Avsnitt
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🎙️ Dr. Charlotte Blum - Director Change & Organisational Design, edding Group
Two fears run underneath every AI rollout, and most companies manage neither. Dr. Charlotte Blum works the human emotions of AI transformation - the fear, identity, and trust that decide whether adoption sticks - from a board-level role most organisations don't have. This is a conversation about why people resist AI, and why the technology itself may be the hardest thing in the room to trust.
📌 The episode
Charlotte Blum was hired after an agility transformation failed, into a staff function reporting to the board, to do the thing change management usually skips: work with what people feel. In this conversation she separates two fears companies tend to blur - fear of the technology and fear of change itself - and argues that AI destabilises people at the level of identity, not workflow. She makes a case that AI is structurally untrustworthy for an unexpected reason: it has no self-interest. A system built only to please will give you the answer it thinks you want, not the true one. Asked whether she has seen an organisation genuinely succeed at AI transformation, her answer is short, and it isn't yes. For any leader who suspects the resistance in their team is emotional rather than logistical, this is a conversation about what the roadmap leaves out.
🧠 What you'll sit with
Why the fear of AI arrives before the technology does, and why it's measurableWhy an organisation that can't move someone's desk won't move them to AIWhat happens to a person when their role, and their sense of self-worth, is suddenly in questionWhy a tool built to please can't be relied on to tell you the truthWhy the thing to optimise for is trust, not results, not time👤 About the guest
Dr. Charlotte Blum is Director Change & Organisational Design at the edding Group, where she leads AI and organisational transformation from a rare board-facing position that treats the emotional side of change as core work rather than an afterthought. Her method is to enable leaders for the conversations no job description trained them for, and she has the internal data suggesting it moves the numbers.
⏱️ Chapters
[00:00] Trust too much, or not at all - the problem with both
[02:16] Why AI is a human shift, not a technology one
[07:12] The skill capitalism doesn't want you to build
[13:48] Why leaders are handed a job they were never trained for
[30:22] The trust equation, and where AI breaks it
[38:11] Three things an organisation must get right🔗 Links
Dr. Charlotte Blum on LinkedIn - https://www.linkedin.com/in/dr-charlotte-blum/
edding Group - https://www.edding.com
The trust equation (credibility + reliability + intimacy / self-orientation) - https://people-shift.com/articles/the-trust-equation/
AllBright Academy - https://www.allbright-stiftung.de/academy
Related episode - Why Empathy Can't be Automated with Gifty Enright - https://open.spotify.com/episode/5NYYRgwROaiZLHur5gbksw -
🎙️ Sarah Marie Sandmann, Innovation & Intrapreneurship, Bundeswehr Cyber Innovation Hub
AI trust in defence starts where slides end: with a soldier under pressure who needs to understand, rely on, and account for the technology in their hands. Sarah Marie Sandmann works at the Bundeswehr Cyber Innovation Hub in Innovation and Intrapreneurship, the official innovation unit of the German armed forces. Sandmann treats trust in defence technology as a capability criterion, something tested under pressure, not asserted in policy, and the organisations getting this right are rebuilding how innovation works within the institution itself.
📖 Episode overview
The Bundeswehr is one of Europe's most structurally complex organisations, built for stability, accountability, and risk minimisation, not speed. Sandmann and her colleagues run innovation projects at 12-month cycles that would take years through standard procurement. This episode explores what that tension looks like in practice: how AI is deployed strictly as decision support rather than decision replacement, how soldiers co-develop the technologies they will eventually trust with their lives, and why a trustworthy defence innovation ecosystem would be measured by capabilities delivered rather than the quality of its presentations. Sandmann also reflects on the post-Ukraine shift she has observed from inside the institution — more civilians wanting to contribute, more startups engaging with defence, and what that change means for civil-military trust.
🔍 Key themes
Whether a soldier can understand, rely on, and explain an AI system, and why all three must be true before deploymentThe structural case for why large institutions are slow to innovate, and why the people inside them usually aren't the problemWhat "decision support, not decision replacement" means as a live design constraint for AI in high-stakes environmentsHow trust between military institutions and the startup ecosystem is actually built, and what breaks itWhat a trustworthy defence innovation ecosystem would need to look like in two to three years👤 About the guest
Sarah Marie Sandmann works at the Bundeswehr Cyber Innovation Hub in Innovation and Intrapreneurship, the official innovation unit of the German armed forces. She works at the intersection of military capability development, startup collaboration, and responsible technology adoption, collaborating on projects that bring AI, autonomous systems, and emerging technologies into operational use through direct co-development with soldiers. She has been inside the institution through the post-Ukraine shift in civil-military engagement and speaks from that experience with unusual clarity.
⏱ Chapter markers
[00:00] What the Cyber Innovation Hub actually does — and why cockroaches are involved[03:18] Trust as an operational requirement in defence technology[08:00] Why innovation resistance is structural, not cultural[13:09] AI as decision support — the bright line and how it holds[21:22] The post-Ukraine shift and what a trustworthy ecosystem would look like🔗 Links
Sarah Marie Sandmann on LinkedIn — https://www.linkedin.com/in/sarah-sandmann/Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/Bundeswehr Cyber Innovation Hub — https://www.cyberinnovationhub.de/en/SwarmBioTactics and Autobugs project — https://www.youtube.com/watch?v=k4vu5AKTkJkKomand.AI and Smart Lead project — https://www.youtube.com/watch?v=-r45um6txpQSonic AI - https://www.youtube.com/watch?v=c9i98jrielwRelated Episode: Why Security Intelligence Fails Before the Attack with Assaf Kipnies - https://open.spotify.com/episode/2D4ODAxGULFbqmXCmgwsfA -
Saknas det avsnitt?
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🎙️ solo episode with host Eva Simone Lihotzky
Anthropic's frontier AI model was pulled offline for every non-American in three days, and suddenly Europe's AI access looked less like something it owns and more like a permission. This is a week where digital trust stopped being abstract: one US export directive, one warning about Europe's compute future, and one lunch table where the people who build AI sat with the people who govern it. For any leader applying AI inside an organisation, it is a week worth understanding in practice, not as headlines.
🧭 In this episode
In a single week of June 2026, three events landed that most coverage treated as separate. Eva reads them as one thread. The US Commerce Department forced Anthropic's Fable and Mythos models offline for any foreign national. The Europe 2031 agenda argued that Europe's window to matter in AI is closing faster than its own authors had predicted. And for the first time, Frontier AI lab CEOs sat at the G7 heads-of-state table. The question underneath all three: when access to the most strategic technology of the moment sits on someone else's permission, what does a European organisation actually own? Eva works through what this means for vendor dependency, infrastructure design, and the difference between treating AI sovereignty as a compute problem and treating it as a trust problem.
🔍 Key themes
Why "access" to a frontier AI model may be a permission that someone else can withdraw — and what that does to a strategy built on itThe gap between Europe's AI story as a capital problem and the trust assumption sitting underneath itWhat changes for a leader when vendor lock-in stops being a risk slide and becomes a live eventWhether building infrastructure and orchestration across many models is now resilience rather than over-engineeringWhen the builders of AI also shape the rules that govern it, who represents the people using it🎙️ About the host
Eva Simone Lihotzky, AI adoption and ethics advisor, formerly MD in one of the largest independent agency groups in Europe and co-author of 10 Moral Questions: How to Design Tech & AI Responsibly. She has spent more than a decade leading AI implementation inside organisations, which is why this episode resists the easy reads — it stays with the gray zone between hypocrisy and conviction, between capital and trust, rather than resolving it. This is a solo reflection: Eva connecting three news events into one question she openly admits is hard to narrow down.
⏱️ Chapters
[00:00] Three news items, one thread[02:22] A frontier model offline in three days[08:00] Europe 2031: the window that closed early[11:30] Mistral, and the scale of the gap[18:45] The G7 table: builders meet the people who govern them[25:10] Who represents the ones using the technology🔗 Links
Eva Simone Lihotzky on LinkedIn — https://www.linkedin.com/in/evalihotzky/Europe 2031 agenda — https://europe2031.ai10 Moral Questions: How to Design Tech & AI Responsibly — https://www.10moralquestions.com/the-bookEva's World Economic Forum reflection, January 2026 — The politics of tech on Spotify: https://open.spotify.com/episode/1RKtxdJWXcQH8vnpnDtgEP?si=wrln7peeSkKb-gotGHYMRgEva's World Economic Forum reflection, January 2026 — The politics of tech on Apple Podcasts: https://podcasts.apple.com/de/podcast/the-in-between-tech-and-trust-podcast/id1828521905?l=en-GB&i=1000747143762Anthropic statement on the Fable / Mythos suspension — https://www.anthropic.com/news/fable-mythos-access -
🎙️ Tobias Burkhardt, Founder of The Shift School
AI, trust and learning are on a collision course, and the casualty is judgment. Tobias Burkhardt, founder of the Shiftschool, argues that the way individuals and organisations are adopting AI in learning is a cultural problem: the reflex to make learning faster and cheaper is precisely what makes AI dangerous to the people using it. This conversation is for anyone who suspects the upskilling programmes around them are solving for the wrong problem.
💡 Episode overview
Tobias Burkhardt has spent years advising organisations on learning and organizational development, and his diagnosis is uncomfortable: cognitive atrophy is real, it is already happening, and it predates AI. The impulse to shortcut understanding — to reach for the tool before doing the thinking — is a cultural pattern that AI accelerates but did not create. In this conversation, he makes the case for treating AI as a relational technology rather than a productivity instrument, and for rebuilding learning around curation, community, and continuity rather than content delivery. He also names something most learning institutions will not: that the ultimate goal of good education is to make oneself obsolete.
🔑 Key themes
Why treating AI as a tool rather than a collaborator is ill-advised, and what the alternative requiresThe faster-and-cheaper reflex in organisational learning, and why it compounds the problem it is meant to solveWhat a school without content actually means, and what takes content's placeThe bilateral responsibility in learning, and why self-discipline alone will never be sufficientTrust as an investment: why waiting for certainty before engaging with AI is the wrong posture🎤 About the guest
Tobias Burkhardt is the founder of The Shiftschool, a learning institution he built because he loved learning and never liked schools. He advises organisations on learning strategies and has developed a philosophy of education built around what does not change — judgment, curation, social interaction, and continuity — rather than around the tools and content that do. His concept of a school without content is a practical response to the decreasing half-life of knowledge in an AI-native world.
⏱ Chapter markers
[00:00] Can we trust ourselves to use AI — not just trust AI itself[04:00] Why the information abundance problem predates AI[08:30] From tool to collaborator to environment — how the relationship with AI evolves[11:00] Cognitive atrophy and the shortcutting reflex[18:30] Lifelong learning as personal obligation — and why institutions cannot wait[22:30] The school without content — what takes knowledge's place[30:00] Redesigning Shift School for an AI-native world🔗 Links
Tobias Burkhardt on LinkedIn: https://www.linkedin.com/in/meetropoly/ Eva Lihotzky on LinkedIn: https://www.linkedin.com/in/evalihotzky/Visit the Shift School: https://shiftschool.deListen to the related episode with Simon Berkler on organisational AI adoption or trust in digital systems (EP 22): https://open.spotify.com/episode/6y8PMaVUnZVAR1hOAR15DN -
🎙️ with Magnus Strobel, Co-Founder and CEO of Nexus Politics
Trust in politics has been eroding across Western democracies for over a decade, and Magnus Strobel thinks the failure is in how democracy works, in the process that has stopped feeling participatory. His company, Nexus Politics, is a for-profit platform built to map the distance between what citizens actually think and what politicians actually do - and to make that distance impossible to ignore.
🔍 Episode overview
This is a conversation about whether transparency can rebuild participation once the machinery of democracy has stopped feeling participatory. It is also about a quieter problem: how a founder building a trust instrument decides whether anyone actually trusts it.
Magnus Strobel and his team create an architecture for a digital democracy platform: how citizen opinion gets routed to the right political actors, how the system maps public sentiment in real time, and where accountability is supposed to live. The harder questions arrive underneath: Why build this as for-profit rather than not-for-profit, and why that choice is the one that makes political neutrality credible. What politicians say they want from such a tool, and why their enthusiasm might mean less compared to how they use it specifically. It is a founder's conversation that keeps circling back to a single uncertainty: you can build the mechanism for trust, but you cannot yet prove the trust is there.
⚖️ Key themes
Why the crisis is in how democracy functions, not in democracy itself - and what that distinction changes How a for-profit structure becomes the argument for political neutralityMapping the gap between what voters think and what politicians do What politicians actually want from civic tech, and why positive feedback is the hardest signal to trustTech as a tool that can repair democratic trust or deepen the damage, depending on who uses it and how🤝 About the guest
Magnus Strobel is co-founder of Nexus Politics, a digital democracy platform built to rebuild participation and accountability in representative democracies. His background is in behavioral economics, which surfaces throughout the conversation in his attention to the gap between what a system is designed to do and what people actually do with it. He builds from Munich, embedded in the local startup ecosystem, with a stated ambition modelled partly on Taiwan's experience of using participation tools to lift satisfaction with democracy.
🌍 Chapter markers
[00:09] What comes to mind when a democracy founder thinks about trust[02:59] Opening the fragmented machinery of politics - participation, transparency, accountability[05:59] Why for-profit is the route to credible neutrality[16:08] The hardest part is always reality - and what politicians really want[22:49] Can tech rebuild democratic trust, or does it cut both ways[35:48] In-between moments: trust, division, and where a founder sits right now⛓️💥 Links
Nexus Politics: www.nexuspolitics.orgMagnus Strobel LinkedIn: https://www.linkedin.com/in/strobelmagnus/ Audrey Tang / Taiwan digital democracy: https://www.demnext.org/people/audrey-tangRebuild conference, Copenhagen: https://www.rebuild.net Related episode - Rebuilding Trust: Tech, Politics and Entrepreneurial Leadership (EP 06) -
Europe and China are on different AI paths at different speeds. Vincent Xiang has spent years inside that corridor: He has been working as a translator between Chinese AI founders and European investors and corporates, and this conversation dives into his experiences, conversations, and operations on the ground and in-between.
🧭 Episode overview
European executives are excited about Chinese AI momentum. But they're also stuck before they act. Chinese founders interpret some of Europe's regulations as inefficiency. Both sides are operating with simplified labels that are accurate enough to feel right and wrong enough to produce bad decisions. Vincent walks through what he actually sees on the ground - why trust in China gets delegated to systems rather than built between strangers, why "AI superpower" and "surveillance dystopia" both miss the territory, why fragmentation is now treated as permanent reality by founders, and what European companies serious about engaging China should do before they book a single meeting.
🔍 Key themes discussed
The different first questions Europe and China ask about new technology, and what each one produces downstreamTrust as delegated infrastructure - the Alipay escrow story and why people trust the system rather than the strangers in itWhy both Western labels for Chinese AI are wrong in the same direction, and what gets missed when leaders operate with themThe three-layer coordination of government, platforms, and institutions in China, and what its absence looks like in EuropeFragmentation as the new permanent reality, and why compliance has to be built in as a product feature from day one👤 About the guest
Vincent Xiang is the founder of China AI Connect, a research and advisory practice helping European investors and corporates evaluate whether Chinese AI is relevant to their strategy, and helping Chinese founders understand the European market. He lived in Germany for seven years, writes the China AI Connect briefings on Chinese AI and deep-tech policy and players, and organises executive trips that bring European leaders to meet founders and operators on the ground. His vantage point is one of the few that sits genuinely between the two systems.
⏱️ Chapter markers
[00:55] The first word that comes to mind: difference
[05:00] People trust the system, not the strangers in it
[12:01] Why "AI superpower" and "surveillance dystopia" both miss the territory
[19:00] Three layers of coordination: government, platforms, institutions
[22:30] Fragmentation as permanent reality, and compliance as a product feature
[35:00] The robotics inflection and what favourable policy makes possible
🔗 Links
Vincent Xiang on LinkedIn - https://www.linkedin.com/in/yxiangeclille/
China AI Connect on Substack - https://vincentxiang.substack.com
AI 2030 / AI Plus initiative reference - https://www.fmprc.gov.cn/eng/xw/zyjh/202509/t20250924_11715960.html
Related episode - Episode on Trust as Geopolitical Requirement: Eva's WEF 2026 recap - https://open.spotify.com/episode/1RKtxdJWXcQH8vnpnDtgEP?si=u_MfnmOvQ2-AXSPRONX6Gw
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Most enterprises have the technology to run agentic AI. They do not yet have the data architecture, identity layer, or empowered workforce to actually trust it. Anthony Alcaraz argues that the bottleneck for agentic AI has shifted from building the agents to building everything around them — and that the organisations most at risk are the ones keeping a human in the loop and calling it transformation. This conversation is for leaders sitting between AI pilots that worked and production systems that have not yet arrived.
💡Episode overview
Anthony joins Eva to map what changes when AI shifts from reactive systems to agents that observe, reason, and act. The conversation moves through what enterprises miss in their own data — systems of record that capture what happened but not why — and the new attack surfaces agents introduce, including tool poisoning. Anthony names the empowerment gap inside organisations: business experts who hold the knowledge agents need, with no clear path to building anything themselves. The most provocative moment lands near the end, when Anthony argues that human-in-the-loop adoption can be a way of avoiding actual transformation rather than achieving it.
🔍 Key themes discussed
The shift from reactive to agentic systems, and what trust has to carry nowWhy most enterprise data is missing the why behind decisionsTool poisoning and the new attack surface for agentsThe empowerment gap between business knowledge and technical capabilityGraph architecture as the control layer for agentic reasoningWhy human-in-the-loop can be a refusal to transform👤 About the guest
Anthony Alcaraz works across three vantage points that rarely sit together: he architects agentic AI systems, invests in early-stage AI startups as an angel, and is the author of Agentic Graph RAG with O'Reilly. He spends most weeks in conversation with founders attempting to enter regulated enterprises, and most evenings building software with the same tools he writes about. His perspective on this episode comes from watching the same gap repeat itself across organisations of very different sizes — the technology is ready, and most of the systems around it are not.
📍 Chapter markers
[00:00] What changes when AI moves from reactive to agentic[05:42] Why agents need access — and what enterprises have not built[10:29] The three problems: data, governance, and the people in between[23:13] Graph architecture and the missing why of enterprise data[32:06] The empowerment gap that no one has solved yet[45:17] In-between: where Anthony finds himself now🔗 Links
Anthony Alcaraz LinkedIn — https://www.linkedin.com/in/anthony-alcaraz-b80763155/ Agentic Graph RAG (O'Reilly) — https://www.oreilly.com/library/view/agentic-graphrag/9798341623163/ Foundation Capital context graph thesis — https://foundationcapital.com/ideas/the-case-for-context-graphs Related episode — Trust as an operating system in AI companions https://open.spotify.com/episode/5t4BtgevPOtMWUfB4jThWX?si=oGo2JPHNTeCTxbqkNXDJMwEva Simone Lihotzky's LinkedIn: https://www.linkedin.com/in/evalihotzky/ -
AI has collapsed the cost of producing political content. Verifying it is another matter, and Cohen has spent two decades watching that gap widen from inside campaigns and classrooms. He has a three-part test for practitioners navigating it — real, authentic, factual — and this conversation is about why he thinks it has to be taught before anyone reaches the job.
📻 Episode overview
Cohen runs Congress in Your Pocket, teaches digital campaign strategy at Johns Hopkins and NYU, and serves as executive director of Fight Hate, which works to reduce anti-Semitism on college campuses. From all of it, his argument is the same: the ethical line gets drawn before practitioners reach the job, or it does not get drawn at all. The conversation moves through what it cost him to hold a non-partisan position when one side of the political spectrum came after him, why he believes hyper-targeting served democracy better than broadcast advertising did, and what his students are starting to find they can no longer reliably spot in AI-generated video. Real, authentic, factual — he gives students that test before they touch the tools, because by the time they are on a campaign, the pressure to cross the line is already there.
🔍 Key themes discussed
What changes when AI makes political content production fast and cheapEighteen years of answering every user email personally — and what that reveals about civic trustWhy he teaches the ethical line before students touch the toolsFight Hate and the deliberate choice to stop fighting hate onlineWhat happens when AI-generated video gets good enough to fool the generation that grew up spotting it👤 About the guest
Dr. Michael Cohen lectures in political campaigning and digital strategy at Johns Hopkins University and NYU, and wrote Modern Political Campaigns: How Professionalism, Technology, and Speed Have Revolutionized Elections. He founded Congress in Your Pocket in the year of the first iPhone and has run it for eighteen years, answering every user email personally throughout. He is currently executive director of Fight Hate, working to reduce anti-Semitism on college campuses through student-led offline organising.
🕐 Chapter markers
[00:01] The iPhone as political infrastructure[06:08] What eighteen years of personal emails taught him about trust[13:36] Why hyper-targeting may be better for democracy than broadcast advertising[19:31] Real, authentic, factual — the line and what it costs[24:35] Fight Hate: using digital tools to get people off them[37:35] The authenticity meter: how far AI video has pushed even digital nativesTimestamps approximate from transcript - adjust after final edit.
🔗 Links
Dr. Michael Cohen on LinkedIn - https://www.linkedin.com/in/michaeldavidcohen/ Congress in Your Pocket - https://www.congressinyourpocket.comFight Hate website - https://fighthate.org/home/Modern Political Campaigns (book) - https://www.modernpoliticalcampaigns.com Blue Square Project by Robert Kraft - https://www.bluesquarealliance.org/bsa-blue-square-alliance-take-over-b/?nab=1Eva is on LinkedIn - https://www.linkedin.com/in/evalihotzky/ -
Most security failures are organisational: This episode is about the gap between threat intelligence that exists and the human systems that never act on it, and what that costs the organisations that keep losing to attacks they already understood.
Assaf Kipnis has spent over a decade inside the threat intelligence and trust and safety functions of some of the world's largest platforms. In this conversation, he maps a structural failure that runs across the industry: the team that identifies threats and the team that deploys detection operate in parallel, with no reliable mechanism to connect them. Intelligence gets produced, reports get written, and the knowledge sits unused while the same attacks return. Assaf describes what it actually took to stop a sophisticated actor group ahead of the 2020 US elections - a rare case where structure and resources aligned - and explains why that outcome is the exception rather than the rule. He also walks through the design decisions behind Catalyst Labs, the company he is now building to close the gap, and why he made provenance non-negotiable even at the cost of speed.
🎙 Key themes discussed
Why security teams are structurally rewarded for fighting fires rather than preventing themThe organisational gap between threat intelligence and detection - and why it persists even in well-resourced teamsWhat data provenance means in practice, and why it matters more than speed when using AI in securityHow attackers learn your defences faster than you can adapt - and what the military analogy revealsWhy trust online currently feels, in Assaf's words, like a pipe dream👤 About the guest
Assaf Kipnis is the founder of Catalyst Labs, with over 12 years working across threat intelligence, information security, and trust and safety at LinkedIn, Google, Meta, and ElevenLabs. He brings the perspective of someone who has spent his career making threats legible to organisations - and watching those organisations lack the structure to act on what they could now see.
🕐 Chapter markers
[00:18] Why the industry keeps fighting the same fires
[08:04] What it actually took to stop an actor group - the 2020 elections case
[12:36] How AI is widening an asymmetry that already existed
[15:31] Catalyst Labs: the provenance problem and why speed comes second
[20:35] What to build first if you're starting a threat intelligence team
🔗 Links
Assaf Kipnis https://www.linkedin.com/in/assafkipnis/
KTLYST Labs https://www.ktlystlabs.com
Background information on MGM / FBI reports: https://www.reuters.com/technology/cybersecurity/fbi-struggled-disrupt-dangerous-casino-hacking-gang-cyber-responders-say-2023-11-14/
Related episode: organisational trust and AI implementation with Simon Berkler https://open.spotify.com/episode/6y8PMaVUnZVAR1hOAR15DN
Related episode: accountability and invisible infrastructure with Sergiu Petean https://open.spotify.com/episode/4KcsZBDgFzkSuwQVihjNR5
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🎙️ Simon Berkler, Co-Founder of The Dive
🎧 About this episode
This episode of The In-Between Tech & Trust Podcast asks a question every leader is quietly facing: what does AI actually do to the trust inside your organization — and what does your trust culture do to AI? Simon Berkler, organizational development expert and co-founder of The Dive, argues that technology doesn't change organizations. It reveals them. The conversation is for leaders, HR professionals, and anyone navigating organizational transformation in the age of AI.
🧭 Episode overview
Eva Simone Lihotzky speaks with Simon Berkler about why trust is not a soft skill but the structural condition that makes organizations work — and why that matters more than ever in the context of AI adoption. Drawing on systems theory, regenerative organizational design, and 20+ years of hands-on OD practice, Simon reframes the tech-and-trust debate: the question is not which AI tools to adopt, but what kind of organization you already are. Because AI, he argues, will act as a mirror — amplifying what's already alive, for better or worse.
They explore how to lead through in-between moments when old logic is crumbling and new logic hasn't formed yet, why collective intuition may be the most underused organizational resource, and what it would mean to design governance structures built for uncertainty rather than against it.
🧩 Key themes discussed
Why trust reduces social complexity — and what that means practically for organizational transformationAI as a mirror of organizational culture: how existing trust levels determine whether AI becomes augmentation or surveillanceThe difference between trust and probability — and why AI runs on the latter, not the formerLeading through in-between spaces: how to change the rules while still playing the gameCollective intuition as a strategic resource for navigating complexity, drawing on the work of organizational psychologist Peter KruseThe Stellar Approach: a regenerative OD framework for moving organizations from conventional to net-positive ways of workingWhy rhythm is the most overlooked asset in transformationShifting organizational governance from optimizing for certainty to optimizing for uncertaintyWhat "safe enough to try" looks like as a leadership stance in AI adoption📥 References & further reading
The Dive — Simon's organizational development consultancy: thedive.comSimon Berkler's personal site & writing: simon-berkler.deThe Stellar Approach by Simon Berkler & Ella Lagé (2024): AmazonNiklas Luhmann, Trust and Power — systems theory foundation for the episode's framing of trust: AmazonNora Bateson & the concept of Warm Data — the distinction between warm and cold data Simon references: warmdata.lifePeter Kruse on collective intuition and complexity — the four ways of dealing with complexity Simon draws on: artsnext.ch summary -
🎙️ Dr. Paul Elvers, Head of AI at Funke Mediengruppe
💬 Summary
This week's episode of the in-between tech & trust podcast examines how AI is being used inside one of the largest media organizations in Germany, with a focus on trust, transparency, and day to day editorial practice - steered by Dr. Paul Elvers, Head of AI at Funke Medienhaus and podcast host Eva Simone Lihotzky. The conversation is for media specialists, editors, product leaders, and anyone working close to news production and consumption. The episode dives deep into the choices directly affecting credibility, audience trust, and the role journalism plays in a democratic society.
🎧 Episode overview
In a detailed discussion, Dr. Paul Elvers walks through how AI actually shows up in newsroom workflows, separating real operational value from common misconceptions. Rather than debating whether AI should exist in journalism, the episode stays grounded in how it is governed, where human responsibility remains essential, and why naïve adoption is a bigger risk than cautious experimentation. The conversation also explores how audiences judge credibility in an environment flooded with synthetic content, and what media organizations can realistically do to maintain trust while adapting to new tools and distribution pressures.
🔍 Key themes discussed
Why trust in AI comes from understanding systems and accountability, not blind confidenceThe difference between deliberate AI integration and careless, volume driven adoptionHow “AI slop” reflects a growing difficulty in judging what is trustworthy, not just content qualityUsing AI to automate necessary but unpopular newsroom tasks while keeping humans at the start and endThe role of recognizable brands and journalists in sustaining audience trustWhat transparency about AI use looks like in real editorial workflowsWhy AI governance in media is iterative, shared, and never fully settled -
🎙️ Iwona Fluda, expert for creativity & ethics
🧭 Opening
This week's episode of the in-between tech & trust podcast examines how AI is reshaping creativity, trust, and responsibility in everyday work. If you work in creative fields, technology, or organizational leadership who are dealing with AI as a practical reality rather than an abstract future, then this podcast is for you.
🗣️ Episode overview
Eva Simone Lihotzky is joined by creativity and ethics expert Iwona Fluda, founder of the Ministry for Creativity, Head of AI and Content Growth at Deamleaps and ambassador for the Royal Society for Arts, Manufactures and Commerce. Together, they unpack why trust in technology is eroding, how AI tools affect human thinking when cognition is outsourced, and why creativity cannot be reduced to speed or output. The discussion moves between individual responsibility, organizational shortcuts, and the ethical gaps that appear when inclusivity and long term design are treated as secondary concerns.
🧩 Key themes discussed
Cognitive engagement and AI
How relying on AI without active thinking weakens human cognition, drawing on research associated with the MIT Media Lab.Creativity under pressure
Creativity as a historically essential survival skill, and why it remains structurally undervalued despite being central to innovation.AI as tool and disruptor
The dual role of AI as a powerful collaborator for some and a driver of job loss for others, especially in creative and marketing work.Trust in technology and platforms
Why skepticism, not trust, defines today’s relationship with technology and institutions, including content ecosystems like LinkedIn.Radical inclusivity by design
The limits of add-on ethics programs and the need to build inclusivity into systems from the very beginning.Efficiency versus responsibility
Organizational choices that favor short term gains over long term impact, even when frameworks like the EU AI Act already exist.Societal and existential risk
Concerns about large scale job displacement and long term societal disruption, including references to thinkers such as Roman Jampolsky. -
🎙️Prof. Dr. Heiko von der Gracht, Professor at the University of Krems
Opening
Episode 19 of the in-between tech & trust podcast explores how organizations can make better decisions under uncertainty through foresight and scenario planning. In conversation with Heiko von der Gracht, professor at the University for Continuing Education Krems and long-standing practitioner of foresight practices, the discussion looks at how trust, technology, and perception shape what leaders think is possible. It is especially relevant for people working with strategy, innovation, or long-term planning in fast-moving environments.
🧭 Episode overview
The conversation examines foresight not as prediction, but as a practical discipline for stress-testing assumptions and improving choices when the future is unclear. Drawing on decades of research and applied work, Heiko reflects on why uncertainty feels overwhelming today, how media and digital systems influence our perception of risk, and why traditional planning often breaks down under rapid change.
The episode also looks at how trust is being reshaped by scalable, anonymous technologies, and what this means for organizations trying to act responsibly and coherently over time.
🔍 Key themes discussed
Why foresight is about decision quality, not forecasting outcomes
The difference between actual uncertainty and how uncertain the world feels
How complexity and speed interact to undermine linear planning
Trust in digital environments shaped by anonymity, scale, and weak accountability
Knowledge overload, misinformation, and the loss of shared reality
Scenario planning as a strategic conversation rather than an analytical exercise
Empirical evidence that sustained foresight investment improves performance
The discussion also draws on Heiko’s involvement in global foresight and governance contexts, including work connected to the World Economic Forum and UNESCO, grounding the conversation in both research and lived practice.
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🎙️Grisha Pavlotsky, Chief Transformation Officer at Miro
Opening paragraph
This episode shares a conversation between Grisha Pavlotsky, CTO of Miro, and Eva Simone Lihotzky. It examines trust as a practical design problem in teams, AI systems, and everyday decision-making. The conversation is for leaders, builders, and parents trying to make sense of how judgment, accountability, and authority shift when AI becomes part of how work and learning happen. It focuses on what needs to be made explicit - intent, guardrails, and decision logic - rather than assumed.Episode overview
Grisha draws on his work leading transformation at Miro and his experience raising four children to explore how trust holds - or breaks - when information is abundant and increasingly synthesized. The discussion moves between organizations and families, treating them as parallel systems facing the same challenge: people are no longer short on answers, but on the ability to judge, contextualize, and disagree productively. Along the way, the episode questions current education models, critiques optional AI adoption, and argues that trust depends less on confidence and more on transparency about how decisions are made and who remains accountable.Key themes discussed
Trust as alignment on intent plus visibility into decision frameworks, not just emotional safety
How AI amplifies confidence without guaranteeing expertise, complicating collaboration
Why probabilistic systems require clear guardrails, not vague goals
The shift from producing synthesis to judging and challenging synthesized viewpoints
Education moving from teaching facts to navigating competing narratives
Identity and ego as the real blockers in large-scale transformation
Leadership responsibility in making AI adoption mandatory rather than optional
Parenting and organizational leadership as the same sense-making problem at different scales
A recurring reference is the idea - attributed to Satya Nadella - that trust is built through consistency over time, and what that consistency demands in an AI-mediated world.
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Opening
This solo episode of The In-Between Tech & Trust Podcast reflects on conversations from Davos and what they reveal about where tech, politics, and trust are heading into 2026. It’s for leaders, operators, and policy-adjacent roles who are trying to make sense of AI adoption beyond tooling. The focus is on what actually changes inside organizations, institutions, and collaborations when AI becomes infrastructure.
🎧 Episode overview
Eva Simone Lihotzky unpacks four threads that kept resurfacing across discussions with tech, political, and business leaders: agentic AI systems, the politics of technology, sovereignty, and the future of collaboration and trust. Rather than reporting speeches, the episode explores tensions beneath the surface - why organizations feel urgency but struggle to act, how AI exposes institutional weaknesses instead of fixing them, and why governance, infrastructure, and responsibility are now inseparable.
The episode moves between business realities and geopolitical dynamics, asking what it really means to design AI-driven organizations, who shapes the rules when tech and politics are interwoven, and how dependence on a small set of platforms reshapes power, accountability, and autonomy.
🔍 Key themes discussed
Agentic AI systems and why they force a rethink of organizational design
AI adoption as a platform shift, not a tool rollout
The gap between AI urgency and practical implementation inside companies
World models vs. specialized models and why both matter
Interoperability as an unsolved infrastructure problem
Tech as both upstream and downstream of politics
Sovereignty across compute, infrastructure, data, operations, and talent
Europe’s position in an AI-driven power landscape
Why collaboration now depends on explicit commitments, not assumptions
How trust becomes harder - and more necessary - as systems scale
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🎙️ with Dr. Marc Roman Franke, Partner & Associate Director AI and digital transformation at BCG
💬 Opening
Eva Simone Lihotzky speaks with Marc Roman Franke, Partner & Associate Director AI and digital transformation at BCG, about how trust is built - or lost - during AI transformation inside large organizations. The conversation is for leaders, product owners, and transformation teams trying to move beyond pilots and into real operating change. It focuses on why execution, governance, and organizational choices determine whether AI creates value or stalls.
🎤 Episode overview
Drawing on large-scale research and implementation experience, the episode examines why only a small share of companies see meaningful returns from AI. Franke argues that the main constraints are not models or tools, but leadership alignment, operating models, and how trust is earned through delivery. The discussion moves from the limits of “AI-ready” programs to what it means to become “AI-first,” including the rise of agentic AI, unmanaged security risks, and why postponing Responsible AI eventually blocks scale.
🎯 Key themes discussed
Trust as a practical outcome of reliable execution and visible value, not long-term promises
Why most AI value depends on people, organization, and leadership rather than algorithms
What separates the small minority of companies that capture real AI value from the rest
The difference between experimenting with AI and redesigning the business around it
How agentic AI changes accountability, decision rights, and human–AI collaboration
Governance as an enabler of adoption and safety, not a compliance afterthought
Security and third-party risks that grow as AI scales
When Responsible AI can be delayed—and why it becomes a blocker later
🤝🏻 Referenced during the conversation:
BCG, MIT, SAP S/4HANA, GDPR, and Steve Jobs.
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🎙️Lior Oren, Chief Technology Officer at Replika
A conversation on how emotionally intimate AI systems are built, monitored, and held together under real-world constraints.
🎧 Opening
This episode explores how trust is built, measured, and sometimes strained in AI systems designed for emotionally intimate conversations. It’s a technical and ethical discussion for people working on conversational AI, product infrastructure, and safety in systems that users form real attachments to. The focus stays on operational reality - what engineers actually face when AI moves from tools to companions.
🔍 Episode overview
Eva Simone Lihotzky speaks with Lior Oren about what it means to run AI companions at scale, where user trust is not an abstract principle but a daily KPI. Drawing on his experience as CTO of Replika and prior work on integrity teams at Meta, Lior explains how unpredictability, observability, and emotional reliance shape engineering decisions.
The conversation examines tensions between flexibility and stability, innovation and guardrails, and regulation and lived product reality. Rather than future speculation, it stays grounded in how teams design memory, user control, and safety systems when conversations themselves are the product.
🧩 Key themes discussed
Trust treated as a measurable success metric, not a philosophical goal
Why observability is essential in statistical, non-deterministic AI systems
Guardrails as part of core infrastructure, similar to security or reliability
Emotional attachment influencing uptime, priorities, and team culture
User agency through transparency, memory control, and conversational steering
The risk of breaking “tone” and continuity when models change
Limits of regulation and the trade-offs inherent in statistical safety systems
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🎙️ with Trusha Rolvering - Director Transformation @adidas
🎙️ with Carina Hauswald - Managing Partner @GlobeOne
🎙️ with Kathrin Steinbichler - Director Narrative Consulting
🎙️ with Mirja Schwartz - Head of Business Development @showz
Summary
In this episode of the in-between trust podcast, host Eva Simone Lihotzky engages in a thought-provoking discussion with four women leaders about the intersection of technology and trust as they look ahead to 2026. The conversation recaps personal and organizational trust in technology, the challenges of AI adoption, and the balance between efficiency and human connection. Each guest shares insights on how to navigate the complexities of technology in their respective fields, culminating in predictions for the future of AI and its impact on trust in 2026.
🔑 Takeaways
The intersection of tech and trust is crucial for transformation.
Trust is a significant barrier to changing human behaviors.
AI can enhance efficiency but requires a shift in mindset.
Organizations need to create space for exploration and experimentation with AI.
Transparency in using AI builds trust with clients and teams.
The speed of technological change can overwhelm organizations.
AI should complement human skills rather than replace them.
Future conversations will focus on new business models and possibilities.
Reflection and pause are essential in the fast-paced tech landscape.
Empowering individuals to explore technology fosters trust and innovation.
🎙️ Sound bites
"It's about trust to communicate yourself."
"AI frees up lots of space and time mentally."
"We need moments to reflect and pause."
⏱️ Chapters
00:00 Introduction to the Intersection of Tech and Trust
05:18 Exploring Personal and Organizational Trust in Technology
13:45 Navigating Expectations vs. Reality in AI Adoption
21:09 The Future of AI: Efficiency vs. Human Connection
34:23 Betting on the Future: Predictions for 2026
40:44 Reflections and Closing Thoughts
🔭 Keywords
tech, trust, AI, transformation, organizational change, human behavior, efficiency, communication, strategy, future predictions
💻 Links
in-between trust on Instagram: @inbetween_trust
More about Trusha Rolvering: https://tinyurl.com/2pj5k69w
More about Kathrin Steinbichler: https://tinyurl.com/2rv2hpef
More about Carina Hauswald: https://tinyurl.com/4twrc37h
More about Mirja Schwartz: https://tinyurl.com/mr26cv3p
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🎙️ with Gifty Enright - author, speaker and expert on women's leadership
Summary
In this episode of The In-Between Trust Podcast, Eva Simone Lihotzky sits down with Gifty Enright - author, speaker, and women’s leadership expert - to unpack a tension many of us feel but rarely articulate:
In a hyper-visible world, we are more “searched” than ever, and yet we feel less seen.
Gifty exposes the invisible emotional infrastructure that holds workplaces together - the labor of noticing, soothing, anticipating, and absorbing complexity - work disproportionately done by women and almost never acknowledged as expertise.
Together, Eva and Gifty explore why trust is less about performance and more about relational safety, how leaders can cultivate embodied awareness in an age of tech, and why empathy can’t be automated — even by the most advanced AI.
The conversation moves from gendered trust patterns to the redesign of leadership for the AI era, offering a grounded reminder: Your body knows the truth long before your mind catches up. If you lead teams, build tech, or are navigating emotional load at work, this episode will challenge how you think about trust, resilience, and the limits of technology.
Takeaways
Trust is shifting from task-based to relational — people follow leaders who create emotional safety.
Technology blurs boundaries and accelerates burnout unless paired with embodied leadership.
Emotional labor — often carried by women — is a hidden operating system of organizations.
AI can pattern-match, but it cannot feel; empathy remains a human capability.
Leaders must regulate their internal systems to earn trust externally.
Women are socialized to earn trust; men are often socialized to assume it.
Stillness is not a luxury — it’s a leadership technology.
Trust grows in the pause; speed erodes nuance.
Tech should augment humanity, not override it.
Soundbites
“Tech can predict patterns, but it can’t feel your pain.”
“Women aren’t trusted by default — they’re conditioned to earn it.”
“Stillness is a leadership tool, not a luxury.”
“The pause is where trust breathes.”
Chapters
00:00 — What Trust Really Means in Today’s Workplace
02:06 — Tech Acceleration & the Cost to Human Wellbeing
04:28 — The Hidden Burden: Emotional Labor in Organizations
07:50 — Why AI Can’t Replace Empathy
12:17 — Embodied Intelligence: Leading from the Body, Not Just the Mind
15:59 — Self-Trust vs. System Trust
18:55 — Gendered Dynamics of Trust and Power
22:02 — Rethinking Leadership for the AI Age
24:07 — Human Evolution, Work, and What Comes NextKeywords
trust, emotional labor, leadership, AI limits, embodied intelligence, workplace wellbeing, gender dynamics, empathy, psychological safety, organizational culture, women in leadership
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🎙️ With Prof. Dr. Tina Weisser – Professor at Hochschule München (University of Applied Sciences Munich)
Summary
In this episode of the in-between trust podcast, Eva Simone Lihotzky speaks with Prof. Dr. Tina Weisser about trust, systems thinking, and AI. Drawing on her path across service design, entrepreneurship, transformation consulting, and academia, Tina explores why “trust reduces complexity” and how that insight reshapes human–technology interaction - from multi-agent systems to the day-to-day realities of teams. The conversation moves from black-box AI to leadership futuring, psychological safety, and the practical redesign of processes so that humans stay in the driver’s seat.
🔑 Takeaways
Trust reduces complexity and enables decisions under uncertainty
AI’s “black-box” behavior demands experience, verification, and critical thinking
Multi-agent systems shift from personal to team and workflow productivity
Don’t “plug & play” AI - redesign processes with a systems lens
Keep humans in the driver’s seat: transparency, orientation, competence
Build psychological safety to experiment, fail, and learn
Treat AI change as an adaptive challenge (not just a technical one)
Bridge IT, HR, and leadership - it cannot be top-down or bottom-up alone
Create time for deep strategic thinking (“leadership futuring”)
Invest in training & enablement, not only tools
🎙️ Sound Bites
“Trust reduces complexity.”
“We need to stay in the driver’s seat - even with agents in the background.”
“It’s not a plug-and-play solution; redesign the system.”
“Build trust through experience - and keep your critical thinking.”
“Make space for deep thinking, not just meetings.”⏱️ Chapters
00:00 Curiosity, boundary-spanning, and systems thinking
03:14 Trust as a way to reduce complexity (Luhmann)
06:47 AI as a black box: probability over truth
10:19 Learning from early multi-agent experiments
12:27 From personal productivity to team workflows
14:49 Redesigning processes (not just “adding AI”)
16:26 Leading uncertainty: safety, training, enablement
18:17 Adaptive vs. technical problems (Heifetz)
22:38 Bridging culture and tech; capabilities leaders need
24:59 Haltung, self-regulation, and authenticity in leadership
27:02 Iteration over perfection; shorter planning cycles
30:24 “Stop the noise—start working”: a pragmatic toolbox
32:59 Leadership futuring: time for strategy, signals, foresight
36:36 In-between moments & reflections🔭 Keywords
trust, systems thinking, leadership, AI, human–AI interaction, multi-agent systems, service design, process redesign, psychological safety, adaptive leadership, experimentation, foresight, leadership futuring
💻 Links
in-between trust on Instagram: @inbetween_trust
More about Hochschule München (HM): [insert link]
More about Prof. Dr. Tina Weisser: [insert link] - Visa fler
