VP of People at Gorilla | In the pursuit of employee significance | Leadership & Career Coach | Illustrator | Corporate Hippy - views expressed here are my own

I’m a tech executive who believes the future belongs to companies that combine AI capability with human intelligence.

Not AI as a silver bullet.
Not people strategy as soft culture work.
Not simplistic “do more, faster” productivity narratives.
But practical systems that help organisations make better decisions as they scale.

I’ve spent my career building and operating those systems across people, performance, operating models, and now AI. 

The work only matters if it shows up in reality; how leaders lead, how teams decide, and how companies change without losing trust.

Most recently, I led the global People function at a Series B energy tech company and co-led our early AI rollout across governance, ethics, enablement, tooling, and experimentation. 

Alongside this, I work with founders, executives, and investors as a fractional leader and advisor. I help organisations roll out AI responsibly, navigate change, and build people systems that compound growth rather than create drag.

If you’re a founder, COO, CPO or leader trying to:
- adopt AI without breaking trust or momentum
- lead change without drowning managers in process
- scale people and operating systems that actually work in the real world.

I write and speak for you.

Things I write about:

- Practical AI adoption without hype or harm
- Where human judgment still matters in everyday AI decisions
- Leading change that creates clarity and engagement, not chaos
- People systems that support growth instead of getting in the way
- Why culture breaks during scale-ups, and how to fix it

Vanessa Monsequeira

It's been a big week, folks

I'm hopping on a plane to Australia for a workation. 

On the agenda:
🌞 Surf - Sun - Seafood.
☕ A proper flat white (finally).
💜 Laughs with family and friends.

Also on the agenda:

Customer calls with Europe from my laptop .. beachside. 

Coffee catch-ups and lunches with Aussie legends. Megan Trotter Matt McFarlane Mark Lewis Mohit Midha Careen Matthews Adam Axon 

to have lots of conversations about AI, People Experience and HR Tech. 

Basically defrosting before tulip season drags me back.

See you on the flip side.

Vanessa Monsequeira - चित्रलेखा

AI Transformation and Change Leadership | Fractional VP People | Building Work as a Product | Corporate Hippy

A woman with dark curly hair and gold-rimmed glasses grins broadly at the camera, holding up an Australian passport. She's wearing a colourful plaid scarf and a red jacket, photographed in an airport terminal.

2 months ago, I had to decide to stay in-house or bet on myself and go solo.

I knew which direction pulled at me.

But I was about to play it safe: positioning myself in a category that felt comfortable, familiar and small.

I told myself I didn't have the right to play in AI.

Even though I'd led AI adoption in my last organisation.

Even though our VC asked me to present best practices and pitfalls to their portfolio companies.

The doubts were loud:
↳ “I'm not technical enough”
↳ “What about the financial risks?”
↳ “Who am I to claim this space?”
↳ “What if I am no good?”

Then I met Frans van Loef in January. 

A busy executive coach, author and former CEO. The kind of person whose calendar you don't expect to make it onto twice.

We had a mentoring conversation over a year ago.

But when we sat down at the same café, same table, it felt like no time had passed.

Like catching up with an old friend who wasn't going to let me shrink. And someone who could show me what it actually looked like to back myself.

He didn't let me get away with playing small.

Frans didn't give me a pep talk. He held up a mirror.

Not just to the last 15 months of AI work, but to 15 years of leadership, change, transformation, tech and business consulting.

The pattern was right there. I'd been doing this work all along, I just hadn't claimed it.

That conversation shifted something.

I stopped waiting for permission and started building.
(more on this every week)

Last week, I launched my business.

I help tech leaders close the gap between AI ambition and AI outcomes, without burning out their managers or losing top talent.

Since then, I've had multiple enquiries and calls.

That's what happens when someone helps you see what you've been too close to see yourself.

Building a business will never be easy.

But if there's a "shortcut", it's having someone who can reflect your capability back to you when self-doubt is doing the talking.

Then it comes down to backing yourself. FULLY.

Frans, thank you for being in my corner!


PS. I’m about to launch my newsletter on How AI is rewiring thinking, leadership, and the workforce. Get it for free: https://lnkd.in/eh_aYrzw

Close-up selfie of two people smiling warmly at the camera indoors. On the left, a man with short light brown hair and blue eyes wears a navy collared shirt. On the right, a woman with shoulder-length dark curly hair wears a light knit top, gold necklace, and colorful earrings. They are standing close together in a cozy space with exposed brick and soft natural light in the background.

Anthropic dropped their AI fluency report this week.

Two findings made me curious and hungry:

1. AI Fluency is strongly linked to iteration and refinement, not just output volume.

2. When creating outputs (e.g. documents, code), users get more directive but less evaluative.

AI is making us better but only if we use it to augment our thinking, not delegate, deliver and forget. 

I believe the answer isn't to stop using AI to create outputs.

It's to use it with critical thinking:
↳ Structure Problem Solving
↳ Analysis and Critique
↳ Decision Making 

The problem is that critical thinking is rarely taught in schools, universities, or businesses.

So we're handing people a powerful tool with no framework for how to actually think with it.

That's why I built the The Critical Thinking Sandwich:

Your thinking is the bread: Thick, sturdy, holding everything together.

The filling (meat or veg) is where the real work happens: The iteration, the critique, the refinement.

AI is the cheese: Essential, delicious, sticks things together, but never the main event.

AI is the cheese but it doesn't make the sandwich.

Your thinking does.


#criticalthinking #AI #cognitivedebt

"How to stay sharp when AI does the work" showing The Critical Thinking Sandwich — a five-layer framework for using AI without losing your edge.
Layer 1 (orange, thick): Critical Forethought. Frame the problem yourself before you prompt.
Layer 2 (yellow, thin): AI Output v1. Raw material. Not the answer.
Layer 3 (green, thick): Critical Analysis. You interrogate first, then ask AI to challenge it from multiple angles.
Layer 4 (yellow, thin): AI Output v2+. Refined and pressure tested.
Layer 5 (orange, thick): Critical Readiness. Can you defend this without AI? If not, go back into the filling.
Footer: "AI is the cheese. It doesn't make the sandwich."

IT teams are about to become everyone's problem.

Most leaders haven't realised it yet.

At a hackathon last month, Debora Gallo shared an example that I haven't stopped thinking about. 

People teams are starting to build their own tools. 

Personalised, scrappy and iterative is exactly what the business needs. 

And IT's response was awesome, but we can't maintain that. 

I've seen this movie before. 

Years ago I was brought in as a tech consultant because a company's manufacturing engineering team had started building their own systems rather than waiting in the IT queue. 

They would "dev all the way to core systems" and then throw it over the fence to IT. 

IT was getting frustrated: 
→ Those systems needed to talk to central infrastructure. 
→ They needed someone to maintain them post development. 
→ They needed security sign-off. 

But nobody had planned for it because they went around IT.

They thought IT was too slow and always said no.  

And the whole thing became an expensive, political mess to untangle. 

That was considered an edge case back then. 

"Shadow IT" was the awkward anomaly everyone quietly complained about. In the age of vibe-coding, it becomes the default. 

And the People team won't be the only ones building. 

Finance, Ops, Marketing, Sales and any function with a problem and a bit of curiosity can now ship something. 

That's genuinely exciting! 

But it's also a structural time bomb if no one is asking the harder questions. 

What leaders need to start doing: 

1️⃣ Map your org design for a world where every function can build, not just request. 

2️⃣ Set the rules now: maintenance, governance and security can't be an afterthought when every team is shipping. 

3️⃣ Audit your future roles and skills. Agentic AI and vibe-coding are rewriting what every job actually requires. 

Keen to hear if you've been doing or observing any of the above. 

#aiadoption #orgdesign #futureroles

Black background with large white serif text reading "In organizations when everyone is vibe-coding......." with the words "vibe-coding" highlighted in purple, followed by "Who will maintain our apps?" with the word "maintain" highlighted in coral orange. Vanessa Monsequeira and Intentional AI Adoption appear in small text at the bottom left and right corners.

Are you team OpenAI or Anthropic?

For me, it's Anthropic for the win. 

I love:
🧡 Claude Chat
🧡 Claude Cowork
🧡 Claude Code (still WIP)

I appreciate a tool that doesn't optimise for dopamine.

No endless suggestions designed to keep you hooked.
(Worth a listen: Lenny's Podcast with Edwin Chen, CEO of Surge)

I support co-founders who:

a) Give away 80% of their personal wealth
b) Don't donate $25M to oppressive political regimes

I support a CEO (Dario Amodei) who writes real thought leadership:

→ The Adolescence of Technology
→ Machines of Loving Grace

Are any of these founders perfect? No
Is the wealth amassed insane? Yes

But the AI bubble isn't bursting anytime soon.

And what that means for employees is non-negotiable:
Upskill on tooling, critical thinking, and ethics.

In my opinion,  🧡 Anthropic for the win on all three.

Video credit: Reuters at AI Summit India

#aiwars #techbrodisputes #AI

Have you every attended a 'conference' that changes the way you think, learn and experiment?

For me that's been PX experiences by SapienX.

Every year I learn things from fellow speakers and peers that keeps me coming back. 

Every single time I've been able to put what I've learned into action immediately.

Key highlights:

🗓️ 2024 - London

➟ How to launch people products using best practices from marketing 
➟ UX to PX applications and north star and meaningful metrics 
➟ Deep dives into service design and the double diamond, and interactive icebreakers I still steal to this day. 
➟ Everyone comes to the game of work with the same set of cards but a different set of rules from past workplaces... and we expect everyone to play the same game 

🗓️ 2025 - London and Amsterdam

➟ PX in action for performance design 
➟ PX and service design deeper dives 
➟ Practical applications of agile for people teams 
➟ The culture card game and a touch of wizardry 
➟ Experiment Design canvas - integrating all our learnings into action 
➟ Purpose at work interactive session and preview of Jessica Zwaan's new book 
➟ The future of HR turning into the future of design of work, workforce, and workflows 


🗓️ 2026 What will we see next? 

Join us at PX Live to find out.

It's going to be a cracking line-up:

Alicia Grimes
Dart Lindsley
Glenn Martin
Chris Metcalfe
Luke O'Mahoney
Thomas Forstner
JooBee Yeow, PhD (OOO)
and me Vanessa Monsequeira - चित्रलेखा

If you're: 
👉🏽 A Head of People trying to drive real impact 
👉🏽 A PX leader building capability beyond admin 
👉🏽 Someone who believes the People function can be more strategic

come join us for the fun, to learn, and to be part of the movement to redesign the future of HR and work.

It's on March 20 and it's limited to 100 practitioners.

Discount code - VANESSA-10

#peopleexperience #pxlive

Promotional banner for PX Live London featuring a diverse audience smiling and seated at a live event. Overlaid text reads: “Work is broken. Product can fix it.” Event details: “PX Live — London | March 20th.” “Limited tickets available” appears alongside a QR code. Supported by hownow, Humaans, and huggg. Sapien logo in the top left corner.

⚡️ Newsletter & KI: 3 Dinge, die Du wissen musst. Sei dabei: Kostenlose Masterclass am 9.7.24 👇

I work with healthcare and life science companies as their Fractional Chief AI Officer: bridging boardroom strategy and hands-on implementation. 

No experiments. 
No buzzwords. 
Measurable results within weeks.
 

What I do:

• Identify where AI saves the most time and money in your specific business
• Build a prioritized AI roadmap with clear ROI
• Leading implementation – working with your team and trusted specialists to ensure it gets done
• Enable your people to work independently with AI
• Provide ongoing strategic sparring at C-level
 

Who I work with:

• PE portfolio companies scaling operations without adding headcount
• MedTech and pharma companies building AI capabilities
• Leadership teams who want strategy AND execution – not just slides
 
Past work: AI transformation programme for European pharma leadership. AI strategy workshops for financial services. Sales team AI enablement across life sciences.
 
Based in Munich. Working remotely across DACH and Europe.
 
Open to conversations about Fractional Chief AI Officer engagements, board advisory, and speaking.
 
→ karolynehahn.com

📩 [mail@karolynehahn.com] | 🌍 | 📞 [https://calendly.com/karolyne-hahn/20-min-zoom]

Karolyne Hahn

7 Free AI Courses for Healthcare Professionals.

Share this before your team spends money on expensive training:

1 – AI in Healthcare (Stanford University)
The gold standard. Free to audit on Coursera.
https://lnkd.in/diKZB5-a


2 – Generative AI for Healthcare (Google Cloud)
Specifically built for healthcare professionals.
Free to audit. Practical, not theoretical.
https://lnkd.in/d3twgWtQ


3 – AI for Medical Diagnosis (DeepLearning.AI)
For those who want to go deeper. Image analysis, diagnostics.
https://lnkd.in/dCQAS4K7


4 – How to Use ChatGPT in Healthcare (edX)
The most practical one. Day-to-day applications.
edx.org → search “ChatGPT in Healthcare”


5 – Elements of AI (University of Helsinki)
Best starting point for non-technical leaders.
Free. Certificate included.
elementsofai.com


6 – AI for Everyone (Andrew Ng / Coursera)
Not healthcare-specific but the best foundation.
Free to audit. Mandatory for any healthcare leader.
https://lnkd.in/dFhuxXZQ

7 – Fundamentals of Machine Learning for Healthcare (Stanford)
For those ready to go beyond ChatGPT.
https://lnkd.in/dtz8sWBM

Save this. 
Share with your team. 
Forward to your CEO.


#HealthcareAI #FreeLearning #MedTech #PharmaAI #AIEducation

10 AI tools healthcare teams can use for free right now. No hype. Only what actually works.

→ 1. Nabla Copilot - AI clinical documentation. 30 free consultations/month. nabla.com

→ 2. Glass.health - Differential diagnosis + ambient scribing. Free trial. glass.health

→ 3. DeepScribe - Voice-to-clinical-notes. Free trial available. deepscribe.ai

→ 4. TL;DV - Meeting documentation + transcription. Free tier. tldv.io 

→ 5. Perplexity AI - Medical literature research with citations. Free. perplexity.ai

→ 6. Claude.ai - Summarize regulatory documents, policies, reports. Free tier. claude.ai

→ 7. Elicit.org - Accelerate systematic reviews. Free for researchers.
elicit.com

→ 8. Consensus.app - Evidence-based answers from 220M+ papers. Free version.
consensus.app

→ 9. Synthesia - Create internal AI training videos. Free trial. synthesia.io

→ 10. Notion AI - Structure clinical SOPs and protocols. Integrated. notion.so/product/ai

Which of these have you already tested?
Save this and share with your team.

Who on your board owns this?
 
Share and save for your next board meeting.

Fractional Chief AI Officer | Healthcare & MedTech | Board Advisory | Speaker

If you’re reading this, you’re statistically in the top 0.04% of AI adoption on Earth.

You‘re not late. 
And no, AI is not a bubble. 
It might be your timeline tricking you.
Most people don’t know what Claude is.

Ps. The red dot at the bottom right corner are people that vibe code apps and tools from scratch (eg. using tools like Lovable, mostly without even knowing how to code).

Save and share it

Cc Ben Tannenbaum

I've been quiet on here for a while.
 
Honest reason: life happened. A difficult personal year that took most of my energy.
 
But I've used that time to get very clear on what I do – and who I do it for.
 
I work with healthcare and life science companies as their Fractional Chief AI Officer.
 
Not as a consultant who delivers a deck and disappears.

Not as a trainer who runs a workshop and moves on.
 
As someone who stays until AI actually works inside the organization.
 
I've seen too many companies go through the motions – workshops, strategies, pilots –
only to watch everything dissolve back into daily business three months later.
 
That's the problem I solve.
 
If you're leading a healthcare or MedTech company and AI hasn't stuck yet – I'd love to talk.
 
#FractionalCAIO #HealthcareAI #MedTech #AIStrategy #LifeSciences

I've worked with leadership teams across pharma and life sciences.
The pattern is always the same.
 
Workshops go well. Energy is high.
People see what AI can do for their specific pain points. 
(And there's always a solution – that part never fails to surprise them.)
 
Then the session ends.
 
Without top management commitment – a named owner, a protected budget, a clear mandate – everything quietly returns to business as usual within weeks.
 
AI doesn't fail because of the technology.
It fails because of organizational gravity.
 
The companies that get results are the ones where someone at the top has personally decided: 

This Is Happening.
 
Everything else is just noise.
 
Is your leadership team aligned on AI – or just interested?

 
#HealthcareAI #AILeadership #MedTech #FractionalCAIO #LifeSciences

No alternative text description for this image

The gap between 'having an AI strategy' and 'AI actually working' is the most expensive gap in healthcare operations right now.

An AI strategy that lives in a document 
is not a strategy.

It's a liability because it creates 
the illusion of progress.


Real AI strategy looks like this:

→ One named person responsible
→ One process changed per quarter
→ One metric to measure it
→ One honest review every 30 days

That's it. No fancy roadmap deck needed.


#HealthcareCEO #AIStrategy #MedTech #HealthcareAI #Leadership

The EU AI Act is officially in force.
Healthcare boards are panicking.
You shouldn't.

I read all 400+ pages so you don't have to.
Here are the 8 things every healthcare leader needs to know now:

1. High-Risk is the Default
Almost all clinical AI systems qualify.
This isn’t a "maybe" or a "borderline" case.
If it touches a patient, it's likely High-Risk.

2. Conformity Before Deployment
Assessment is mandatory before you go live.
Not after. No "grandfathering" in old systems.
Compliance starts at the design stage.

3. Human Oversight ≠ "A Doctor Looks at It"
"A human is in the loop" is a phrase, not a strategy.
You need documented protocols on how humans intervene.
If it’s not in writing, it didn’t happen.

4. Radical Transparency
You must disclose AI use to patients.
Trust is the new currency.
Transparency is the only way to earn it.

5. Validation is Never "Done"
One-time validation reports are dead.
Ongoing performance monitoring is the new standard.
AI evolves; your oversight must evolve faster.

6. Data Provenance is King
Where did the training data come from?
Is it representative? Did you test for bias?
Show your work. All of it.

7. The Clock is Ticking
- New systems: The time is now.
- Existing MDR/IVDR systems: You have until 2027–2029.
The runway is shorter than you think.

8. Compliance as a Competitive Edge
Don't view this as a hurdle. View it as a moat.
Organizations building for compliance now will lead the market in 2028.
Chaos is a ladder—if you have the right map.

The Hard Truth:
Most boards are waiting for a "compliance officer" to handle this.
That is a €35M mistake. (Or 7% of global turnover).

The EU AI Act isn't a legal checkbox.
It’s a fundamental shift in how healthcare tech survives.

The Question:
Who on your board actually owns the AI Risk Map?
If the answer is "no one," you're already behind.

💾 Save and share this post. You’ll need it when the 2027 deadline hits.

I've watched 4 pharma AI pilots fail in 2 years.

Same reason every time.

They got the anatomy wrong.

Here's what a pilot that actually scales looks like:

→ Start with pain, not technology 
"We lose X hours/week because of Y." 
No specific pain = no real use case.

→ Audit your data before you build anything 
Is your CRM 80%+ complete? 
Are HCP records deduplicated? 
If no: fix that first. AI won't save bad data.

→ Pick boring over ambitious 
Not the sexiest use case. 
The one your reps will actually use on Monday.

→ 4 people make or break the pilot 
1 champion. 1 data owner. 1 frontline user. 1 exec sponsor. 
Missing one = it dies quietly.

→ Define "working" on day 1 
Not "improve efficiency." 
But: "Pre-call prep under 15 min within 8 weeks." 
3 metrics. Measurable. Non-negotiable.

→ Week 1 is everything 
Let one rep generate one AI brief. 
Watch their face. 
That reaction is your internal case study.

→ 90 days. Then decide. 
2 of 3 metrics hit? Scale. 
If not? Diagnose. Re-pilot. 
Never just abandon.

The transformation doesn't start with the technology.

It starts with getting the anatomy right.

♻️ Save and repost if this is useful for someone building AI initiatives in pharma or medtech.

The Anatomy of a Pharma AI Pilot

Viele Vorstände behandeln AI wie ein Software-Update. Nett zu haben, aber delegierbar.

Falsch. 
AI entwickelt sich rasant zu einem reinen Board-Thema. 

Nicht wegen der Technik, sondern wegen der existenziellen Einschläge:

1. Angreifer brauchen keine 10 Jahre mehr, um den Markt abzunehmen. Sie brauchen nur ein besseres Modell und weniger Overhead.

2. Die aktuelle Wertschöpfung wird in Echtzeit entwertet. Was heute Marge bringt, ist morgen Freeware.

3. Wer "AI" nicht versteht, kann sie nicht führen. Wer sie nicht führt, haftet für die Konsequenzen.

Wer wartet, bis die IT-Abteilung antreibt und eine Lösung präsentiert, hat verloren.

Agree?

Two sentences are killing European healthcare.

They don’t sound like failures.
They sound like "careful leadership."
1. "That’s an IT decision."
2. "Global has to decide."

Both mean exactly the same thing:
„Not my problem.“


1. The Ownership Trap

When a CEO says "That’s an IT decision," they aren't being humble.

They are outsourcing the soul of the company.
• IT manages infrastructure.
• IT reduces risk.
• IT maintains systems.

IT was never designed to answer:
• What does AI mean for our workforce?
• Where is human judgment irreplaceable?
• What kind of company do we want to be in 2030?

Those aren't technical hurdles.
Those are leadership mandates.
The moment you hand them to IT, 
the vision dies in a support ticket.


2. The "Global" Mirage

I’ve seen organizations wait years 
for "Global" to approve a framework.
While you wait, your competitors move.

Understand this:
- Silence is a decision.
- Waiting is a decision.
- "Let’s see how it develops" is a decision.

You aren't being "strategic."
You are delegating ownership to nobody.


The Reality Check

AI isn't a tech stack. 
It's a leadership choice.

If you treat your future like a software update, don't be surprised when your organization crashes.

P.S. If your CEO’s first reaction to AI is "Ask IT," you’re not leading a company.
You're managing a museum.

Agree?

Die meisten Pharma-Vertriebler hassen ihr CRM. Sie pflegen Daten ein, um Ruhe vorm Chef zu haben, nicht um besser zu verkaufen. 

Das Ergebnis? Millionen Euro für Lizenzen (Veeva, Salesforce), aber null Impact auf die Verschreibungsquote.

Warum? Weil die Angst vor der DSGVO jede intelligente Nutzung blockiert.


Hier sind drei konkrete Hebel, wie AI das CRM rechtssicher zum Profit-Center macht:

Beispiel 1: Der "E-Mail-Clustering" Bot (Datenschutzkonform): 

Statt dass ein Mitarbeiter mühsam liest, welche Betreffzeile bei welchem Onkologen zieht, anonymisiert eine AI die Interaktionsdaten. 

Sie erkennt: „Ärzte in Unikliniken öffnen Studien-Links montags um 8 Uhr, niedergelassene Ärzte bevorzugen Video-Snippets am Mittwochabend.“

Der Witz: Die AI lernt das Muster, nicht den Namen. Der Datenschutz bleibt gewahrt, die Klickrate verdoppelt sich.


Beispiel 2: Voice-to-CRM (Effizienz): 

Der Außendienst verliert 20 % seiner Zeit mit Tippen. 

Die Lösung: Ein lokales, verschlüsseltes Sprachmodell. Der Mitarbeiter spricht das Besuchsresümee ins Handy. Die AI filtert medizinische Insights heraus, kategorisiert sie nach Compliance-Vorgaben (keine unzulässigen Heilversprechen!) und füttert das CRM.

Der Clou: Die AI fungiert als „Compliance-Filter“, bevor der Text überhaupt dauerhaft gespeichert wird.


Beispiel 3: Churn-Prediction für Apotheken: 

Die KI erkennt im CRM: „Apotheke X hat seit 3 Monaten keine Schulung mehr angefragt und die Bestellintervalle vergrößern sich.“

Die Aktion: Das CRM schickt nicht blind den Außendienst vorbei, sondern triggert automatisiert ein individualisiertes Fortbildungsangebot.

Die Realität: Datenschutz ist kein technisches Hindernis, sondern eine Frage der Architektur. Wer AI in das CRM integriert, statt sie davor auszusperren, macht aus einer Protokoll-Pflicht eine Wachstums-Maschine.

Hören wir auf, Daten zu sammeln. Fangen wir an, sie zu nutzen.

Agree?

A pharma leadership team.
3 half-day workshops.
One honest lesson.

I worked with a European pharma company's leadership team to identify where AI could solve their biggest operational pain points.

We went deep. Mapped processes. Prioritized opportunities.

The energy in the room was real.
By the end, they knew exactly which 5 areas AI could transform - and in what order to tackle them.

Then: the Managing Director left. 
Internal restructuring. 
Global compliance questions.
The roadmap sat in a folder.

The lesson wasn't that AI failed. The lesson was that strategy without implementation ownership is just an expensive workshop.

I don't deliver roadmaps. 
I stay until the change is real.
That's a different kind of engagement. 
And it's the only kind that works.

DM me if you're ready for that.

#PharmaAI #AIImplementation #HealthcareAI #ChangeManagement #FractionalCAIO

6 signs your AI initiative will fail by 2027.
Most companies aren't "innovating."
They are stalling.

If these 6 signs look familiar, you aren’t in an "evaluation phase."
You’re in a graveyard.


1. Zero Accountability

There is a team. A committee. A working group.
There is no single name.
If nobody’s career depends on it, nobody is working on it.


2. Vague Definitions

Bad: "Improve patient outcomes."
Good: "Reduce ICU documentation by 40% by Q3."

If success isn't one sentence with a number and a date, it’s a hobby.


3. Governance Theater

The last meeting produced a slide deck, not a decision.
Real governance says "No" more than it says "Maybe."
Everything else is just a coffee break.


4. Activity ≠ Outcomes

Your board sees reports on:
• Pilots completed.
• Workshops held.
• Vendors met.

They should be seeing:
• Cost per case.
• Hours saved.
• Errors caught.

Stop reporting on how busy you are. Start reporting on how much you saved.


5. The Ghost Sponsor

The project sponsor changed roles 8 months ago.
The initiative is still "running."
But nobody knows who is driving the bus.


6. The Regulation Excuse

"We’re waiting for the EU AI Act to settle."
Winners aren't waiting for permission.
They are building flexible governance that complies by design.


2027 is closer than you think.

In two years, you will either be a Case Study or a Cautionary Tale.

P.S. What’s the biggest bottleneck in your industry? 👇

There is no middle ground.

Last Friday, Donald Trump didn't just criticize Anthropic. He blacklisted them.

Effectively immediately: 
A "National Security Supply Chain Risk."

The Crime?
Anthropic refused to let the Pentagon use Claude for "unrestricted lethal operations."

They stood by their safety guardrails.
Washington responded with a total ban and threats of "criminal consequences."

If you are in Pharma, MedTech, or Healthcare, the "Safe AI" era is over.

You are caught in the crossfire of a Geopolitical War.


The 3 Brutal Realities for 2026:

1. Compliance is a Liability: If you have US federal contracts, your "safe" Claude-R&D stack is now a legal ticking time bomb.

2. OpenAI is the "State Model": Speed over safety. Military access over ethical guardrails. That is the new US mandate.

3. The Sovereign Pivot is Mandatory: If you don't own the weights and host them on your own servers, you don't own your research.

The Move:
Stop buying subscriptions.
Start building a fortress.
Own the model. Control the hardware.

The "Old World" follows the landlord.
The "New World" owns the house.

Where is your intellectual property tonight?

#PharmaAI #AnthropicBan #DigitalSovereignty #AIStrategy #TrumpAI 

Image cc evolving ai

The 6-Week AI Pilot Plan For Pharma Sales (Week By Week)
You want to run an AI pilot in your sales team.
Here's the exact 6-week plan:

Week 1: Diagnose before you build
Don't touch technology yet.
Interview 10 reps. 

Ask:
→ "Where do you lose the most time every week?"
→ "What information do you wish you had before every HCP call?"
→ "What does a great prep session look like vs. a bad one?"
You will hear the same 3 answers from almost everyone.

Those 3 answers are your use cases.

Week 2: Fix your data
Choose one use case from Week 1. 
Audit the data you need for it.

CRM completeness: are call notes filled in? 
Are HCPs deduplicated? 
Are territories current?

You cannot build good AI on bad data.
Don't skip this week. 
It's the most important one.

Week 3: Build the simplest possible version
MVP only. 
No integrations. 
No fancy dashboards.

If the use case is pre-call briefs:
→ Build a simple template
→ Have a team member manually compile it for 5 HCPs using existing data
→ Give it to 3 reps and watch them use it

If the brief helps them, you've proven the concept.
Now you automate.

Week 4: Automate and test
Connect your data sources. 
Generate the brief automatically.
Test it on 20 HCPs. 

Check accuracy. 
Fix errors.
Let reps give real-time feedback — what's missing, what's irrelevant.

Week 5: Measure obsessively
Three metrics only:
→ Time saved per rep per week (self-reported + CRM logs)
→ HCP meeting quality (rep-rated on 1–5 scale pre/post)
→ Call conversion rate (meeting request → accepted)
No other metrics matter at this stage.

Week 6: Decision and scale
If metrics improved: write up the results. 
Present to leadership. 
Get budget for Phase 2.

If metrics didn't improve: 
Go back to Week 1 with what you learned. 
Change the use case or the approach.
Don't kill the initiative because one use case didn't land.
Learn and iterate.

6 weeks. One use case. Real data.
That's how AI transformation actually starts.

Save and share 🙋🏻‍♀️

PE investors are sitting on an untapped value lever in their portfolio companies.

It's not a new product. 
Not a new market.
It's operational efficiency through AI.

Here's the math.

A healthcare company with 80 employees,
each losing 1.5 hours/day to tasks AI could handle:

→ 120 hours/day recovered
→ ~600 hours/week
→ Equivalent to 15 additional FTEs

→ At €60k average cost = €900k/year in recovered capacity
Without hiring a single person.

Most PE-backed healthcare companies haven't touched this yet.
Not because it's hard. 
Because nobody has made it a priority.

That's what a Fractional Chief AI Officer does for your portfolio.

One engagement. 
Measurable EBITDA impact. 
Scalable across companies.

Curious what this could look like for your portfolio? 
Let's talk.

#PrivateEquity #HealthcareAI #PortfolioValue #MedTech #FractionalCAIO

I used to tell clients AI would transform their medical affairs team.
I was wrong. 
AI doesn't transform medical affairs.
It gives back the 90 minutes before the real work starts.

The real work - medical judgment, HCP relationships, regulatory strategy - AI can't touch that.

What AI can do: structure a clinical study summary so your team spends 10 minutes reviewing instead of 90 minutes writing.
That's it. That's the honest promise.

Here's the prompt that delivers it. 

"You are a Senior Medical Affairs Manager preparing a briefing for a specialist physician. The physician has 3 minutes and is deciding whether this data changes their prescribing behaviour.

BEFORE YOU BEGIN: Identify and state: (1) study type - RCT, observational, meta-analysis, or single-arm (2) whether a comparator exists (3) study phase. Frame your entire summary through these three facts.

STUDY IN ONE LINE: Drug name, mechanism class, indication, population, phase, duration. One sentence. No interpretation.

HEADLINE NUMBER: Absolute risk reduction AND relative risk reduction. 
If only relative risk is reported - flag this explicitly. 
Relative-only reporting is a red flag.

SAFETY SIGNAL: Exact incidence rates. Discontinuation rate due to AEs. 
If no significant findings - state explicitly. Do not skip.

VS. STANDARD OF CARE: How does this compare to what the physician currently uses? If no head-to-head data exists - state explicitly. 
Do not imply comparison where none was made.

WHAT CHANGES IN PRACTICE: Would a physician rationally change a treatment decision based on this data alone? 
If yes - for which patient profile specifically? If no - say so.

STATISTICAL ROBUSTNESS: Sample size. P-value. Confidence interval. 
Flag if present: underpowered sample, wide confidence intervals, high dropout rate, per-protocol vs intention-to-treat discrepancy.

LIMITATIONS: Maximum 3. Prioritise what a regulator or payer would flag - not what the authors listed.

EXPERT REVIEW FLAGS: List every claim requiring medical-legal review before HCP use. Cite the claim - not just the section.

CONTRADICTIONS: If any data appears internally inconsistent - flag it. Do not smooth over contradictions.

Format: Bullet points only. No jargon. Max 260 words. Never speculate beyond the data. If insufficient data for any section: write exactly 'Insufficient data.' Do not assume."

Paste into your fav. AI, e.g. Claude, Copilot.
Upload any clinical study.

What took a medical writer 90 minutes now takes 3.

Save and share this prompt.

One honest warning:
AI still hallucinates in medical contexts. 

Especially:
→ Numbers from PDF tables: often extracted incorrectly 
→ Confidence intervals and p-values: sometimes invented 
→ Study population details: summarised instead of cited

#MedicalAffairs #PharmaAI #HealthcareAI

Anatomy of a Medical Affairs AI Prompt

Die eigentliche Herausforderung von AI ist nicht die Technologie.

Sie ist organisatorisch.

Denn AI verlangt von Unternehmen:

• schneller zu entscheiden
• stärker zu priorisieren
• transparenter zu führen
• mutiger Verantwortung zu übernehmen

Viele Organisationen sind darauf nicht vorbereitet.

Nicht, weil sie die Technologie nicht beherrschen.
Sondern weil ihre Entscheidungsstrukturen aus einer anderen Zeit stammen.

Die Unternehmen, die AI erfolgreich nutzen werden, sind nicht die mit der besten Technologie.

Sondern die mit starkem Leadership. 

Agree?

The most underused AI tool in healthcare companies right now:
Meeting intelligence.

Here's the setup that saves my clients hours every week:

1. Every internal meeting gets recorded (with consent, always)
2. AI tool (tldv , Jamie, or Microsoft Copilot ) auto-transcribes
3. After the meeting, AI generates:
  → Key decisions made
  → Action items with owners
  → Open questions
  → 3-sentence summary for stakeholders who weren't there

The meeting ends. 
The follow-up is already written.

In pharma and MedTech, where documentation is everything,
this alone saves a team lead 3–4 hours a week.

Setup time: 20 minutes.
ROI: immediate.

What's your current meeting documentation process?

#HealthcareAI #MeetingIntelligence #AITools #PharmaOps #MedTech

The most underused AI tool in healthcare companies

Most Pharma & MedTech AI projects fail.
Not because of bad technology.

Because of bad leadership decisions.
I’ve seen it across Medical Affairs, Commercial, and Regulatory:

→ Pilots running for 14 months with no exit decision
→ Vendors pitching IT instead of leadership
→ $2M spent. ROI undefined.
→ Clinicians consulted after the build. Not before.

So I built this.

A free cheat sheet for Pharma & MedTech leaders — with:

✅ Real use cases (Sanofi, Novartis, Parexel) with actual numbers
✅ What your team can do THIS WEEK — no budget needed
✅ Copy-paste prompts for Medical Affairs, Commercial & Regulatory
✅ EU AI Act obligations specific to your function

Save it. Share it with your team.

Follow me for daily AI insights -built for Pharma & MedTech leaders.

👇

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