<![CDATA[Professor Nils Urbach - News]]>Tue, 09 Jun 2026 13:15:16 +0200Weebly<![CDATA[Surveillance state, state crypto, and the end of cash? A more nuanced look at the digital euro]]>Fri, 05 Jun 2026 11:55:24 GMThttp://www.nils-urbach.de/news/surveillance-state-state-crypto-and-the-end-of-cash-a-more-nuanced-look-at-the-digital-euroThe public debate on the digital euro is often shaped by striking narratives: Will it abolish cash? Will it become an instrument of state surveillance? Will the European Central Bank turn into a competitor of commercial banks? Or is the digital euro simply a form of “state crypto”?

In our eFin-Blog article, Valeryia Arnold and I argue that such questions are understandable, but often too simplistic. They tend to turn theoretical possibilities into seemingly inevitable outcomes. A more productive discussion, we suggest, should focus on the concrete design choices currently being considered for the digital euro.

To structure this discussion, we use an architectural perspective on central bank digital currencies. We distinguish four key layers: access, control, business model, and data. Each layer reveals different design options and helps separate realistic developments from speculative scenarios.

On the access layer, the digital euro appears to be designed not as a replacement for cash, but as an additional payment option. Access could be provided through apps, web interfaces, cards, QR codes, or contactless payments. Importantly, the current direction points toward integration into existing payment habits rather than forcing entirely new user behavior.

On the control layer, our article addresses the widespread concern that the digital euro could enable comprehensive state surveillance. We show that the actual design space is more differentiated. Identification and regulatory checks would likely remain with banks and payment service providers, while privacy-enhancing mechanisms such as the separation of identity and transaction data, encryption, and offline payments for smaller amounts are being discussed.

On the business model layer, we make clear that the European Central Bank is not expected to take over the customer-facing role of commercial banks. Rather, the emerging model points to a division of labor: the ECB would provide the basic infrastructure, while banks and payment service providers would continue to handle customer interaction, identification, compliance, and parts of payment processing. Holding limits could also reduce the risk of large-scale shifts of deposits from commercial banks to digital euros.

On the data layer, we highlight the importance of technological choices such as account-based versus token-based models, offline functionality, interoperability, and standardization. The current direction seems to be hybrid: larger online payments would remain close to existing account-based structures, while token-based approaches may become relevant for offline payments that resemble some characteristics of cash.

The central message of our article is that the digital euro should neither be dismissed as a surveillance tool nor celebrated as a purely technological innovation without risks. Its societal implications depend on concrete architectural and regulatory decisions. The digital euro currently appears less like a radical break with the existing financial system and more like an attempt to extend Europe’s payment infrastructure with additional digital, interoperable, and potentially more privacy-sensitive capabilities.

The full article is available here (in German): https://zevedi.de/efinblog-ueberwachungsstaat-staats-krypto-bargeldende-diskussion-um-den-digitalen-euro-und-gestaltungsoptionen/
Picture
]]><![CDATA[Balancing exploration and exploitation]]>Tue, 21 Apr 2026 08:26:20 GMThttp://www.nils-urbach.de/news/balancing-exploration-and-exploitationIn an era of rapid technological disruption, organizations must increasingly balance the exploitation of existing capabilities with the exploration of innovative business models and technologies, a duality known as ambidexterity. While prior research has largely focused on ambidexterity at the organizational and individual levels, its operationalization at the team level, particularly within project contexts, remains underexplored. 

Our recently published study addresses this gap by designing and evaluating a decision-support tool that enables structured, ambidexterity-informed staffing of project teams. The tool supports the scoping of business transformation projects, assesses the required balance between exploration and exploitation across different phases, and recommends team compositions aligned with these needs. Following a Design Science Research approach, the artifact was iteratively developed and refined through expert interviews, practitioner workshops, and a scholarly focus group. Additionally, we derive three design principles to guide the effective staffing of ambidextrous project teams and to support the development of information systems in ambidextrous contexts. 

Our study advances ambidexterity research by identifying micro-mechanisms and phase-contingent requirements, linking these to concrete skill profiles through validated measurement, and demonstrating how organizations can embed evidence-based ambidextrous staffing into existing processes.

I am happy that our paper "Balancing Exploration and Exploitation—Designing a Staffing Tool to Leverage Ambidextrous Team Compositionshas" has been accepted for publication in the Schmalenbach Journal of Business Research and is now availabe online (open access).
Picture
]]><![CDATA[Designing a blockchain-based information system for procurement processes]]>Mon, 13 Apr 2026 17:32:07 GMThttp://www.nils-urbach.de/news/designing-a-blockchain-based-information-system-for-procurement-processesThe inter-organizational processes in procurement remain burdened by media discontinuity, inefficiencies, and a lack of trust among trading partners. Blockchain-based information systems are frequently proposed as a remedy because they enable shared, tamper-evident records. However, existing instantiations rarely scale beyond small consortia because they fail to address the extended blockchain trilemma, which requires simultaneously achieving decentralization, scalability, security, and strict privacy requirements for sensitive commercial information.

In contrast to prior blockchain procurement prototypes that manage the extended blockchain trilemma primarily through permissioned architectures, our recent study investigates how a blockchain-based information system can be designed to reconcile the trade-offs inherent in the extended trilemma, achieving a viable balance through architectural allocation and cryptographic enforcement. Following the design-science research paradigm, an empirically validated problem statement is synthesized from a structured literature review and expert interviews. Five design objectives are derived, evaluated, and used to guide a prototype design, which is then iteratively refined and evaluated through quantitative and formative assessments and sixteen semi-structured expert interviews.

Reflection on the build–evaluate cycles yields two design principles: (1) Balancing decentralization, scalability, and security by using a public chain as a trust anchor, a Layer 2 for scaling, and decentralized communication between layers. (2) Maintaining that balance when privacy is required by integrating efficient, resilient cryptography and minimizing control points. These principles extend existing procurement research, linking business requirements to infrastructural choices, providing a transferable foundation for scholars and practitioners aiming to deploy secure, scalable, and privacy-preserving blockchain solutions in inter-organizational contexts.

I am happy that our paper "Designing a blockchain-based information system for procurement processes—Balancing decentralization, scalability, and security while maintaining privacy" has been accepted for publication in Information Systems and is now availabe online (open access).
Picture
]]><![CDATA[Coding scales – Thinking remains limited]]>Tue, 31 Mar 2026 16:28:51 GMThttp://www.nils-urbach.de/news/coding-skaliert-denken-bleibt-knapp
Picture
AI coding is not just a matter of tools – it is a strategic leadership task
Those who establish a structured, company-specific approach early on will gain sustainable competitive advantages – while those who experiment in a piecemeal fashion or simply copy others risk wasting time, causing organizational friction, and creating inefficiency.
Generative and agent-based AI are fundamentally transforming software development
What begins today as AI coding and vibe coding is evolving into a new model of digital innovation: AI-centric software development. Large language models and development agents can already accelerate or partially automate many software development tasks – from writing code and testing to documentation and refactoring. This drastically reduces implementation costs and time. We see the results in technology and tool development, in software and innovation processes, in the job market, and in the sometimes comparatively low valuations of software stocks.

From coding to AI-centric innovation: A structural shift in software development
We are developing and using new software development tools, but there is much more at stake – the structure of digital innovation is changing. Since implementation scales better through automation, it is no longer a central bottleneck. The focus is shifting from implementation to design: from coding to problem understanding, system design, architecture, and the orchestration of intelligent product and process development. Software development is thus evolving from an implementation-driven discipline into an AI-centered innovation system. Within this system, new bottlenecks, new roles, new processes, and new governance requirements are emerging.

AI-centered innovation: Hypotheses, open questions, and an invitation to dialogue
This discussion paper does not present established scientific findings, but rather questions and hypotheses. It reflects our current observations and thoughts as inspiration for others and serves as an invitation to dialogue. The field is evolving so rapidly that it is not feasible to outline a detailed vision for AI-centered innovation and software development over the next few years and then systematically implement it. On the other hand, one cannot ignore the new possibilities and opportunities without falling behind the competition. Companies should not view generative and agent-based AI merely as productivity tools for coding, but should instead align their innovation and development organizations early on with an AI-centered model of software development – with a stronger focus on problem understanding, system design, and governance – while learning and experimenting iteratively to quickly capitalize on opportunities without rigid roadmaps.
]]><![CDATA[Veränderungen auf dem Arbeitsmarkt durch KI]]>Mon, 02 Mar 2026 19:47:18 GMThttp://www.nils-urbach.de/news/veranderungen-auf-dem-arbeitsmarkt-durch-kunstliche-intelligenz
Picture
]]><![CDATA[Artificial intelligence and the future of work]]>Thu, 19 Feb 2026 23:00:00 GMThttp://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 GMThttp://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.
Picture
]]><![CDATA[Artificial intelligence and the future of work: Between productivity boost and organizational disillusionment]]>Wed, 04 Feb 2026 09:05:48 GMThttp://www.nils-urbach.de/news/artificial-intelligence-and-the-future-of-work-between-productivity-boost-and-organizational-disillusionment
Picture
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 GMThttp://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.
Picture
]]><![CDATA[Best Paper Award Nomination at ICIS 2025]]>Fri, 12 Dec 2025 11:30:13 GMThttp://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.
Picture
]]>