For hospitals and clinic groups

The hospital bought AI. The clinicians aren’t using it.

I build clinical intelligence for European hospital groups — documentation, records, the unglamorous places where AI either saves a ward real time or quietly creates work. I speak and run sessions about what separates the two.

Dr. Sven Jungmann
Clinic group, Berlin

Where it usually breaks

The workflow nobody redesigned

The tool is dropped into a documentation process that was already broken, so it produces faster versions of the wrong document. Adoption dies quietly, and everyone blames the model.

The trust question nobody closed

Clinicians will not sign their name under output they cannot check. If nobody has answered what happens when it is wrong, and who is liable, the safest professional choice is not to use it.

The pilot that proved the wrong thing

A successful pilot in one department with three enthusiasts tells you nothing about the ward at 3 a.m. with agency staff. Scaling on that evidence is how programmes stall in year two.

Das Wissen zu Künstlicher Intelligenz war nicht nur theoretisch, sondern praxisnah und greifbar. Man spürte, dass das Wissen gelebt und nicht nur gelernt ist.
Teilnehmer · Keynote für NEULAND Wohnungsgesellschaft
SiemensHealthineersMedtronicColiquioSamediAvieElsevier

Talks for this audience

01

The AI is installed. Almost nobody uses it.

Why adoption stalls, and what the organisations where it worked did differently.

EN · DE · FR

The gap between AI that is bought and AI that is used, taken apart with real numbers from real deployments — including the ones that failed. Built for an audience that has already spent money and wants to know why the return has not arrived.

What the room leaves with
  • The four frictions that account for most stalled adoption, and which one is yours
  • Why the pilot that succeeded is often the reason the rollout failed
  • A test for telling genuine productivity from displaced work

02

What AI actually changes in a hospital.

From someone who is deploying it in one this quarter.

EN · DE

Documentation, records, handovers, coding — where clinical AI genuinely gives time back, where it creates a new class of error, and what the wards that made it work insisted on. For clinical leadership, medical councils and hospital IT.

What the room leaves with
  • The specific failure mode of AI that writes into a patient record
  • What clinicians need before they will sign their name under machine output
  • Which three questions to ask a vendor that they will not enjoy

03

Two senior roles, one AI, and me.

What changed when I took over two experts’ work with AI, and what it means for how you lead.

EN · DE · FR

In my own company I let two people go whose work I could not have done myself before, a lawyer in the COO role and a UI designer with healthcare experience. I did not refill the roles. I took over their jobs, with AI: sixteen hours a week at first, a normal week now, and more than €300,000 a year in salaries. The talk tells that story without pride, including the mistakes of the first months, and draws out what holds for any leader: which work AI lets you take back, which stays with the team, and why delegating is getting more expensive while doing it yourself is getting cheaper.

What the room leaves with
  • One criterion for deciding which tasks to take over with AI and which to leave in the team
  • Why productivity drops in the first weeks and how to get through that phase
  • What changes for leaders when two layers between them and the work disappear

More about this talk →

Written on this

The Map Is Not the Terrain

The penicillin allergy on page seven that had never been tested, the discharge letter that read clean but wasn’t, and what happens when AI extracts data from documents that were already wrong.

Read the essay →

Bring it to your clinic group.

Board days, leadership retreats, medical-council meetings, digital-health congresses. In German or English.

Dr. Sven Jungmann