At 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.

The 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.

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.

In 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.

Our 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.

Since 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).

Google 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.

Artificial 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.

While 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).