# Sven Jungmann Physician and founder of aiomics. Speaking explores the adoption of AI in healthcare and organisations, grounded in his work building the company. ## Background and speaking - [About](https://www.svenjungmann.com/about) - [Talks](https://www.svenjungmann.com/keynotes) - [Enquire about a talk](https://www.svenjungmann.com/booking) ## Original articles and practical guides - [Who owns the outcome when AI work crosses departments?](https://www.svenjungmann.com/writing/ai-outcome-ownership-across-departments): A fictional procurement case shows how AI can shift work between teams. Outcome ownership needs a shared definition of success and clear decision rights. - [Prepare an AI talk around your audience’s decisions](https://www.svenjungmann.com/guides/prepare-ai-talk-organiser-brief): A guide for organisers: define the audience, decision and format, distinguish scientific claims, and discuss accessibility, recording and medical education recognition early. - [Five questions about an AI claim: TrialGPT for pharmaceutical leaders](https://www.svenjungmann.com/guides/pharma-ai-claim-trialgpt-five-questions): What 42.6% less screening time means, and the five questions pharmaceutical leaders should answer before turning that claim into an investment. - [Before your AI pilot, decide what would make you stop](https://www.svenjungmann.com/guides/precommit-ai-pilot-decision): Agree interruption rules, sufficient evidence and the next decision before an AI pilot. Includes a 20-page booklet with fillable working pages. - [Verify AI outputs: sources, omissions and contradictions](https://www.svenjungmann.com/guides/verify-ai-output-against-sources): A reproducible source exercise demonstrates five planted errors, two omissions and an unresolved discrepancy in the primary paper. Two working instructions help structure your own review. - [Who maintains the knowledge your AI uses?](https://www.svenjungmann.com/guides/ai-organisational-knowledge-ownership): A practical guide to reliable definitions, ownership and current knowledge, using a capacity-planning example and a small maintenance agreement. - [What an AI benchmark can tell a hospital](https://www.svenjungmann.com/writing/medical-ai-benchmarks-hospitals): Technical capability, collaboration and patient outcomes answer different questions. Eight supplier questions help hospitals connect benchmark evidence to the intended use. - [Evaluate AI pilots: quality, effort and value](https://www.svenjungmann.com/guides/evaluate-ai-pilots): A practical process for leaders: define one task, compare representative cases and count the complete effort required to produce a usable result. - [AI saves ten minutes. What happens next?](https://www.svenjungmann.com/writing/ai-time-savings-organizations): How less drafting time becomes a real benefit: a complete worked example including review, rework, abandoned attempts and the decision about where the time saved should go. - [AI agents: define permissions and approvals](https://www.svenjungmann.com/guides/define-ai-agent-permissions): Which actions may an AI agent take? A guide to bounded assignments, technical permissions, approval points and verifiable execution. - [AI and early careers: how leaders can read conflicting employment evidence](https://www.svenjungmann.com/writing/ai-early-career-employment-evidence): US payroll data, a Danish study and an international task index answer different questions. A guide to interpreting them when making workforce decisions under uncertainty. - [What can we still do after AI has helped?](https://www.svenjungmann.com/writing/ai-independent-learning): Two education studies show how strongly the effects of AI depend on the learning environment. They raise practical questions for clinical education and leadership development. - [When AI shares expertise: what should experienced professionals do?](https://www.svenjungmann.com/writing/ai-expertise-transfer): A large customer-support study raises a leadership question: how can organisations share expertise while continuing to develop the capacity for new decisions? - [An AI learning session that rehearses the next decision](https://www.svenjungmann.com/guides/ai-training-decision-practice): An adaptable learning sequence for healthcare and business: form a judgement, use assistance, work through a new case and assess learning again later. - [When AI becomes unavailable: prepare for continuity and vendor exit](https://www.svenjungmann.com/guides/ai-service-continuity-vendor-exit): A guide for leaders to define minimum operations, identify dependencies and rehearse recovery using a fictional case. Includes preparation for a provider change. - [Maintaining shared AI instructions: test changes and preserve versions](https://www.svenjungmann.com/guides/maintaining-shared-ai-instructions): A practical maintenance agreement for shared AI instructions: purpose, test cases, changes, approval and returning to an earlier version. - [When people and AI disagree: resolve the disputed decision](https://www.svenjungmann.com/guides/resolving-human-ai-disagreement): An adaptable procedure for organisational decisions: define the disagreement, check authoritative information and record the cause for future cases. - [Ending an AI initiative while preserving what was learned](https://www.svenjungmann.com/guides/ending-ai-initiative-preserving-learning): How leaders can compare continuing, pausing, redirecting and ending an initiative, with clear next commitments and an orderly handover. - [The responsibility behind an AI draft](https://www.svenjungmann.com/writing/ai-drafts-review-responsibility): An AI draft needs inspectable evidence and a clear handover of responsibility. A fully fictional administrative workflow shows who approves each claim and the action it may support. - [An AI-designed antibody binds: which decision comes next?](https://www.svenjungmann.com/writing/ai-antibody-binding-development-decisions): What a binding result means for drug development, and how pharmaceutical companies can select the next experiments by the decisions they inform. - [A robot’s capacity includes the people who support it](https://www.svenjungmann.com/writing/robot-capacity-human-support): How organisations can include human assistance, interruptions and changing environments when planning the use of physical AI. - [AI electricity use: assess requests, workflows and infrastructure separately](https://www.svenjungmann.com/writing/ai-electricity-request-workflow-infrastructure): How leaders can interpret AI electricity figures and turn individual requests into an accountable view of a complete workflow. - [Buying or building AI: the complete cost calculation](https://www.svenjungmann.com/guides/buy-or-build-ai-whole-cost): A fully hypothetical worked example includes integration, review, corrections, operation and exit. Inspect the assumptions and the costs that can change the decision. - [When experienced professionals hesitate over AI: what remains unresolved?](https://www.svenjungmann.com/writing/experienced-professionals-ai-hesitation): How leaders can turn reservations about AI into concrete questions without inferring motives from experience, age or a refusal. - [AI makes the task easier. How should collaboration change?](https://www.svenjungmann.com/writing/ai-task-capability-team-redesign): A reflection on Benedict Evans’s conversation: what individual results tell us about team composition, and how a new way of working can be examined. - [What an AI disclosure changes about a medical answer](https://www.svenjungmann.com/writing/ai-disclosure-medical-answer-trust): Identical answers can receive different evaluations depending on their attributed source. What research shows about AI disclosure, clinical review and clear explanations. - [Ambient documentation: what remains after working hours?](https://www.svenjungmann.com/writing/ambient-documentation-after-hours-work): What clinical studies show about note-writing time, exhaustion and after-hours work, and which measurements hospital medical leaders need for a local pilot.