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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

Die meisten Unternehmen scheitern nicht an AI, sondern an Führungsentscheidungen rund um AI.

Nicht, weil die Technologie unklar wäre, sondern weil sie drei unbequeme Punkte aufwirft:

1. AI zwingt zu strategischer Priorisierung.
Man kann nicht mehr „n bisschen experimentieren“.

2. AI verschiebt Machtstrukturen.
Daten, Entscheidungsbefugnisse und Verantwortlichkeiten verändern sich.

3. AI macht fehlende Leadership-Alignment sichtbar. Viele Führungsteams sind sich nicht wirklich einig, was strategisch zählt.


Deshalb ist AI kein IT-Thema, sondern ein Führungsthema.

Und gehört ins Board-Level.


Die entscheidende Frage ist nicht mehr:
„Sollen wir AI einsetzen?“

Sondern.. 

„Wo schafft AI echten Wettbewerbsvorteil - und wo nicht?“

Agree?

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

Nick Reynolds

Wavestone

Life Science business advisory with a focus on Customer Engagement, Patient Experience and Business Transformation following the acquisition of Pen Partnership by Wavestone.

Partner

Gameplan+ Ltd

Independent consultant support for strategic and analytic problem solving in areas such as customer experience, voice of the customer, innovation and data literacy

Director & Founder

Working for a specialist life sciences consulting firm, winning and leading projects across a variety of areas, including:
- Customer Experience Management and Implementation
- Voice of the Customer and Customer Analytics programs
- Data, analytics programs and capability development
- Innovation and experimentation
- Strategic program management

Associate Partner

Worked on specific projects supporting the Pen team with clients with specific focus on commercial effectiveness and customer experience/go-to-market activities,

Associate

Pfizer

Leading in-country strategic analytics and insights teams across the major developed markets

Senior Director, Head of Developed Markets for Analytics & Insights

Led the analytics and insights functions across Europe supporting Pfizer's Essential Health business, with a focus on digital transformation and commercial strategy and optimisation activities.

Senior Director, European Head of Analytics & Insights, Pfizer Essential Health

Led capability enablement for Business Analytics function of c350 colleagues globally. Two core aspects of the group's role:
Enabling through innovative tools, techniques and methods including customer insights, experimentation and digital marketing analytics.
Ways of working including consulting skills such as problem solving, communication and influencing.

Analytic Capabilities

Member of a small teamthat support senior leaders and program leads globally to de-risk their priority initiatives and increase the chance of successful implementation. This is done via workshops and further consultative support to develop agile execution plans. These plans enable thoughtful risk-taking & faster decision-making by identifying critical assumptions, building risk-mitigation maps & creating early learning plans to improve outcomes.

Part of small team tasked with developing enterprise-wide innovation and experimentation capability and culture to accelerate insight-based ideation through a rapid and low-cost test, learn and adapt methodology.

Director, Disciplined Experimentation

Supported strategic planning for Primary Care globally.

Two key activities in this role. the first was the development of a global digital marketing strategy for the business unit, working across multiple functions and geographies, with specific focus on global consolidation and the APAC region.

The second was a global trend analysis and following set of critical insights requiring action both at a global and regional level, which were taken up and became part of approach to 2013 planning.

Director of Strategic Planning, Primary Care

Head of Strategic Planning, Europe/Canada Primary Care

Manage team of strategic intelligence professionals and informatics function.
Key activities:
1. Resourcing and investment analysis
2. Development of new performance reporting system for regional teams for predictive analytics
3. Part of global competitive intelligence core team
4. Environmental and competitive landscaping
5. Designed and ran landscaping and strategy workshops across the region for brand and country teams
6. Built capability in strategy games across organization
7. Built competitive early warning system

Head of Strategic Intelligence, Europe/Canada

Made proposal to Australian Leadership for a new function to support strategic decision making. As a result the Competitive Intelligence function was formed. Key responsibilities included:
1. Team management and leadership 
2. Data Evolution & analysis
2.1. Development of "One Source of the Truth" information access process
2.2. Updating performance measurements across the organisation to deliver transparent, consistent, accurate and agreed measures of performance
2.3 Help develop new data sources
2.4. Promotional sensitivity and effectiveness analysis
2.5. Customer targeting and segmentation enhancement
3. Strategic monitoring and alerting process
3.1. Creation and lead on new environmental scanning process and methodology
3.2. Competitor scanning process
3.3. War Gaming analysis and key alert setting
3.4. Early warning measurement system
4. Strategic management and projects
4.1 Balanced Scorecard
4.2 Analysis lead on project looking at non-core products
4.3 Pfizer lead for the “Factbook” for a project looking at defence and growth strategies
4.4 Customer Understanding – looking at the broader customer universe & future landscape scenarios

Head of Competitive Intelligence (Pfizer Australia)

Analysis supporting the commercial decision making of Pfizer within Australia.  Work included promotional effectiveness and sensitivity analysis, competitor scanning, customer targeting enhancement, strategy development.

Senior Manager, Business Intelligence

I had the opportunity to go on secondment to HQ to work with the Planning and Business Development Group supporting the Global President of Human Health.  During my time there I worked as a business analyst leading and supporting on a number of projects, including portfolio gap analysis, pricing impacts analysis and a portfolio optimisation project for Africa region as well as supporting analysis for board reports and earnings releases.

Senior Manager, Planning & Business Development

I transferred to Pfizer Australia to continue my career development within Pfizer.  I had responsibility for Neuroscience and Pain (NS&P) market research.  In addition, I was responsible for Strategic market research and analytical support to deliver insight and opportunity across the portfolio, including sales force effectiveness analysis and restructure.

Key achievements include being nominated as 2006 Pfizer Australia Ambassador for my performance.

Senior Manager - Market Research

I joined to work in the Mental Health Team.  Key Achievements:
•Provision of a segmentation & profiling study for primary care physicians
•Development of a new forecast model
•Instrumental in the delivery of new positioning for the brand
•LOE modelling and part of generic taskforce
•Detailed sales force analysis, looking at how best to promote the brand

In June 2003 I moved to the Coxib team with responsibility for three brands.  Key achievements:
•Analysis of secondary care performance
•Share of Voice and impact of promotional changes on forecasts
•Market segmentation work in primary care
•Involvement in field force size and structure assessments
•Operational and strategic planning, including ROI modelling for programmes and market forecast modelling

Senior Market Researcher

Isis Research

I joined Isis Research to help set up and launch Decision Scripts Live (DSL), an initiations monitor.  This is a database based on the Jigsaw sales force effectiveness service.

As well as DSL, my role involved working as part of the Healthcare Intelligence Systems team, across the syndicated services they offered, including launch effectiveness tools and the sales force effectiveness monitor, Jigsaw.

DSL Manager

Ipsos-RSL

Ipsos-RSL had recently set up and were expanding their healthcare team.  I joined specifically to design and set up some new syndicated research services based on patients and general project management of multi-country studies.

CompuFile Limited

CompuFile supplied market research databases to the UK market.  These include NCTE, an initiations monitor and DIN-LINK, a longitudinal patient database.  My role was to run these on a day-to-day basis, delivering support on the software systems as well as the methodologies used and the general application of the databases and to provide analytical support to clients.

Senior Client Services Executive

My career has been focused on helping drive better decisions through data, with a passion for co-created and practical, implementable strategy.  I enjoy the challenge of making the complex simple, ensuring appropriate adaptive measurement plans support initiatives. 

I started my career with market research and analytics providers before spending 19 years holding roles of increasing seniority leading country, regional and global teams for a world-leading pharmaceutical company.  The focus has been on portfolio and corporate strategy, creating thought-provoking and challenging insights and leading large-scale change and capability programs.  
 
Driving teams to challenge the status quo is my approach, with the aim of helping identify organizational blind spots through better data-driven understanding. My career purpose is working with teams to make strategy more than theoretical through building data-driven adaptive and executable plans to maximize the chance of successful outcomes.

PRIMARY EXPERTISE
- Analytic capability development
- Adaptive strategy and planning
- Insight-led innovation
- Execution and change planning

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