Managing Around

Let me ask you something. When was the last time you actually knew where your knowledge is stored? Not a file. Not a saved email. Not a bookmark you'll never open again. I mean a living sense of what you know, how it connects, and why it matters — right now, in your professional life. For most of us working in management, in social work, in education — the answer is: we don't. We generate ideas in meetings, publish papers, annotate reports, we paste things into apps we'll forget we have. And then we wonder why it feels like we're starting from scratch every Monday again. In this episode, I want to talk about a practice called Digital Gardening — and why I think it might be one of the most underrated approaches in personal knowledge management and higher education alike. Not because it's new. And not because it's technological. But because it forces you to do something that good management has always required: to take responsibility for your own thinking.

Uncover even more insights and valuable information by visiting the blog profmanagement.de. Thank you for tuning in! If you enjoyed this episode, we'd be thrilled if you could leave us a glowing review on Apple Podcasts. Got a thought or opinion about this episode? Have a suggestion for a future topic? Send an audio file or voice note to [email protected]. For all other comments, send us a tweet or DM at @profmanagement on Twitter or Instagram.

Imagine you are developing an idea for new research. You find a mountain of journal articles that seems perfect, but how can we process the vast amount of information, and how can we use it for our own research? Or imagine you need to synthesise 5 studies on “remote work productivity” for your next class on organisation research by tomorrow. In the past, this was a manual grind and involved a great deal of labour. While producing high-quality research remains a rigorous human endeavour, AI-powered literature review apps now allow you to synthesise insights more efficiently by having a conversation directly with the literature. We often talk about the “hype” versus the “real promise” of AI in management and social science. But how do we actually manage this? Today, I want to take you through three research engines that I’ve been exploring and using for quite some time: Elicit.com, Scite.ai, and Consensus. First, we will examine their specific functionalities—including recent advancements in agentic workflows and structured data extraction—to identify the unique purposes and benefits each tool offers to the researcher.

Uncover even more insights and valuable information by visiting the blog profmanagement.de. Thank you for tuning in! If you enjoyed this episode, we'd be thrilled if you could leave us a glowing review on Apple Podcasts. Got a thought or opinion about this episode? Have a suggestion for a future topic? Send an audio file or voice note to [email protected]. For all other comments, send us a tweet or DM at @profmanagement on Twitter or Instagram.

You’re sitting at your desk, staring at a mountain of interview transcripts. Hours upon hours of human stories. Contradictions. Emotions. Moments of confusion. Moments of clarity. Everything that makes qualitative data so beautifully complex… and so incredibly time-consuming. You stretch your shoulders. You breathe in. Likewise, you prepare for the long, slow, careful journey ahead. And then — someone else pulls up a chair. Someone who reads your transcripts in seconds. Someone who instantly suggests themes, highlights patterns, and summarizes ten interviews faster than you can sip your espresso. But this “someone”… doesn’t understand any of it. They don’t grasp cultural nuance. They don’t know your theory. Furthermore, they certainly don’t know your participants. This “someone” is AI. And suddenly the big questions appear: What does it mean to bring AI into the deeply interpretive world of qualitative research? What do we gain? What do we risk? And how does it change the way we, as researchers, make sense of the world? That’s where we’re going today. We’re talking about something that feels both exciting and a little unsettling: What happens when artificial intelligence joins us in qualitative research? 

Uncover even more insights and valuable information by visiting the blog profmanagement.de. Thank you for tuning in! If you enjoyed this episode, we'd be thrilled if you could leave us a glowing review on Apple Podcasts. Got a thought or opinion about this episode? Have a suggestion for a future topic? Send an audio file or voice note to [email protected]. For all other comments, send us a tweet or DM at @profmanagement on Twitter or Instagram.

Picture this: It's 2 AM, and Professor Maik is sitting in his office, surrounded by stacks of research papers, sticky notes covering his monitor, and notebooks filled with half-finished ideas. He's preparing for tomorrow's lecture but can't find that brilliant insight about team dynamics. It's somewhere in his notes, but where? Sounds familiar? This scenario plays out in workspaces worldwide every day. We're drowning in information, yet starving for knowledge. But what if the solution isn't just better filing systems or productivity apps? What if it lies in understanding how we can become better knowledge managers ourselves? In this episode, we're exploring how teachers have become the unsung heroes of knowledge management, and what the rest of us can learn from their approach. Teachers don't just deliver information – they're sophisticated knowledge managers who create, organize, share, and evolve knowledge daily. The strategies they use can revolutionize how any of us handles information professionally.

References:

Mayowa-Adebara, O., & Enakrire, R. T. (2024). Determinant of Knowledge Sharing among Lecturers in Higher Education Institutions in Nigeria. European Conference on Knowledge Management, 25(1), Article 1. https://doi.org/10.34190/eckm.25.1.2769

Quarchioni, S., Paternostro, S., & Trovarelli, F. (2020). Knowledge management in higher education: A literature review and further research avenues. Knowledge Management Research & Practice, 20(2), 304–319. https://doi.org/10.1080/14778238.2020.1730717

Uncover even more insights and valuable information by visiting the blog profmanagement.de. Thank you for tuning in! If you enjoyed this episode, we'd be thrilled if you could leave us a glowing review on Apple Podcasts. Got a thought or opinion about this episode? Have a suggestion for a future topic? Send an audio file or voice note to [email protected]. For all other comments, send us a tweet or DM at @profmanagement on Twitter or Instagram.

Welcome back, dear listeners, to another fascinating dive into the ever-changing management world around us! Today, we're tackling a topic that's often shrouded in mystery, sometimes fear, but also immense potential: Artificial Intelligence, or Generative AI, and its surprising, often subtle, year powerful role in the field of Social Work. It is no longer a question of whether its inclusion in social work organisations is necessary; the focus is now on where and how it should be implemented. I'd like to sincerely thank TH Nuremberg for generously developing and sharing the e-learning course on AI in Social Work, freely available to all of us.

Reference: 
Lehmann, R. (2025). KI und Soziale Arbeit. [E-Learning]. TH Nürnberghttps://open.vhb.org/blocks/occoursemetaselect/detailpage.php?id=236

Uncover even more insights and valuable information by visiting the blog profmanagement.de. Thank you for tuning in! If you enjoyed this episode, we'd be thrilled if you could leave us a glowing review on Apple Podcasts. Got a thought or opinion about this episode? Have a suggestion for a future topic? Send an audio file or voice note to [email protected]. For all other comments, send us a tweet or DM at @profmanagement on Twitter or Instagram.