-suchen-sie-nach-ki-kompetenz-accg-200362260.html

https://www.faz.net/pro/digitalwirtschaft/zukunft-der-arbeit/chatgpt-und-ki-weniger-stellenanzeigen-in-deutschland-accg-200455613.html

https://www.faz.net/pro/digitalwirtschaft/zukunft-der-arbeit/ki-kompetenzen-worauf-es-im-job-wirklich-ankommt-accg-110768933.html

https://www.spiegel.de/wirtschaft/soziales/david-autor-welche-jobs-sind-auch-mit-ki-noch-sicher-a-0978af3a-3632-4ca8-b9d8-ce828f1a69de
]]><![CDATA[hessian.AI.literacy]]>Tue, 16 Dec 2025 12:41:49 GMThttps://www.nils-urbach.de/news/hessianailiteracyIn the project hessian.AI.literacy, our Research Lab for Digital Innovation & Transformation (ditlab), together with the Research Lab for Law and applied Technologies (ReLLaTe), the Frankfurt University of Applied Sciences, Technische Universität Darmstadt, and hessian.AI, examined what Article 4 of the EU AI Act truly demands in terms of AI literacy – and how companies can operationalize these requirements in practice.

Our interdisciplinary team combined legal analysis with organizational and technological perspectives to clarify what AI literacy means and how firms can build the necessary structures, skills, and governance mechanisms. The result is a concrete, actionable interpretation of AI literacy obligations that can directly support organizations on their compliance and capability-building journey. All core insights, frameworks, and recommendations have been brought together in a comprehensive brochure.
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]]><![CDATA[Best Paper Award Nomination at ICIS 2025]]>Fri, 12 Dec 2025 11:30:13 GMThttps://www.nils-urbach.de/news/best-paper-award-at-icis-2025Our paper „One Solution to fix them all: Does Decentralization fix the Problems of Social Media?” has been nominated for the Best Paper Award at ICIS 2025. Huge thanks and congratulations to my fantastic co-authors Diana Fischer-Preßler and Sara Alida Volkmer – this recognition is well deserved. I’m grateful to have been able to contribute and to be part of such a great collaboration.
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]]><![CDATA[Beyond disintermediation]]>Sun, 23 Nov 2025 13:55:02 GMThttps://www.nils-urbach.de/news/beyond-disintermediationSince the introduction of blockchain technology, both academic and practical discourse has explored its impact on intermediation. Early research emphasized the notion of disintermediation, removing traditional intermediaries through decentralized trust and automated coordination. Yet, more recent studies highlight that blockchain systems often give rise to new forms of intermediation. These roles may not resemble legacy intermediaries but nonetheless fulfil essential functions such as compliance, governance, and technical integration. 

Based on a multiple case study, we investigated how and why such re-intermediation occurs in blockchain ecosystems. Our analysis identifies three recurring drivers, system integrity and resilience, boundary and interface management, and governance efficiency, that structurally necessitate new coordination layers. This study thereby contributes to electronic markets and blockchain literature by offering a refined conceptualization of intermediation dynamics in distributed systems.

I am happy that our paper "Beyond disintermediation: A multiple case study of emerging intermediary roles in blockchain applications" has been accepted for publication in Electronic Markets and is now available online (open access).
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]]><![CDATA[On investments in data centers and digital sovereignty]]>Thu, 13 Nov 2025 13:31:19 GMThttps://www.nils-urbach.de/news/on-investments-in-data-centers-and-digital-sovereigntyGoogle is investing billions in German data Centers – a strong signal for Germany as a business location. But what at first glance looks like a clear locational advantage could, on closer inspection, prove to be a double-edged sword for Europe's digital sovereignty.

On the one hand, such investments secure access to modern digital infrastructure, create jobs, and give the economy an important boost. They stand for progress and dynamism. So, first of all, it's great that Google is investing in Hesse!

On the other hand, with every new data center built by a US corporation, we are also relinquishing another piece of our digital independence. If Europe wants to be a creator rather than just a user in the digital world, we must specifically promote our own investments and strong European alternatives.

I had the opportunity to briefly comment on this topic in my interviews for hessenschau and SAT.1 news.
]]><![CDATA[Managing Artficial Intelligence (preview)]]>Wed, 05 Nov 2025 10:03:20 GMThttps://www.nils-urbach.de/news/previewArtificial intelligence (AI) is reshaping the way organizations operate, make decisions, and create value. As AI systems become increasingly embedded in business processes, the challenge lies not only in understanding the technology but in managing it effectively. This book provides a comprehensive and structured overview of the principles, strategies, and practices required to integrate AI into modern organizations.

It spans the full AI lifecycle, from foundational concepts and learning methods to the identification of use cases, the implementation of AI strategies and governance mechanisms, as well as the design and development of AI applications. It examines how to design meaningful human-AI interactions, navigate workforce transformation, and operate AI systems at scale. Ethical, legal, and social dimensions are addressed to ensure that AI adoption aligns with values such as transparency, fairness, and accountability.

The book is written for decision-makers, professionals, and students who are not only curious about AI – but who want to actively shape its role in organizations. Whether you’re leading AI initiatives or preparing for the future of work, it provides essential guidance for leveraging AI in a strategic and impactful way. After all, AI hasn’t (yet) figured out how to manage itself.
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]]><![CDATA[Identifying artificial intelligence use cases]]>Tue, 04 Nov 2025 15:49:17 GMThttps://www.nils-urbach.de/news/identifying-artificial-intelligence-use-casesWhile artificial intelligence (AI) technologies offer much potential for a wide range of AI uses in organizations, identifying use cases involves making many decisions amidst influencing complications. While nascent methods for identifying such AI use cases seem promising, we know very little about their proven efficacy to actually guide decisions during the use case identification process.

To investigate this efficacy, we conducted action design research at EnBW, one of Europe’s largest energy suppliers. We draw on interviews and an intervention at EnBW’s practices to develop a method, derive six design principles, and describe factors that increase our method’s efficacy. We therefore contribute a novel theoretical perspective on decision-making in contexts of organized anarchy, explaining how organizations can navigate the complexities of decisions in AI use case identification.

I am happy that our paper “Identifying Artificial Intelligence Use Cases: Toward a Method that Facilitates Garbage Can Innovation Processes” has been accepted for publication in Information & Management and is now available online (open access).
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