Acquire and expand customers quicker, cheaper, and for longer | Coaches B2B scale-ups scale more efficiently and effectively through proven frameworks | Organizing the MarTech World Forum London 2024

If your board or investors ask simple revenue questions and three people disappear into Excel, we need to talk.

Your forecast sounds confident on Monday. By Thursday, reality disagrees, and no one can explain why.
You spend more time arguing about stages than coaching deals. 
Deals do not die in your CRM. They just quietly age out while everyone watches.

The heroics that got you here are now the bottleneck. You have 15 dashboards, forecast theatre instead of truth, and a revenue engine leaking cash at invisible handoff points.

You are not bad at this. The system is.

I help €20 to 100M B2B SaaS scale-ups turn scattered data and forecast theatre into a revenue engine you can actually steer. Within 90 days. Without a reorg, new tools, or hiring a full-time VP RevOps.

Your RevOps team owns the system when I leave. I bring the pattern. They keep it running.

We start with a fast 4-part maturity scan to find the real constraint. Then we install three structures:
→ One-page strategy linking goals to the programs that actually move pipeline, win rates, and NRR.
→ One Revenue Room where metrics, risks, and decisions live in one view. No more "marketing numbers" vs "sales numbers." Just the number.
→ One operating cadence that replaces updates with decisions. If we are not making decisions, we are just having expensive coffee.

When it works, you explain where pipeline is healthy, where it is leaking, and how you are fixing it in 10 minutes flat. You get the headspace to think 3 years ahead, not 3 hours behind.

I am Rutger Katz, Fractional VP of RevOps.
Most RevOps people grew up in the growth bubble. They know how to spend money. I spent 15 years architecting efficient growth, so I know how to stop wasting money.

At one tech company, I uncovered €1M in unseen upsell opportunities. 
At Relay42, I rebuilt onboarding and adoption to move NRR.

Stop drifting. Start steering.
Revenue Engine Checkup in Featured. 10 minutes.
Or DM me "CHECKUP" for the questions directly.

I also speak/podcast guest on strategic RevOps, neurodivergence in leadership, and scaling to 100M ARR. 
Check www.neontriforce.com/speaking for more

Rutger Katz

I hate lead magnets. I'm also terrible at sharing files.
 
I've spent a year collecting RevOps resources, talking to ChatGPT/Claude, and formulating proven frameworks I applied the last 15 years. I extracted that in a set of Claude skills specifically for RevOps and GTM work. 

Not generic prompts. 

Skills with real methodology baked in: SPICED qualification, forecasting frameworks, HubSpot implementation patterns, change management, crisis triage, GTM comp design.
 
25 skills. 8,333 lines. Free.
 
But the more interesting thing is how they work.
 
Most people use Claude with structured prompts. "Act as a senior RevOps leader. Here is the context. Here is what I need. Format it like this." Every single time.
 
Skills are different. You upload the file to a Claude Project once. Claude reads it as background knowledge. From that point, every conversation in that project runs on the skill automatically. Claude knows which frameworks to use, which questions to ask, what to watch out for.
 
You stop prompting. You just ask questions or give assignments.
 
↳ "Why is our forecast always wrong?" 
↳ "Help me build a change management plan for this CRM migration." 
↳ "Our pipeline coverage looks fine but we keep missing. Where's the leak?"
 
Claude responds like a specialist, not a generalist.
 
Six skills stayed private. Those have client diagnostic and proposal methodology in them that people pay for.
 
The other 25 are yours.
Free (for now).
 
Comment or DM IHateLeadMagnets 
(or something else so that I can see you want it).
Put your hand up in whatever way you like, I'll send you the ZIP.

AI-native revenue engines for €15–150M B2B SaaS | Fractional VP of RevOps | Replacing spreadsheets and gut feel with compounding systems in 90 days | Double Daughter Dad

A simple new direction turns into seven things RevOps now has to rebuild.
 
Sandy Robinson, M.Ed. put words to it in Pavilion's RevOps School this week. I've seen it across enough clients to know it's not an exception. It's the pattern.
 
Leadership makes a call. New segment. Going upmarket. ICP shift. Motion change from inbound to ABM. 

It sounds like one decision. A direction. A slide in a deck.
 
By the time it reaches RevOps, it's something else entirely.
 
↳ ICP changes. Every targeting criteria, every account list, every scoring model needs rethinking. 
↳ Process changes. Qualification methods built for the old motion don't fit the new one. Handoffs break. Playbooks are wrong. 
↳ Tooling changes. Sequences, automations, configurations. Nothing carries over cleanly. All of it needs rebuilding. 
↳ Data definitions change. What does qualified mean now? What's a good fit? What does the funnel even look like?
 
Nedap is a good example of the opposite. They understand the size of what they're taking on. They want to map it properly before they start moving. That's not caution. That's how you avoid getting buried.

They scoped change across technology, data, process, upskilling, kpis and more. A big program, but it will stick.
 
The younger scale-ups I've worked with often hit this mid-execution. Three months in, RevOps is buried. Not because they're slow. Because nobody showed them what was coming.
 
The fix isn't complicated. Bring RevOps into the strategic conversation before the announcement. Not after. Not to slow it down. To map the avalanche while there's still time to prepare.
 
One decision at the top. Everything moves underneath. They should probably know it's coming.

I stopped writing long AI prompts. Built skills instead. The output quality jumped.

Next week I'm releasing a free GTM skill set for Claude. The skill carries the structure, the frameworks, the context. You describe what you need. It already knows how to think about it.

The RevOps set I built:
↳ RevOps Strategy
↳ RevOps Diagnostic
↳ RevOps Crisis
↳ RevOps Change Management
↳ RevOps Data Governance
↳ RevOps Forecasting
↳ RevOps Four-Capability Maturity Assessment
↳ RevOps HubSpot
↳ RevOps Metrics
↳ RevOps Tech Stack
↳ Revenue Operating Cadence
↳ CS Operations

Plus curated sales and marketing skills — GTM planning, compensation, sales methodology, marketing operations — so you have the full picture, not just one function.

No prompt engineering. The expertise is baked in.

There aren't any freely available RevOps skill sets out there right now. Feels like the right moment to change that.


Other things compounding quietly this week:

↳ 22 push-ups this yesterday. Was grinding through 10 a week ago. 1% improvements are invisible until they aren't.

↳ 60 sign-ups for the next Scale Up with HubSpot event. We were bracing to scrape to 50. Keep making the thing better, the numbers follow. Same principle, different context.

↳ EU roadshow conversations starting. Small, intimate events by design. Quality exchange only happens in a room where everyone knows everyone's name. The Dutch say massa is kassa. We respectfully disagree.

↳ If you're a sponsor whose ears just twitched, my DMs are open.


Drop a comment below if you want a heads-up when the skills drop next week. I'll come find you.

Governance is the most boring word in RevOps.
 
It is also the one thing that separates strategic partners from ticket-takers.
 
Everyone wants RevOps to be strategic. Few companies give RevOps the governance, decision rights, and process ownership that make strategic work possible.
 
I sat down with Jeff Ignacio on The RevOps Review Podcast to talk about what that shift actually looks like in practice.
 
We covered:
 
↳ What I think Winning by Design gets right about how revenue should work 
↳ Why governance and clear decision-making matter more as you scale 
↳ The real blockers when RevOps tries to move from tactical to strategic 
↳ Why customer adoption is a revenue conversation, not just a CS conversation 
↳ My Revenue Ops Maturity Model and where most scale-ups honestly sit on it
 
21 minutes. And we managed to talk about revenue operations without mentioning AI once.
 
Funny how much there is to sort out before the robots take over.
 
Link in comments.

Two companies deployed AI in their GTM. Same tools. 
One grew revenue per seller by 61%. 
The other lost 26%.

The difference wasn't the technology.

The 2026 Fullcast × Pavilion benchmark just made this painfully clear across 361,000 opportunities and 316 companies.

Group one focused on leverage. They closed fewer deals, moved upmarket, and used AI to sharpen qualification, routing, and deal prioritisation. 
R͟e͟s͟u͟l͟t͟: revenue per seller up 61%. Average deal size up 44%.

Group two focused on volume. They hired 38% more reps, flooded the top of funnel, and used AI to scale outreach and activity. 
R͟e͟s͟u͟l͟t͟: pipeline grew 72%. But revenue per seller dropped 26%. And deal sizes shrank.

Both groups adopted AI. Only one built the operating system to make it work.

And by operating system I don't mean software. I mean the decisions baked into how you run revenue: who you target, how deals qualify, what triggers action, and what gets ignored.

This is the pattern I keep seeing at scale-ups between €15M and €150M. The instinct when growth slows is to add volume: more reps, more pipe, more tools. The data (and my experience) says the opposite works: fewer, better-qualified deals with rules that enforce quality at every stage.
AI is an accelerant. It doesn't care what it accelerates.

→ Precise ICP, enforced stage gates, signal-based routing? AI compounds that.
→ Fuzzy targeting, no governance, activity-based KPIs? AI compounds that too. Faster.

Before you buy the next tool, ask one uncomfortable question:

Are the decisions underneath it worth accelerating?

📊 Data: Fullcast × Pavilion 2026 GTM Benchmark Report
h/t Guy Rubin

The worst feeling in SaaS:
 
Huge pipeline. Missed target. Again.
 
Here's the pattern I see with every founder past €5M ARR:
 
They're targeting €5M this quarter. 
They've got €20M in pipeline. 
They're converting 20%.
 
That's €4M. They miss.
 
So what's the reflex?
 
"We need more pipeline."
 
I've watched founders double down on this for 6 months straight. 
More outbound. More content. More events. 
The CRM looks like a crime scene. 
The sales team is drowning in demos they'll never close.
 
And conversion stays flat at 20%.
 
Because if your conversion is broken, more pipeline just means you're burning CAC faster on deals you'll never win.
 
It's like hiring more waiters when the kitchen's on fire. Everyone's very busy. Nothing's getting served.
 
𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀:
 
𝗙𝗶𝗿𝘀𝘁, 𝗱𝗲𝗳𝗹𝗮𝘁𝗲 𝘁𝗵𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲.
 
That €20M is contaminated. Zombie deals. Wrong-fit ICPs. Opportunities your reps keep "nurturing" because killing feels like losing.
 
Cut it to €15M. Same target. Less noise.
 
Now you're converting 27% instead of 20%. Same €4M. Way less waste.
 
𝗧𝗵𝗲𝗻 𝗽𝘂𝘀𝗵 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝟲 𝗽𝗼𝗶𝗻𝘁𝘀.
 
Fix the handoffs. Sharpen the enablement. Give people clear next actions instead of dashboards they never open.
 
33% of €15M = €5M.
 
Target hit. Zero extra pipeline required.
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗥𝗲𝘃𝗢𝗽𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼𝗲𝘀. 
Not dashboards. Not Salesforce janitor work.
 
It turns pipeline chaos into a system that compounds.
 
Most founders skip it. They hire GTM Engineers and Clay gurus instead. Then wonder why two quarters later they're still missing the target with twice the CAC.
 
Same pattern every time:
 
They think it's a pipeline problem. It's a conversion problem. 
They think it's a people problem. It's a system problem.
 
If you're past €5M ARR and this feels familiar, there's a 10-minute diagnostic in my featured section Rutger Katz.
 
It shows you where your revenue is actually leaking and what not to touch yet.
 
If it's worth going deeper, we book 45 minutes and make it practical.

Move pipeline is not the answer, RevOps is

Most revenue architecture rollouts plateau at the same point.
 
Teams are enthusiastic. Bowtie gets mapped. SPICED gets trained. Early wins show up.
 
Then it stalls. Quietly.
 
It's almost never the framework.
 
What's missing sits underneath it. Two layers and one rare resource that most companies leave completely unprotected.
 
↳ Swipe through for the full breakdown.
 
The short version: Governance protects the investment. Enablement makes the new way of working stick. Champions carry both.
 
I've seen what happens when you lose them. Eight people driving a platform rollout at a bank. Strategy shifted. All eight scattered overnight. Two years of work, gone in a week.
 
If you're rolling out revenue architecture or any operating model across multiple teams, one question before you start:
 
What system protects this when priorities shift?
 
If you can't answer that, you're hoping it lands. And hope doesn't scale.

Change fails without governance and enablement

Suffering from success. I hate lead magnets. Made one anyway. Then spent an evening emailing .zip files to strangers.
 
Last week I posted "25 Claude Skills for RevOps & GTM." Built them because they didn't exist and I wanted them for client work. Posted them because other people probably need them too.
 
About 50 people responded.
 
LinkedIn doesn't allow .zip files in DMs. So I did the only logical thing: emailed them individually.
 
This morning, I built a resource page on Lovable. Took 30 minutes. Drop your email, get the files. I can add every free resource I build going forward.
 
One follow-up email goes out a week later: "Did any of it stick?"
 
Not to sell. To learn. The skills only get better if I know what's broken.
 
I build revenue systems for companies stuck doing things manually. Apparently, I needed a LinkedIn limitation to apply that to myself.
 
Link in first comment (which LinkedIn probably buries).

I was a terrible corporate employee.
 
Held back in school, not because I couldn't do it, but because it bored me. Tried five different study programmes before finishing. Six companies in 12 years (low for most salespeople). Failed spectacularly at project management. Frustrated every manager I worked for.
 
My brain has one setting: obsessed or checked out.
 
ADD means I can't tolerate noise. Broken processes, meetings without purpose, misaligned KPIs... My brain physically rejects them. In corporate that made me "difficult." In consulting, it became a diagnostic instrument.
 
Every role taught me what a broken system looks like from the inside:
 
→ At a marketing consultancy, five disciplines refused to collaborate because each had separate KPIs 
→ At a logistics company, three leadership changes produced zero improvement because the company's operating system never changed 
→ At a 100-person consultancy, I watched brilliant frameworks die because nobody installed the cadence to make them stick
 
I didn't collect these experiences on purpose. But now I walk into a company and within two weeks the patterns are screaming. Not because I'm brilliant (but it does help 😉).
 
Because I've been inside six versions of the same broken system.
 
What looked like a messy career was actually pattern recognition in training.
 
If you've ever felt like the wrong fit for the system, maybe the system was wrong, not you.

P.S. Guys, it's Women's Day, so grab a bouquet of flowers for your wife and shower her with love

Three people will describe your sales process. None of them are right.
 
The CEO describes the one they designed two years ago. 
The VP describes the one they trained on last quarter. 
The rep describes the one they follow when their manager is watching.
 
Ask what actually happens when push comes to shove: last week of the quarter, big deal on the line, pipeline review in two hours, and you get a fourth answer. From nobody, because nobody has looked.
 
I spend my first week sitting on sales calls. Not to understand the product. To see what reps actually do when it matters.
 
Then I open HubSpot.
 
Deals stuck in the same stage for 60 days. Missing qualification fields. Stages that do not reflect the conversation that actually happened. You can see the gap between what the process says and what people do. Right there in the deal records.
 
That gap is where revenue leaks. Not in the strategy deck. Not in the methodology training. In the space between what leadership thinks is happening and what reps actually do under pressure.
 
The first 90 days are not about building something new.
 
They are about making visible what is already there, standardising what works, and removing what does not.
 
Most companies try to fix what they think is broken. The ones that improve fix what is actually broken. That requires seeing it first.

Your pipeline just shrank. Congratulations. That is progress.
 
First thing I do in any engagement: kill deals. Zombie opportunities that have not moved in three months. Prospects who went dark after the demo. Deals sitting in late stage because nobody wanted to close them out and watch the coverage number drop.
 
Strip those out and the pipeline is €3M lighter.
 
The CRO panics. 


Understandable.
 
But here is what actually changed. The forecast is now honest. The conversion rate now reflects reality. The team stops spending cycles on deals that were never going to close and starts focusing on the ones that might.
 
Nobody asks about the ROI of that.
 
They ask about the ROI of the system, the process, the engagement. Fair question. But the ROI is not always a bigger number. Sometimes it is a smaller number you can actually trust.
 
Zombie deals made your pipeline look healthy right up until you missed. They made your conversion rate meaningless, your forecast unreliable, and your team's effort invisible, because effort spread across dead deals looks like activity without results.
 
Killing them does not cost you revenue. It just removes the illusion of it.
 
It gets worse though...


 
Strip out the zombies and you might discover your pipeline is not just dishonest. It is too small.
 
You were not over-covered. You were under-covered and did not know it because the dead deals were filling the gap.
 
Now you have a different problem to solve. A real one.
 
That is still progress. You cannot fix what you cannot see.

Helping healthcare use the power of AI⚕️

With a childhood filled with hospital visits, I knew I would be a part of improving healthcare one day.

From seeing the fragile human life, to how much the treatment will influence the future.

My dad's car accident marked my life, not physically, but mentally.

As a witness to my dad's struggle, I saw the realities of healthcare. Its triumphs and its failures.

My glimmer of hope for healthcare lies in innovation, specifically the transformative power of AI.

The hope has evolved to become my passion.

A passion that makes sure:
Patients get the best care out there
Clinicians can focus on their patients
Peace of mind for caregivers

At the bottom line, is dignity for every individual.

In order to do so, healthcare as is needs to change.

Technology is the only feasible way to truly change healthcare as is.

A future where AI in healthcare is not just a possibility.

It's a necessity.

Knowing what to change though, can be difficult.

Should you be following the old ways of:
Prioritizing aesthetics over functionality
Making small tweaks ignoring core issues
Inefficient exchange of information

AI can create true value and impact in healthcare.

Interested in working towards a clear vision of AI for your organization?

Contact me to plan a call (sigrid@eirhealth.ai)

Sigrid Berge van Rooijen

Think AI in healthcare is a hype?

Here are ten ways how AI is already being used healthcare,
from consultations to surgeries.

But how can you actually use AI?

Example 1 - Predictive Analytics Incorporation.
↳ AI analyzes patient data for better health predictions.

Example 2 - Personalized Care Delivery.
↳ AI customizes treatment plans based on individual patient data.

Example 3 - Streamline Healthcare Efficiency.
↳ AI refines administrative and clinical workflows in healthcare.

Example 4 - Enable Remote Monitoring.
↳ AI empowers devices to monitor patients’ adherence to treatment plans.

Example 5 - Offer Virtual Assistance.
↳ AI provides personalized virtual health assistance for questions, support, and reminders.

Example 6 - Digital Consultations.
↳ AI improves patient-doctor communication via digital consultations.

Example 7 - Image Analysis
↳ AI can be used in medical imaging for image analysis and disease detection.

Example 8 - Clinical Documentation
↳ AI can be used to automate documentation, summarization and real-time data entry.

Example 9 - Triage Tool
↳ AI supports clinicians in prioritizing, managing and coordinating patient care.

Example 10 - Surgical Precision
↳ AI can provide real-time guidance for surgeons during surgery, automating parts of the surgery and predicting potential issues.  

AI has great potential in healthcare, from fraud prevention to access to care in underdeveloped nations, and even GenAI in MedTech. 

It's time we start taking it’s potential seriously to improve health outcomes globally.

What do you need to take the next step?

What if your AI tool is brilliant, but illegal?

Without knowing regulatory requirements,
even the smartest AI can become a liability instead of a lifesaver.

Healthcare organizations risk deploying AI solutions that compromise patient safety and violate regulations.

Regulation is not only for medical device companies.
Healthcare organizations also need to understand the basics.

Understanding the Medical Device Regulation (MDR) and EU AI Act is equally crucial for AI implementation because they provide the framework for responsible, ethical, and safe use.

Especially when talking tools that impact patients. 

How can you expect medical device companies to follow strict regulatory processes,
but when you go to healthcare organizations it’s the wild west.

Here are some reasons why you should not ignore these regulations:
1) Healthcare organizations need to verify that clinical AI tools are CE-marked and clinically evaluated.

2) The EU AI Act pushes towards transparency, human oversight, and bias mitigation, also for healthcare organizations.

3) Continuous risk assessment and mitigation are mandatory, making sure that AI tools remain safe and effective.

4) Responsibilities in incident reporting and data governance.

5) Need for competent users and human oversight to ensure safe device operation.

6) Users need to be trained to safely and effectively operate AI-enabled medical devices or software.

Healthcare organizations need to understand the intended use of tools. 
Only when that is done, can they define the implications on clinical practice. 
And patients. 

Even though many tools don’t impact patients, the tools would still fall under EU AI Act. 
And obligations would be different than from MDR. 

It’s hard to keep track,
But that is not a reason to ignore. 
Healthcare organizations need to take their responsibilities.
And if the organizations deploying tools ignore regulations, 
Can we really expect health AI tools to be safe to the patients?

How do you keep track if a tool falls under MDR or not?

This could rewrite healthcare. 
And you cannot afford ignoring it.

This one legislation makes me proud of being European. 
(And not just Norwegian, doing amazing in the Olympics)

Not only will it reshape patient care.
It will also impact innovation.
And data control across Europe. 
Who will access your data, but also how the data will be used.

Understanding EHDS requirements now
Means you won’t scramble later to
Comply with cross-border health data exchange and interoperability standards.

In one sentence, EHDS is
a framework for the use and exchange of health data.

For primary and secondary use.

Designed to provide a trustworthy setting for data exchange.

Aligned with EU data strategy and regulations.

With expected benefits for both primary and secondary use.
Patients and healthcare professionals alike.
But also for regulators, policymakers
Innovators and research

Implementation will be a gradual process:

𝗠𝗮𝗿𝗰𝗵 26, 2027

Member states:
• Appoint representatives to the EHDS board
• Designate or establish authority responsible for overseeing compliance
• Cross-boarder digital health services and infrastructures must be operational

European Commission:
• Publish common specifications for interoperability, security, and EHR logging
• Set up digital testing environments to verify compliance of EHR systems

𝗠𝗮𝗿𝗰𝗵 26, 2029

EHR Systems:
Comply with EHDS standards for priority categories of personal electronic health data, such as:
• Patient summaries
• E-prescriptions
• Laboratory results

Healthcare Providers:
- Ensure that all new and updated EHR systems conform to interoperability standards
- Support secure patient access
- Allow cross-boarder sharing

Data Holders:
Provide structured access to priority health data categories.

𝗠𝗮𝗿𝗰𝗵 26, 2031
Hospitals & Clinics:
Fully integrate EHDS-compliant systems and provide all required data categories.

EHR Systems:
Ensure full technical compliance and obtain required certifications.

Member States:
Enforce compliance and update national regulations to align with EHDS.

Additional Health Categories:
• Hospital discharge reports
• Medical imaging
• Genomic data

26 𝗠𝗮𝗿𝗰𝗵, 2035
All stakeholders need to ensure cybersecurity compliance.

AI & HealthTech Companies:
Align digital health applications with EHDS standards.

Regulatory Authorities:
Conduct full-scale audits and enforce penalties for non-compliance.

The plan is ambitious. 
And it is not perfect.
But if we continue to accept status-quo, we won’t get anywhere.

How are you preparing for the EHDS? And what implications do you see for your organization?

EHDS

More than 500 000 hospitals admissions in the EU could have been prevented. 

In 2023 alone.

Costing almost €30 billion. Per year.

Avoidable admissions are declining, but still an issue. 

According to OECD, 473 of 100 000 hospital admissions are avoidable. 
That’s 0,5% of all hospital admissions. 
Avoidable. 

Countries with most avoidable hospital admissions?
Lithuania
Germany
Poland
United States
Slovakia

AI could significantly play a part in reducing these preventable hospital admissions.

With the high and highest-risk patients, AI reduced hospital admissions with 27%.

AI in healthcare is becoming more essential for reducing hospital admissions.

Here are 12 ways AI could be a crucial part in reducing hospital admissions:

1) Predictive Analytics

1. Risk Stratification: AI identifies patient at high risk of readmission, allowing for targeted interventions.
2. Early Warning Systems: AI used to monitor vital signs and calculate early warning scores, helping detect patient deterioration before it becomes critical.
3. Outbreak Prediction: AI can analyze large datasets to predict disease outbreaks, promoting proactive measures.

2) Enhanced Care Coordination

1. Transition Management: AI helps manage care transitions effectively, from admission to discharge, ensuring patients receive appropriate care at each stage.
2. Real-time Monitoring: Wearable devices paired with AI algorithms monitor vital signs continuously, alerting providers to anomalies.
3. Remote Patient Monitoring: AI enables effective remote monitoring of patients post-discharge, allowing for timely interventions when needed.

3) Clinical Decision Support

1. Treatment Recommendations: AI analyzes patient data to suggest personalized treatment options, potentially preventing unnecessary admissions.
2. Diagnostic Assistance: AI-powered diagnostic tools can help identify conditions early, reducing the need for emergency admissions.
3. Drug Interaction Predictions: AI accelerates drug discovery and predicts potential drug interactions, helping prevent adverse events that could lead to hospitalization.

4) Patient Empowerment

1. Self-Triage Tools: AI-powered symptom checkers guide patients to appropriate care levels, potentially avoiding unnecessary doctor and hospital visits.
2. Health Education: AI can provide personalized health information to patients, improving self-management of chronic conditions.
3. Behavioral Nudges: AI can send timely reminders and encouragement to patients, promoting healthier behaviors that reduce hospitalization risk.

What role do you think AI will have in reducing hospital admissions?


______
Read more about AI in proactive care management here: https://lnkd.in/eCt5c5E2

Will health.tech | global summit move beyond the glorified picture of AI?
And will it bring needed voices to the scene?

Next week I’ll be in Basel for the Summit.

I am hopeful it isn’t “just another conference”. 
Since the conference is getting different stakeholders in the same room. 
All trying to answer hard questions, but from different angles:
How do we move from AI pilots to AI that is safely used in everyday care?
How do we design incentives and infrastructure so the right patients actually get the right treatment at the right time?

I’m especially interested in three angles this year:
- Innovative Solutions: What new developments are there to modernizing healthcare, not just more tools, but tools that actually reduce cognitive load and documentation burden.

- Preventive and personalised care: using data and AI to avoid avoidable admissions and missed treatment opportunities, instead of just optimising what happens after a patient is already in crisis.

- Governance in practice: the implications of MDR, the EU AI Act and the EHDS in healthcare organizations, startups, and pharma, and how they are really shaping roadmaps.

At the same time, I’m going in with a few question marks:
- We still talk a lot about “scaling AI” and far less about de‑implementation: what will we stop doing to make space for the new?
- Patient and frontline clinician voices are often underrepresented in strategic discussions. I’m curious if and how meaningfully they are brought into the conversation.
- And I’m curious how much of the conversation will move beyond shiny use cases to boring (but critical) topics like procurement criteria, validation in real‑world workflows and long‑term maintenance.

If you’ll be there and you’re working on:
- Innovative solutions that genuinely reduce workload for clinicians or improve quality of life for patients
- Getting your organizations ready for AI implementation
- Choosing Health AI tools for your organization

…then I’d love to connect for a coffee or a short walking meeting.

Basel next week. Hope we’ll see how close we can get to AI that is not just possible, but practical, safe and actually used.

Will you give me a heads-up who I should meet up with?

Is your organizations choosing the wrong AI tool?

Or skipping essential steps in the decision process?

If you want positive impact from AI tools, 
You don’t want to skip these steps. 
Because skipping them, will only lead to problems for later. 

You want to be sure that you are fixing a problem that needs a fix. 
You want to know that the tool works for your specific setting. 
You also want to make sure that your organization is ready, from people, processes and platforms. 

This is what I support organizations with, 
But I don’t want to keep it to myself. 
So I decided to create a cheat sheet for you,
To make sure you’re selecting and implementing AI tools effectively.

1. Identify the Problem & Define the Objective
Understand the problem or process that you want to improve with AI.
It could be anything from disease diagnosis to patient care.

Clearly define what you want to achieve with AI.
It could be improving patient outcomes, reducing healthcare costs, or enhancing medical research.

2. Assess Organizational Readiness:
Identify barriers to AI adoption and ensure necessary resource allocation to support successful integration.
People & Culture
Governance & Ethics
Strategy & Business Value
Data Readiness
Infrastructure & Technology

3. Select AI Tool:
Choose AI tools aligning with the identified problem and organizational capabilities.

Assess tools based on:
Clinical Relevance
Technical Compatibility
Scalability
Vendor Support
Vendor Credibility
Bias & Fairness

4. Validate with Internal Data:
Test the selected AI tool using your organization's data to ensure it performs well in your specific context.

Representation - Dataset that represents patient demographics
Errors - Analysis for limitations and failures
Performance - Stress testing for performance
Acceptance - Acceptance testing with clinicians and other staff
Benchmarks - Compare with human expert benchmarks

5. Implement & Integrate Tool:
Deploy the validated AI tool across the organization,
integrating it into clinical workflows.
Start small before rolling out.

Provide training, and integrate into existing decision pathways.

6. Monitor and Update the AI Model with New Data:
AI is not a one-time process.
You need to continuously monitor the model and update it if necessary.

Performance metrics
Performance over time
Data quality & Input monitoring
Security & Access
Safety, bias & fairness
Usage & Workflow integration

How are you selecting new tools for your organization?

And if you do need support - drop me a message to see how I can help.

Your Clinical Data is Worthless

And you have been lied to believe that your data is the new gold.

If you can’t get answers from your data, is it really worth anything?

Too often, we’ve been told that data is the most valuable asset.
But the truth is:
unless you know how to turn it into actionable insights, it’s just noise.

Insights, not raw data, 
are what drive better decisions, 
improved outcomes, 
and ultimately, save lives.

But getting insights from your data is not that straight forward.

You would need to:

• Digitize your records
• Standardize formats across systems
• Clean your data, removing duplicates, errors, and bias
• Structure and connect data sources
• Ensure data privacy and security

Insights in healthcare have a lot of potential.

Imagine if you could use healthcare data for:
- Visibility into patient care metrics
- Improve operational efficiency
- Identify high-risk patients
- Forecasting patient loads and staff need
- Improving patient satisfaction and quality of life

An easy way to get insights from data would be having data dashboards.

I have seen this firsthand at hospitals.

Dashboards on:
Waiting lists per department
Number of planned appointments
Patient stay duration
Occupied hospital beds

Simple insights that can help improve quality of care.
Giving an overview of what is happening in the organization.

And not just operational metrics,
they are the difference between a patient waiting weeks or days for treatment.

Data can be valuable, yes, but only if you manage to get insights from it.

And until the data is ready, your organization will also not benefit from true AI insights. 

What are you doing to increase insights from your data?

Healthcare organizations are skipping this important step with AI

The step that will make or break the success of your AI project

If you want successful AI initiatives, you need to prepare. 

Healthcare needs more than just good algorithms to transform healthcare. 

Readiness makes sure:
- Staff is motivated and trained
- Infrastructure supports AI
- The culture embraces change
- Investments don't fail

There are three pillars of readiness:
Psychological readiness: Are people willing and motivated to change?
Structural readiness: Are resources, data, and IT systems in place?
Cultural readiness: Does organization’s culture support innovation and learning?

These need to be assessed and common barriers identified, before implementation. 

Common barriers are:
- Lack of clear vision or strategy for AI
- Insufficient IT infrastructure and data quality
- Staff resistance or fear of job loss
- Regulatory and ethical uncertainties
- Limited funding and expertise

Organizational readiness can be built, by creating a positive environment where people feel safe and supported to embrace AI. 

Open discussions around fears should be encouraged. 
And leadership should encourage change while supporting staff.
And, of course, IT systems and data should be ready before any implementation. 

Guide your organization step-by-step. 
Define goals
Build multidisciplinary teams
Align strategy with regulatory requirements
Plan testing, training, and gradual rollout

What have you done to ensure your organization is ready for the AI wave?

_____

Curious about AI for your organization?
Reach out to hear how we can help.

Organizational readiness

The impact of biased data in healthcare is growing.
But not many are talking about it.

AI tools are not 100% accurate. 
Especially not general GenAIs. 
But we accept the lower accuracy. Even with our health.

Yesterday my grandpa-in-law told me he uses AI.
He’s not alone.

According to some numbers, today, over 1 billion people use AI tools each month.

People who use AI for health-related questions, 
need to understand that most general AI tools 
have not been validated by real healthcare professionals. 
Especially not for every speciality. 

The AI tools are pushing health use-cases. 
And how these will impact real people will also have an effect on trust. 

Not only trust for general GenAIs, but also trust for all AI tools. 

GenAI is everywhere in healthcare now. 
But where does it get its information? 
How is that information checked?

One should ignore these questions.
Because the wrong output could negatively impact people.

There are too many examples of this. 
In healthcare, but also outside of healthcare. 
Wrong diagnosis, 
Wrong treatment, 
Signals ignored. 

Though there are also ways GenAI can make healthcare more accessible. 
We cannot ignore that.

Many hospitals are already using AI for diagnosis and monitoring.

And whether or not healthcare organizations have deployed AI, the clinicians are familiar with available AI tools.
Easy access from their private devices.

The scale is huge.

And I don’t know a way to check whether the tools are used and how they are used.

Imagine, in a healthcare setting, both patients and healthcare professionals alike have access to the same tools. 
Can the organization fully block the tools? 

And what impact will the tools have if AI learns from bad or unchecked data?

We need transparency.
We need to understand where the AI’s health info comes from.
We need to know if clinicians were involved?

Who uses the tools?
And how are they used?

In healthcare, accuracy is life or death.

Healthcare organizations need to start to ask tough questions. 
Is the output validated?
How is it validated?
Can we trust its recommendations?

If AI spreads errors, it risks public health and trust.
Not only impacting GenAI tools, but future innovation.

We need to build AI systems on verified data and expert oversight. No shortcuts.
But also understand who and how tools are used.

The future of healthcare and innovation depends on it.

How can we make sure innovation can be deployed in healthcare in a safe way, protecting patients?

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The Battle

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