It’s often said that most clinical trials don’t fail because the drug doesn’t work, but because it doesn’t work in enough of the people chosen for the study. In the same way, most ideas in life sciences don’t fail because the science is weak – they fail because the story doesn’t travel.

We get so used to speaking in our own dialects – scientific, regulatory, financial – that by the time we’ve explained the same thing three different ways, even we’re not sure what the central message was anymore. Somewhere between the paper, the pitch deck and the press release, the thread snaps.

I’ve been reading a lot about this lately and thinking about how to overcome it in my own work, where the goal is often to communicate complex technologies, new ways of analysing data, and novel healthcare possibilities that are so groundbreaking they can sound – to some – like science fiction.

The challenge isn’t just to make people understand what we do; it’s to help them believe it’s possible, relevant, and valuable to them.

Every innovation has to speak to multiple audiences: clinicians, payers, regulators, investors, patients – and sometimes all of them in the same wweek. Each sees the world through a different lens. And yet, the message has to stay coherent and can bend, but can’t break.

This is a guide for anyone trying to make one message travel across many worlds – how to stay true to your core story while adapting it to each audience that matters.

Subscribe now

Why does it matter?

Innovation doesn’t sell itself.

(Unless of course you’re a silicon valley start up creating a secretive AI company and know some investors with very deep pockets – in which case, have at it, pile in.
The rest of us have to communicate.)

In healthcare and biotech, “value” can look completely different depending on who you ask. A scientist may want methodological novelty and reproducibility. A clinician wants actionable insight and minimal disruption to their workflow. A payer wants to know what it will save. A regulator wants to see safety, compliance and traceability. A patient just wants to feel better, sooner.

The same dataset can mean “statistically significant signal” to a scientist, “cost-reduction potential” to a payer, “clinical differentiation” to an investor, and “hope” to a patient. All true, but filtered through different values.

When I first started working in life sciences marketing, I thought messaging was about getting everyone to agree on one story, but of course, it isn’t. It’s about getting everyone to believe the same truth in the language they understand.

That’s where the idea of a messaging ladder comes in – a way of taking one coherent story and reframing it up or down depending on who you’re speaking to.

Finding the core story

Every product, platform, or study has one unchanging truth at its centre. Everything else – the features, metrics, market positioning – ladders up or down from that.

Let’s take an example. Say you’ve developed a diagnostic test that can detect disease earlier through a new analytic method.

At its core, your story might be:

“We help detect disease earlier so people can be treated sooner and live healthier lives.”

That’s your north star. It’s not a tagline, and it’s not copy for a press release. It’s the essence – the human reason this thing exists.

From there, you can expand and tailor the message depending on the stakeholder, but it should always lead back to this central truth. If you start to drift away from it, you’re not translating – you’re reinventing.

A good test is to ask: if I stripped away the jargon, would this still make sense to a patient? If not, you’ve probably lost your anchor.

The messaging ladder

Think of a messaging ladder as a structured way to climb between the abstract and the specific without losing your grip.

At the top is the platform message – the big, emotional “why” that ties everything together. In the middle sits the value proposition – the rational “what” that explains how this matters to each stakeholder. At the bottom are your proof points – the data and evidence that make it credible.

You start with the big idea, translate it into what matters for each audience, and ground it with data that supports your claim.

If you’re speaking to scientists, lead with the novelty and reproducibility of your research – for example, you might say your approach reveals new disease mechanisms validated across multiple cohorts.

For clinicians, focus on patient outcomes and workflow benefits: explain how the same insight enables earlier intervention and more accurate triage.

For payers, turn that into economics: preventing disease progression, reducing downstream interventions, and improving quality of life at lower cost.

Regulators will want evidence that the system is transparent, validated and safe – so emphasise compliance, auditability, and clear chains of evidence.

Investors care about defensibility and scalability: position it as a differentiated approach addressing a significant unmet market need with room for growth.

And patients want hope over not hype. Show how it helps to understand and manage their health earlier and more effectively within the scope of the product’s approved claims.

Each version sounds different because it’s tuned to the listener’s worldview, yet every one of them carries the same underlying message – that earlier, more personalised detection and intervention can change health outcomes for the better.

How to translate one story for many audiences

Let’s take a simple example: a research breakthrough in understanding why some people are more prone to a chronic disease.

For a scientific audience, the story would emphasise methodology and validation: “Our study identified new biological pathways underlying disease susceptibility, confirmed across several independent datasets.”

For clinicians, you’d focus on practical relevance: “This insight helps identify patients earlier and tailor treatments to their specific biology.”

For payers, the value shifts to prevention and efficiency: “By improving risk prediction and reducing unnecessary interventions, this approach could save thousands per patient and lessen the long-term burden on healthcare systems.”

To investors, the same result becomes a commercial opportunity: “We’re addressing a large unmet need with a validated approach that bridges discovery and clinical application.”

And for patients, the message becomes human again: “For years, this condition has gone unexplained. We’re uncovering the reasons behind it so that people can finally get the answers – and the care – they deserve.”

It’s not about rewriting your story five times; it’s about choosing the right lens for each audience while keeping the same truth in focus.

Building the ladder in practice

Creating a messaging ladder shouldn’t be a solo writing exercise – it’s a team sport.

Bring together people from science, medical, commercial, and communications, and co-create it. Start with an agreed upon set of headline statement and key messages. This document acts as the central pillar or underlying foundation of all the different branches for your audiences.

I find it best to the start with the patient message first. If everyone in the room can agree on the simplest, most human way to describe the value, the rest of the ladder will write itself.

Then climb upward: what does this mean in clinical terms? How does that translate into payer value? What proof points make it credible to a regulator or investor? The order matters less than the consistency. You just want to avoid ending up with a patchwork of disconnected mini-stories written in isolation by different teams.

And don’t wait until launch to test your messaging. Even without a big research budget, you can still get meaningful feedback. Reach out to trusted peers on LinkedIn and ask how they’d interpret your value proposition. For patient-facing messages, talk to disease charities or patient organisations – many have Patient and Public Involvement and Engagement (PPIE) representatives who’ll happily review your tone and clarity.

Those small, informal feedback loops are often worth more than another internal brainstorm.

The coherence test

When you’ve built your messaging ladder, pressure-test it. Pick any proof point and trace it back to your core message. If it doesn’t logically connect, it doesn’t belong.

For instance, if one of your proof points is “validated in thousands of patients across multiple studies” and your top-level message is “helping people live healthier lives through earlier detection,” the connection is clear. But if your proof point is “we’ve filed ten patents” and your main story is about improving patient outcomes, it’s probably a distraction.

Coherence comes from connection, not volume.

Tone and truth

There’s a delicate balance between adapting your language and bending your truth. You can’t just tell each stakeholder what they want to hear. Every version of your message should be both true and verifiable.

Scientists should recognise their data. Clinicians should see their practice reflected. Patients should feel their experience acknowledged. Investors should find the business case credible.

Inconsistent messaging doesn’t just confuse people – it makes them suspicious. The moment one audience hears something that doesn’t align with what another has been told, trust erodes. And in life sciences, trust is the only currency that compounds.

Common pitfalls

There are four I see most often.

First, too many messages, not enough meaning. If you try to say everything, people remember nothing. Choose three core truths you want every audience to take away and repeat them until they stick.

Second, the data dump. You can’t data your way into belief. Evidence matters, but meaning sells. Numbers alone don’t persuade without narrative.

Third, over-promising. The more revolutionary your technology sounds, the more proof it needs. Ground your ambition in evidence or it’ll come off as hype.

And fourth, losing emotional coherence. If your patient message is full of hope and your investor deck reads like a spreadsheet, something’s off. Every audience still wants to feel something – confidence, excitement, reassurance – so keep the human thread running throughout.

A quick checklist for multi-stakeholder messaging

To build coherence from the start, ask yourself these questions:

1. What’s the simple, human truth at the centre of what we do?

2. What does that truth mean to each stakeholder?

3. What evidence makes it credible to them?

4. How should it sound to earn their trust?

5. What do we want them to do next?

If you can answer those five questions consistently, you’re halfway to a message that travels intact.

The storyteller’s dilemma

Science communication isn’t about dumbing things down – it’s about opening them up. The most sophisticated audiences still want clarity. I’ve yet to meet a scientist who prefers confusion to simplicity.

Sometimes, we’re so close to the work that we forget how miraculous it is. Translating your science into accessible language doesn’t cheapen it – it reveals it. And if the message doesn’t travel, neither will the impact.

There’s an old saying that you should explain your work so that your grandmother could understand it. I’d amend that slightly: explain it so that your grandmother would care. That’s the heart of good multi-stakeholder communication. It’s empathy, not simplification.

Closing thoughts

Every idea has to live in multiple worlds. In science, it’s a hypothesis. In healthcare, it’s a protocol. In finance, it’s a bet. In regulation, it’s a risk. In marketing, it’s a story. Your job is to make those worlds connect without contradiction.

The art is in coherence – keeping one story intact while helping each audience see themselves in it. That’s not manipulation; it’s respect. Because people don’t invest in ideas they don’t understand, and they don’t act on ideas they don’t believe.

The best message is not the loudest or the cleverest. It’s the one that travels intact from lab to clinic to boardroom to patient, carrying the same quiet truth all the way through.

Signal over noise.

Thanks for reading Signal.
If you made it all the way here, either you’re deeply interested in the subject or you’re just too polite to stop scrolling – either way, I’m grateful.

If you think this piece is worth sharing, please do. It helps get these ideas in front of the people who can actually do something with them (or at least argue with me about them in the comments).

You can subscribe for free to get future posts direct to your inbox. No spam, no pop-up ads for shoes you never wanted, just science and stories I hope are worth your time.

Bringing a new life science product to market is rarely straightforward. It means translating years of scientific development into something clinicians trust, regulators approve, and health systems will actually buy and use. The stakes are high, not because of hype, but because the science deserves to reach patients and the market rarely gives you a second chance.

I cut my life sciences teeth in a heavily product-focused SaaS company and had the good fortune to work alongside some incredible product teams and mentor brilliant product marketers. With their support I’ve led go-to-market (GTM) and growth strategies for FDA-cleared SaaS medical devices used in drug discovery and at the point of care, cloud-based eCOA platforms deployed in global pharmaceutical clinical trials, CE-marked IVD and clinical decision support platforms used in European clinics, and U.S. market entry strategies for next-generation diagnostic and decision support tools.

These experiences taught me that while each product is unique, the principles of successful GTM in life sciences are remarkably consistent. In this post, I’ll share those foundational strategies – from positioning and value propositions to validation, regulatory navigation, stakeholder engagement, and commercial execution – and how they pave the way for sustainable growth.

Get it right and you accelerate clinical adoption, build lasting partnerships, and create genuine healthcare impact. Get it wrong and even the most promising innovations will struggle to find a foothold.

Product marketing in life sciences is about managing that balance — between evidence and emotion, proof and persuasion — and doing so with precision.

Subscribe now

Positioning that earns attention

If you cannot explain what your product is, who it is for, and why it is better than the next best alternative, you will be treated as interchangeable and beaten down in a race to the bottom on price. Strong positioning starts with a single sentence that anchors the category and the specific job you do for a specific audience. For example, when launching a tablet-based screening test into primary care, we did not describe a generic “assessment platform.” We defined a fast, point-of-care screen for clinically relevant memory issues that fit inside a standard consultation with actionable outcomes and a clinical pathway. That created instant context, a reason to listen, and a route to implementation.

Good positioning is built on insight, not guesswork. Map the competitive set honestly. Identify the two or three capabilities that matter most to your target users and buyers, and be willing to trade off the rest. Then test your position with expert users and key decision makers in structured interviews and focus groups. If the first reaction you hear is a “so what,” go back and sharpen.

Value propositions for every stakeholder

There rarely, if ever, a single audience. And a successful value proposition speaks the language of each stakeholder you need to convince.

• Clinicians and end users. Clinical efficacy, safety, clarity of outputs, and minimal workflow friction. If your tool saves time, shows risk clearly, or improves diagnostic confidence, say so in plain language and back it with data.

• Patients. Convenience, non-invasiveness, faster answers, and better outcomes. If you replace a complex pathway with a simple step, make the human benefit obvious.

• Payers and provider finance. Outcomes and economics. Budget impact, avoided downstream costs, and resource efficiency. Build a believable model and get it reviewed by credible health economists.

• Administrators, labs, and IT. Interoperability, information security, deployment effort, and total cost of ownership. Show how you fit the stack and who supports which part of the rollout.

Turn these into one coherent story that ties clinical benefit to operational and financial value. Use pilots, focus groups, and early adopter programs to gather proof points and quotes you can stand behind.

Clinical and analytical validation

Credibility is the currency in life science product marketing. For software as a medical device, diagnostics, and decision support tools, you need both analytical validation and clinical validation. That means showing the system is technically robust and that it performs against the right clinical endpoints in the right populations.

Design studies that mirror real use. Reproduce results in independent cohorts. Publish where possible. Then translate that evidence into materials that busy clinicians, administrators, and payers can grasp quickly.

A clean figure that shows sensitivity, specificity, likelihood ratios, or odds ratios will travel further than paragraphs of adjectives.

Engage respected experts early. Advisory boards, investigator sites, and structured user testing with expert users and decision makers will surface flaws fast and create future advocates. Their feedback improves the product. Their endorsement accelerates adoption.

Regulation as a trust signal and a gating constraint

Regulatory strategy must be part of go-to-market, not a separate track. Your intended use and claims shape your regulatory class, which shapes your evidence plan and timeline, which shapes your launch plan.

Treat clearance or certification as both a milestone and a message. “FDA 510(k) cleared” or “CE-marked for clinical use” tells a skeptical audience that your product has been examined and approved for a defined purpose. Build your roadmap with regional differences in mind, and be honest about what is research use only versus clinical use.

Keep marketing and medical affairs joined at the hip so claims in the field match what has been approved.

Health economics and market access

Approval is the start line. Coverage and payment unlock scale. Plan market access early. Identify the likely reimbursement pathway, codes, and evidence standards. Build budget impact models and cost-effectiveness analyses that a payer medical director can interrogate.

Where direct reimbursement is immature for digital or decision support tools, consider provider-purchased models while you accumulate the outcomes evidence that payers require. Price to the value delivered, not to engineering effort. If you save a hospital money or release clinical capacity, quantify it and price accordingly.

Building a value-based narrative is key for market access. Frame your product in terms of value-based care if possible: how it improves outcomes per dollar spent. For example, when marketing an AI decision support for complex chronic diseases, we framed it as “precision medicine that avoids trial-and-error treatments, thereby saving costs on ineffective therapies and adverse events.” We backed this with a pharmacoeconomic model showing potential savings if patients are matched to the right therapy first time.

Stakeholder mapping and engagement

One of the unique challenges in life sciences GTM is the complex web of stakeholders involved in product adoption. Unlike consumer products where you often market directly to the end-user, in life sciences and healthcare the user, buyer, influencer, and beneficiary can all be different people and/or teams. A successful launch strategy requires mapping out this ecosystem and engaging each player deliberately.

List the people who can say yes, the people who can say no, and the people who can make either group nervous. That typically includes clinicians, service managers, lab directors, procurement, IT security, data governance, payers, and patient advocates. Build a simple map of who influences whom inside priority accounts.

Plan how you will engage each group. Use advisory boards, focus groups, lunch-and-learns, CME-accredited webinars, and site visits. Bring reference customers into your process early and support them so they can speak publicly about results. Peer-to-peer advocacy will do more for you than any brochure.

A critical piece of stakeholder engagement is KOL (Key Opinion Leader) strategy. KOLs are the influential experts whose opinions carry weight in the community. In every launch I’ve managed, a portion of our marketing strategy was essentially KOL engagement: identifying the top 5–10 names in the field and finding ways to involve them – through research collaborations, speaker programs, or simply relationship-building. Podcasting is another excellent way to achieve this, but far too many B2B podcasts only feature speakers from their own company and in doing so are missing a trick. When KOLs speak positively about your product (at a conference, online or in a publication), the broader market listens.

Commercial strategy and execution

Complex products are won through focused execution, not broad noise. Start with a defined beachhead where the need is sharp, the workflow fit is strong, and the champions are motivated. Land reference sites that others respect. Support them intensely. Publish what you can. Then expand by segment and geography with clear criteria for readiness.

On the marketing communications front, education-driven content marketing is king in life sciences. Because these products are technical and often novel, your potential customers benefit from (and appreciate) high-quality educational content. We created white papers, webinars, and conference presentations to explain the science behind our product and the problem it solves. By positioning ourselves as a thought leader in, say, cognitive assessment or precision medicine, we attracted leads who were hungry for solutions to the challenges we discussed. This content serves a dual purpose: it educates the market (expanding the pool of potential customers who understand why they need your product) and it builds trust in your expertise.

One thing to guard against is the temptation of purely tactical marketing tricks at the expense of strategy. Digital tools (and yes, even AI tools) can accelerate your reach, but without strategic clarity, they can also amplify noise. I would always remind my team: strategy first, tactics second. Make sure the messaging and targeting are rock solid (the “who and why”), then deploy tactics (the “how”) like inbound campaigns, LinkedIn ads, search marketing, or stakeholder outreach accordingly.

A few practical principles that have served me across launches:

• Account focus. Treat priority provider groups, labs, or trial sponsors as named accounts with tailored plans. Coordinate product, medical, marketing, and sales so every touch adds up.

• Education-led marketing. Use clear, neutral education to explain the problem and the mechanism behind your solution. White papers, webinars, posters, and short explainers work well. Keep claims within the label.

• Sales as consultants. Equip the team to help customers build their internal case. That includes ROI calculators, workflow diagrams, sample SOPs, and security documentation.

• Customer success as marketing. Measure time to first result, time to go-live, user satisfaction, and support response. Fix friction quickly. Turn delighted users into named references and speakers.

  ---

Principles that travel across categories

Across FDA-cleared software, CE-marked IVDs, and clinical decision support tools, a few philosophies have been consistently effective.

• Science to story. The science earns you a hearing. The story earns you adoption. Translate mechanisms and statistics into a narrative that shows how real people benefit.

• Credibility first. If you cannot substantiate it, do not say it. Understatement backed by data outperforms hype.

• Solve the last mile. Training, integration, clinic flow, and reporting are often the real adoption barriers. Design for them as seriously as you design the core algorithm.

• Frameworks, not formulas. Use simple frames to pressure test your plan. My five questions: Who exactly is the customer and what do they need. What exactly is our solution and unique value. How do we prove it. How do we reach and convince them. How do we support and expand adoption after the sale.

• Learn in market. Build feedback loops. Run post-launch reviews at 30, 60, and 90 days. Adjust messaging, materials, or the product itself based on what you learn.

Pitfalls to avoid

Even with the best plans, there are recurring pitfalls that companies fall into when launching new products. Here are some of the most common pitfalls I’ve observed – and strategies to avoid them:

• Technology in search of a problem. If the use case is vague, adoption will be too. Validate the problem and the workflow with expert users before you scale.

• Muddled differentiation. If a buyer cannot see why you are better than their status quo or a known competitor, price becomes your only lever. Clarify the two things you win on and say them everywhere.

• Thin evidence. Pilots and anecdotes are not enough for scale. Build the analytical, clinical, and economic case you will need for each audience.

• Late market access planning. Coverage decisions take time. Start payer conversations early and build the evidence they ask for.

• Stakeholder blind spots. Ignoring procurement, IT, or data governance will delay or kill deals. Engage them early with complete documentation.

• Launch and leave. The first 6 to 12 months set the trajectory. Monitor usage, outcomes, and satisfaction. Close the loop fast when something is not working.

Closing thoughts

Great science deserves great go-to-market and in-market strategy.

The stakes are high – for patients who need better solutions, for companies that invest years and capital, and for healthcare systems striving for improvements. What separates the success stories from the cautionary tales often boils down to strategic excellence in go-to-market execution.

Know your audience and earn their trust. Build credibility step by step. Align with regulators and payers as partners, not adversaries. And maintain a relentless focus on the end goal – improving patient lives and healthcare outcomes – as your North Star.

The reward for getting it right is real clinical impact, sustainable growth, and a product that becomes part of standard care for the people it serves. The penalty for getting it wrong is a long, expensive wait in evaluation limbo. Choose disciplined strategy. Choose credibility. Choose to make adoption as thoughtfully engineered as the product itself and never lose sight of the human impact behind all the data.

Signal over noise.

This Post Will Not Go Viral.

There’s a growing genre of online writing that reads like it was made in a sterile room, piped through a focus group, and polished by a mildly attentive AI before it’s posted. My LinkedIn feed has become a museum of it - immaculate paragraphs from people who, until recently, I would have bet good money could write something far more interesting themselves.

Post-docs, pharma execs, even Nobel laureates. People whose minds are capable of sparking with wit, nuance, and original thought - all producing “content” that feels like it’s been through the same rinse cycle. The tone is consistent. The phrasing familiar. The cadence suspiciously machine-like.

It’s not that there’s anything wrong with using AI to draft your updates, we’ve all done it. But the result is that everything starts to sound the same. The quirks vanish. The edges are sanded down. You lose the little linguistic fingerprints that make human writing feel alive.

And then there’s the bigger, more insidious problem: people are no longer writing for their audience - they’re writing for an algorithm.

Subscribe now

LinkedIn, for all its faults, was once a place where original thinking could reach the right audience without an advertising budget. That’s becoming rare.

Now, it’s increasingly pay to play. Organic reach has plummeted unless you either pay for ads or feed the algorithm exactly what it craves. The result? An outbreak of uniform posts designed to juice engagement metrics rather than spark meaningful discussion.

We’ve entered the era of the humblebrag case study, the faux-vulnerable leadership lesson, the relentless carousel of success theatre. Engagement pods, bots, and engagement for the sake of boosting one’s own algorithmic score rather than to add any real value to a conversation.

All trying to trick the algorithm into thinking something’s hot.

But something is most definitely not.

Here’s a perfect parody of the modern LinkedIn post, shared with me by a friend, original source unknown to me - it made me laugh, and then die a little inside:

What does it take to be a LinkedInfluencer? Let me share the formula I’ve worked out.

I used to write normally.
Paragraphs. Flow. Structure.

Then I discovered something.
✨ White space.
✨ Fragmented wisdom.
✨ The art of… dramatic pacing.

Now?
I write like this.

Because it makes you stop.
Scroll.
Stare.

What am I saying?
Does it matter?

Influence.
Isn’t built on clarity.
It’s built.
On rhythm.

👇
If this moved you, let’s connect.
If it didn’t, think deeper.
This is strategy.
This is provocative.

This
Is
Content. 🔥

The Disappearing Craft

The deeper loss here isn’t just that our feeds are less interesting - it’s the potential that the underlying skill of constructing an idea in words is eroding. Writing isn’t just a delivery mechanism for information. It’s multiple cognitive processes working together - reasoning, structuring, and refining in real time.

When we outsource too much of that to AI, we risk hollowing out the skill itself. And that doesn’t just affect writers. Scientists, mathematicians, and engineers also think through making - whether that’s in words, diagrams, models, or code. The process itself is the crucible for insight. Remove it, and you risk losing the deeper problem-solving instincts that emerge only through the act of wrestling with an idea.

A young researcher who never has to iterate on a paper draft may never develop the discipline to spot weak arguments or poorly structured logic. A data scientist who always uses automated model selection may never learn how subtle parameter changes shift results.

Over time, my fear is that these small absences compound into a profound skills gap.

AI in Healthcare: Promise and Precaution

Psychologist Mihaly Csikszentmihalyi believes creativity arises from a dynamic interaction where the individual engages deeply with a field and its established knowledge, shaping it to introduce something - a feedback loop where the act of making reshapes the maker. Automating too much of that loop risks breaking it entirely.

Nowhere is this tension more visible to me than in healthcare. AI is now helping radiologists detect tumours earlier, predicting disease outbreaks before they happen, summarising clinical trial literature for overburdened researchers, and, remarkably, managing to transcribe doctor’s handwriting so that their notes may actually be read.

Incredible.

The upside is real: faster decisions, earlier interventions, and the possibility of extending high-quality care to communities that have long been underserved.

But medicine is more than the application of pattern recognition to a dataset. Biology does not behave like data - it is complex, dynamic, and context-dependent. Where algorithms see correlations, biology demands causation. A model might correctly associate certain symptoms or biomarkers with a condition, but without understanding the underlying mechanisms, it cannot explain why that pattern exists or how it might differ from one patient to the next.

AI’s biggest limitation is that it inherits the biases and blind spots of its training data. If the data underrepresents certain populations or diseases, the AI will too. Biology, by contrast, is inherently unbiased. The mechanisms of disease operate independently of who a person is or where they live. The challenge is that we still lack the tools to read those mechanisms clearly and connect them to meaningful patient outcomes.

That is where the future of healthcare lies - not in replacing clinicians with black box algorithms, but in giving them better visibility into the causal biology of disease. We need analytics that can identify the biological drivers of disease rather than just describe associations. We need mechanistic diagnostic tests that can detect those biological signatures in patients early, inform clinicians of underlying risk factors, and triage people more precisely to the right specialist or care pathway. These tools can guide targeted interventions before conditions worsen, enabling a shift from reactive to predictive, preventive medicine.

AI can help accelerate that shift, but only if it is used to illuminate biology, not obscure it. That means building explainable systems rooted in mechanistic understanding - systems that enhance clinical decision-making by showing why a disease occurs and how to intervene effectively.

Because in healthcare, as in writing, science, and art, understanding the process matters as much as the product. And when the process is lost, so too is our ability to question, to adapt, and to truly heal.

Holding on to the Human Parts

Every technological leap has sparked fears of skill loss. The printing press, the calculator, the camera - each forced adaptation. The skills didn’t vanish, but they changed. The difference with AI is its scope: it touches not one craft, but many, simultaneously. It’s not just a single skill we risk losing, but the broader capacity to think creatively across domains.

The danger is that, in skipping the wrong turns, we lose the part of the journey that makes us capable of right turns in the first place. In healthcare, that could mean missing the nuance that turns a standard treatment into the wrong one for a particular patient. In every field, it means risking the erosion of our most human abilities.

The risk is not that AI will make us worse creators. The risk is that we will stop being creators altogether - in our writing, in our science, and even in our medicine. The tools are powerful, but without the hands to guide them, we may end up with precision and speed at the cost of empathy and understanding.

This post will not go viral. But it is mine - written by a human brain, flawed, subjective, and shaped by experience.

Signal over noise.

Thanks for reading Signal Over Noise
If you made it all the way here, either you’re deeply interested in the subject or you’re just too polite to stop scrolling – either way, I’m grateful.

If you think this piece is worth sharing, please do. It helps get these ideas in front of more people like you.

Share

When I published The New Rules of Life Science Marketing: How to Lead in the Age of AI, one of the questions I was asked was: how do these rules apply specifically to startups?

This article is my attempt to answer that question. It draws on years of scaling young companies and looks at what AI really means when you are not a global giant with a marketing army, but a small team with lean resources, a bold idea, and the urgency of growth.

Subscribe now

Startups: fewer hands, bigger goals

Early in my career I was leading communications programs for big bluechip corporates like Barclays and BT. The budgets were large, the teams even larger, and the challenge was usually one of scale and coordination. But the initial excitement turned into burnout and I was left wanting something with greater purpose that could be rewarding beyond renumeration. So, I then jumped head first into the world of science and technology startups.

And I’ve never looked back.

There is no greater motivator than working for a company that can tangibly change lives for the better. And in startups, the chance to build something transformative from the ground up is magnified. The sense that the work you do today could shape a patient’s future tomorrow is hard to beat.

Startups are defined by contradictions. You are expected to look like an established brand, but you are building it with a tiny team. You are competing with incumbents who have global reputations, while you are still trying to get noticed. And you are constantly asked to do more with less.

AI has shifted that balance. A small three-person marketing team can now do the work of ten. Generative tools can create first drafts of press releases, pitch decks, and blog posts in minutes. Automated design platforms can produce graphics that once required expensive agencies. Agentic AI systems can run customer research, identify leads, and personalise outreach at a level that was previously out of reach.

But AI does not replace the need for skilled marketers in scale-ups. If anything, its arrival makes experienced judgment more important than ever.

Of course, I would say that, wouldn’t I. But the reality is that while these tools extend our reach, they do not replace the craft of positioning, storytelling, or understanding audiences.

We’ve all seen it. In the wrong hands, AI creates and amplifies mediocrity. Just look at your LinkedIn feed to prove the point.

But, in the right hands, it allows a small team to move with the power and pace of a much larger one.

Where AI adds genuine scale

Used carefully, AI can help startups in places that matter. It speeds up the routine work, early drafts, research scans, social posts, basic reporting, so that marketers can spend more time on higher-value strategic tasks, understanding audiences, and building relationships.

It makes personalisation easier. Instead of sending the same pitch deck to twenty investors, you can adapt the narrative to reflect each investor’s portfolio and priorities in a fraction of the time.

AI also stretches scarce content. A single research paper or company announcement can be turned into podcasts, blogs, LinkedIn posts, newsletters, and investor briefings. For young companies, the ability to repurpose and squeeze every drop of value out of limited assets is a lifeline.

And used responsibly, AI can sharpen decision-making. By scanning publications, competitor updates, funding flows, and even online discussions, it can give startups access to some of the business intelligence that can otherwise cost thousands per annum in subscription fees or used to require analyst firms or large communications teams. Now it is within reach for anyone using the right tools and asking the right questions.

Where caution is essential

As I’ve repeated many times over like a broken record, the same AI technology that creates opportunity also creates risk.

Startups (at least those with any chance of success) are usually marketing something genuinely new, and novelty is exactly where generative AI struggles. Exciting new and progressive approaches, technologies, products and discoveries lose all edge if AI is relied upon too heavily for messaging and communications.

Without enough pre-existing source material for gen AI to draw upon, what it will produce will be bland at best, likely inaccurate, and void of differentiation or impact.

Ask AI to describe truly novel science and it will create generic content from the status quo. Ask it to describe your pioneering breakthrough and it will often fill the gaps with confident invention. That kind of “hallucination” may look like useful content, but it undermines your differentiation and in some cases misrepresents your innovation altogether.

As Google’s AI Search so eloquently explains:

AI hallucinations are false or misleading information generated by an AI model, such as large language models (LLMs). These hallucinations occur when the AI fabricates facts, cites non-existent sources, or presents irrelevant information as true, often due to biased or incomplete training data, insufficient understanding of a query, or by filling gaps in knowledge based on its training. While AI models are improving, hallucinations can have serious consequences, especially in critical fields like law, science, and medicine.

These consequences are serious and compliance adds another layer of risk. AI does not know the difference between what is possible and what is permissible. In life sciences, a misplaced claim is not just sloppy marketing, it can have regulatory and reputational consequences. That responsibility lies with skilled marketers and product teams working closely with regulatory colleagues.

And then there is voice. Startups succeed or fail as much on the strength of their story as on the quality of their product. Automated text too often flattens everything into corporate sameness. Turning your favourite artisan neapolitan ice cream into mass produced tubs of vanilla packed full of artificial flavours and sweeteners.

Forgive me, I’m getting hungry and the sun’s out. The point is that in a technical B2B market, blending in means being invisible. This is where human expertise matters most, shaping every AI-assisted draft into something distinctive, accurate, and compelling.

What an AI-ready playbook looks like

For scale-ups, the AI-ready playbook begins with strategy. Without clear positioning, defined audiences, and a differentiated value proposition, AI will only generate more noise. Strategy remains the foundation. AI is the amplifier.

The most effective teams will embed AI early in their processes. They use it during brainstorming, planning, and testing, not just as a shortcut to churn out content at the end. They encourage experimentation, use it to improve rather than create content, share what works, and build confidence in when to trust the tool and when to challenge it. In this scenario, upskilling early is vital. A small team or single individual cannot afford to lag, and the first hires need to be confident in leading the way.

Measurement also needs rethinking. Perfect marketing attribution is already broken thanks to privacy laws and platform changes. The real question is not how many clicks a campaign produced but whether it increased brand search, raised awareness within the intended audiences and accounts, drove more inbound enquiries, or helped secure conversations with the right partners and investors. Lift matters more.

And above all sits trust. Be transparent about how you use AI. Check every claim. Keep your storytelling rooted in the science and the mission. That is how we build credibility in a market already filling with automated content.

Human first, AI fast

Scaling a startup means moving quickly without losing your footing. AI can help. It widens reach, extends bandwidth, and frees our teams to focus on the work that drives growth. But it is not a silver bullet. It requires oversight, judgment, and a clear strategic focus.

AI gives us speed, but the steering wheel - your story, your standards, your strategy - I believe still needs to be held firmly by people who understand your company, the science, the audience, and the risks.

In my view, startups that get this balance right won’t necessarily be the ones creating the most but will create the most compelling and high quality marketing. They will build more than campaigns. They will build momentum. And in the early stages of growth, momentum is the most valuable marketing asset you can create.

Signal over noise.

Thanks for reading Signal Over Noise
If you made it all the way here, either you’re deeply interested in the subject or you’re just too polite to stop scrolling – either way, I’m grateful.

If you think this piece is worth sharing, please do. It helps get these ideas in front of the people who can actually do something with them (or at least argue with me about them in the comments).

This article will explore:

• The challenges of communicating prevention over cure

• Cautionary tales from climate change to COVID-19

• Lessons learned from successful prevention campaigns

Please add your ideas, comment, and share.

There’s a quiet revolution underway in healthcare. If it works as intended, it will save billions of lives, trillions in costs, and drastically increase healthspan - the number of years spent in good health. Yet, unlike the latest medical breakthrough, it rarely makes the front page.

The revolution is preventative health.

From new understanding of the mechanisms driving complex chronic disease to enable early genetic risk screening, accurate disease prediction, precise diagnostics and digital health monitoring, to the discovery of actively protective biology that could prevent high-risk diseases with new prophylactic drugs and vaccinations, the tools are more sophisticated than ever.

The challenge? Their success is invisible: the heart attack that never happens, the diagnosis that never comes, the hospital stay avoided. That invisibility makes trying to sell prevention and create action in those who would benefit a critical issue that needs to be resolved.

The (marketing) problem with prevention

Selling preventative health is like selling fire alarms to people who have never seen a fire. It works quietly until it doesn’t.

“You’re asking people to invest in something intangible,” notes Dr Lisa Cooper of Johns Hopkins. “They must believe your risk prediction, trust the intervention, and defer satisfaction now for something that may never happen.”

Present bias, preferring dessert over salad even when future health is at stake, is powerful.

That explains why, even though preventative measures like lifestyle changes could prevent around 40% of premature UK deaths, spending on prevention remains below 5% of the UK NHS budget.

Cures command attention: surgeons saving lives, miracle drugs in development. Prevention? Not so much.

As Dr Harvey Fineberg said in his paper The paradox of disease prevention: “When prevention works, nothing happens." And when nothing happens, there is no gratitude, no headlines, no funding.

Climate change as a cautionary tale

The climate crisis is a real-world analogue to the prevention paradox described. For decades, scientists have warned of global warming, rising CO₂, and melting ice.

Despite that, key targets keep slipping. The UN Environment Programme warns we must cut emissions by 42% by 2030 to stay within 1.5°C of warming, and we are not even close. A recent study revealed that a collapse of the Atlantic Meridional Overturning Circulation (AMOC) may now be likely within decades, even under low-emission scenarios.

Part of the challenge has been communication. Scientists and policymakers have struggled to convey the scale, urgency, and immediacy of the threat in ways that cut through to the public. But the greater challenge has been driving action from the main protagonists: the states and corporations responsible for the majority of emissions, who also hold the greatest power to prevent the crisis.

Despite decades of warnings, vested interests, short-term incentives, and political hesitation have delayed meaningful action, leaving the world to bear the consequences of prevention ignored.

We knew about the danger, had the tools to act, but didn’t. Prevention worked quietly, so we ignored it.

The diagnostic paradox

Preventative diagnostics, especially in complex chronic diseases, can provoke anxiety before they provide reassurance. Mechanism-based diagnostics now make it possible to pinpoint the biological pathways that drive conditions such as endometriosis, COPD, Alzheimer’s, ALS, or Long COVID. This allows much more accurate risk prediction and earlier diagnosis than traditional metabolomic diagnostics, symptom-based approaches, or polygenic risk scoring.

But while a predictive test might reveal that you are at higher risk for Alzheimer’s or cardiovascular disease and point to specific interventions that can reduce that risk, it also forces you to confront uncomfortable truths about your health future.

Dr Ananya Banerjee, a behavioural scientist at McGill University, describes this as “the emotional toll of knowing.” For some, this knowledge is empowering. For others, it feels overwhelming.

That is why framing matters.

The real value of mechanism-based diagnostics is not in only telling you what is wrong but in equipping you with agency to act.

Prevention is most persuasive when positioned as a way to take control of your trajectory not a reminder of your vulnerability. Being able to seek the specific treatment and medical specialism that will be most effective in improving your health, by knowing which specific mechanisms are driving your disease.

When prevention isn't rewarded

Economics do not always help either. The production and sale of fossil fuels is too lucrative for those who make financial gains and profit in the short term while the planet suffers in the longer term.

Disease prevention faces the same misalignment as the climate crisis. The health economics argument in favour of prevention over cure is undeniable. However, fee-for-service healthcare models remunerate treatment, not avoidance. Drug developers currently profit more from disease than from preventing it. Even insurers might not benefit, since patients change plans and long-term savings accrue to someone else.

The more effective you are at predicting and preventing, the smaller the market for treatment drugs becomes.

Some progress comes from value-based care models, where providers are rewarded for keeping patients healthier for longer, but shifting incentives is slow.

Post-pandemic backsliding

COVID-19 was the greatest global health crisis in a century. More than 7 million people have died worldwide, with excess deaths estimated far higher. The economic shock was equally staggering: trillions wiped from global GDP, health systems overwhelmed, education disrupted, and long-term impacts still unfolding in the form of Long COVID and workforce displacement.

It was a painful demonstration of what happens when prevention fails. Warnings about pandemic risk had been clear for decades, yet systems were underfunded, stockpiles inadequate, and preparedness plans often theoretical.

And yet, only a few years later, we are already dismantling the very safeguards that might prevent the next pandemic when it happens.

• In the U.S., the Trump administration pulled $11 billion in federal funding from state and local health agencies. This triggered layoffs, gutted programs, and experts warned it “dramatically erodes pandemic preparedness.”

• The C.D.C. faced wholesale staff layoffs. Key programs such as epidemiology, immunisation, and outbreak analytics were dismantled or severely reduced. Overall, the budget would cut the C.D.C.'s funding by about $5 billion.

• Global health security funding is also being slashed. The U.S. administration proposed cutting more than $500 million from global health security programs, and dissolved USAID, disrupting pandemic resilience internationally.

• In the U.K., chronic underfunding and a decade of austerity following the 2008 global financial crisis left the public health system depleted when COVID hit. Though the 2024 autumn budget pledged £460 million to strengthen preparedness, those investments arrive late and reactive, not preventative.

How can these reductions be possible straight after we have witnessed how devastating a pandemic can be? How quick are we to forget?

As with the climate crisis, the prevention of major issues tomorrow is being deprioritised in favour of smaller, more immediate fixes today.

Lessons from successful prevention campaigns

If prevention is such a tough sell, what can we learn from the cases where it has worked?

Public health does have some victories, and they suggest an important truth: it is not that people cannot be persuaded to act preventively, it is that the way the message is framed matters enormously.

When campaigns have succeeded, it is rarely because the raw data was compelling enough on its own. It is because the story, the framing, and the cultural context made prevention feel urgent, relevant, and actionable.

Anti-smoking efforts, for example, did not win hearts and minds by showing risk tables in journals. They cut through by showing the human consequences in shocking, visceral ways.

Sunscreen brands do not sell based on twenty-year melanoma projections; they sell based on vanity and immediate benefits you can see in the mirror.

The lesson here is that we are not irrational, but we are human. We respond to what feels close, personal, and emotionally resonant.

Successful prevention campaigns have consistently:

1. Made the invisible visible: anti-smoking ads linked grisly lung images with real stories.

2. Reframed benefits: sunscreen says “look younger now,” not “avoid melanoma later.”

3. Made it easy: HPV vaccination succeeded when integrated into school programs.

4. Appealed to identity: COVID-19 campaigns that said “protect others” outperformed self-focused messaging.

5. Linked to aspiration: fitness devices sell "be healthier today," not "avoid heart disease tomorrow."

**Please share other successful prevention campaign examples that come to mind.**

The question for us now is how to take these lessons and apply them to more complex and less visible threats, from chronic disease to global warming and pandemic prevention.

Rethinking how we talk about prevention

If the old playbook of fear and risk awareness is not enough, we need a new vocabulary for prevention. The current framing often positions prevention as something that takes away pleasure today in exchange for uncertain benefits tomorrow. That is hardly an inspiring narrative.

Past examples suggest a more effective approach would be to flip the script: prevention is not about restriction, it is about expansion. It is about unlocking years of vitality, increasing healthspan and longevity, protecting the experiences and relationships people want to preserve, and enabling better choices that align with our goals. It should feel like an investment in a better life, not a tax on the present.

This is where advances in science can be powerful storytellers. Mechanism-based diagnostics and disease risk prediction give us the ability to explain risk in ways that are tangible and personal.

Instead of a generic warning that “heart disease is the number one killer,” you can show someone how their specific biology increases risk, and what specific action can change the trajectory.

When paired with precision medicine approaches in this way, prevention is not just more accurate, but more relatable.

The language we use makes a big difference here:

• Say “protect decades of healthy living” instead of “screen to catch disease early.”

• Say “increase odds of staying well longer” instead of “reduce your risk.”

• Personalise risk. "You have 40% higher risk than average" is more motivating than abstract stats.

• Make it actionable. Using mechanistic insights, describe the physiological and molecular processes causing a patient’s illness and its resulting symptoms so that intervention can be targeted and effective.

Prevention has to feel empowering rather than punitive. It should create a sense of agency and optimism, not dread. That means every touchpoint, from diagnostics to public health messaging, has to be designed not just for accuracy, but for human psychology.

If we want preventative health to become mainstream, we need to stop selling it as an insurance policy against fear and start selling it as a passport to a healthier life, lived longer.

The role of trust

None of this works without trust. People must believe the science, the source, and the motive.

Mechanism-based insights can help here. By showing people the biological reasoning behind their risk rather than a black-box prediction, prevention becomes more credible and less abstract. Trust in science is not always a given - we live in a time when more people than ever appear to be sceptical of truth and accepting of misinformation. But trust will grow when explanations feel transparent, personalised, and evidence-based.

Equity matters too. If prevention feels like a privilege only for the wealthy or for certain demographics, distrust will deepen.

Prevention as a movement

It feels like we need cultural change. It starts in schools, becomes a norm in health conversations and healthcare institutions, and becomes the default path. Incentives, social and financial, must all align with prevention.

Mechanism-based diagnostics, precision medicine drug development, actively protective biology, and preventative health initiatives could anchor this movement. They allow us to move beyond vague advice toward precise, personalised prevention - creating opportunities not only to delay disease but to avoid it entirely.

And that is a story worth telling.

Prevention’s success is silent. The headlines will not feature empty hospital beds or avoided diagnoses. Still, in lives saved, healthspan extended, suffering spared, and resources conserved, the difference is seismic.

Signal over noise

I’ve been in life science marketing for over 15 years, and I can say confidently that we’re in the middle of a seismic transformation. Marketers have long felt the ground shifting beneath their feet – even a 2013 survey found 76% believed marketing had changed more in the prior two years than in the past 50.

Fast forward to today, and that pace has only accelerated. The convergence of artificial intelligence (AI), digital-first engagement, and an urgent need for speed and trust has upended old playbooks. If you feel a bit of FOMO (fear of missing out) these days, you’re not alone.

The rules of the game are being rewritten in real time, and those who don’t adapt risk being left behind.

There’s an industry adage that “the only moat is speed” in the AI age, and it rings true: winners and losers in our field are being defined faster than ever. In this whirlwind, I’ve learned that we must embrace new strategies while holding tight to the founding principles.

In this essay, I’ll share the new rules of life science marketing as I see them – a blend of experience and insight – to help us all lead in the age of AI, speed, and trust.

Half a Century of Change, in Two Years

Life science marketing has evolved more in the last two years than in the previous fifty. Consider how our audience’s behaviour has changed. Today’s buyers (whether pharma execs, clinicians, lab scientists, geneticists, payers, providers or patients) are ultra-informed and digitally savvy.

By the time they ever speak to someone from your company, they’ve often self-educated through a gauntlet of online content. On average, B2B buyers now review about 11 pieces of content before even contacting a vendor.

In my early career, a scientist might hear about a new technology at a conference or read a trade journal article; now they’re just as likely to discover it via a LinkedIn post, a podcast, or even an AI-driven search result. This multi-touch, self-service behaviour means we have to engage our audience on many fronts simultaneously.

Another dramatic shift is the rise of multi-stakeholder influence. Decisions in pharma, biotech and healthcare are rarely made by a single decision maker; often a whole committee of researchers, procurement officers, and executives weighs in. Businesses must influence an entire buying ecosystem – not just win over one champion.

We’ve all seen deals stall because one scientist or one budget-holder in the background wasn’t convinced. The new rule: assume that every message needs to resonate with multiple personas. It’s no surprise that B2B purchases now involve an average of 4 to 10 stakeholders.

Our strategies have to account for all of them, delivering a mix of technical depth, economic justification, and emotional reassurance.

What’s driving this breathtaking rate of change? Two big factors are digital expectation and AI acceleration. Digital-first experiences are now the norm even in conservative industries.

Consumers of all walks expect slick user experiences, on-demand information, and omnichannel communication. If we don’t meet them with the same ease and personalisation they get in other parts of their lives, we lose.

And then there’s AI: from automating repetitive tasks to uncovering patterns in customer behaviour, AI has injected both speed and complexity into our world. We can scale campaigns faster than ever, but we also face pitfalls (more on that soon).

The New Playbook

In this new world, the old marketing funnel has exploded into a constellation of channels and touchpoints.

Our playbook today is decidedly digital and diverse.

SEO and SEM (search engine marketing) are foundational – if a curious prospect can’t find you on Google or the scientific literature databases, you basically don’t exist. But how to leverage these tactics has changed completely in the last 12-months with AI and ‘zero-click’ search.

LinkedIn remains the primary B2B platform and a phenomenal resource when leveraged to its full potential. But the recent seismic changes to its algorithms have left many marketers scrambling. Twitter became a hub for scientists swapping protocols and ideas, but while the future of X is uncertain, platforms like Bluesky are springing up where that scientific community migrates. I’ve also seen a surge of interest in niche communities: specialty Reddit forums, ResearchGate, even private Slack groups for professionals. A modern life science marketer has to be a social chameleon, listening and joining these conversations. Successful strategies take great planning, judgement, creativity, and discipline to execute.

Podcasts and video have emerged as powerhouse formats. A podcast interview with a thought leader or a short explainer video can cut through the noise in ways whitepapers sometimes don’t. Busy PhDs now listen to industry podcasts on their commute or while running experiments. And video’s influence is undeniable: 94% of people watch explainer videos to understand products or concepts better, and 86% of marketers report increased lead generation with video. The key is to ensure these videos and podcasts deliver real value.

Another rule: Ungated, ecosystem content trumps the old gated funnel.

We used to hoard our best content behind lead forms, creating barriers and hurdles for our audience to navigate. In the age of information abundance, forcing prospects to jump through hoops just turns them off. I’ve shifted to an ungated content strategy for most educational materials – freely accessible articles, interactive tools, and resource hubs. The payoff is trust and shareability.

When you ungate, you signal confidence and generosity, which draws people in rather than pushing them away. Besides, today we can capture intent data through less intrusive means without walling off content.

One concept I champion is building a content ecosystem instead of a linear funnel. We publish peer-reviewed papers, from which we create blog posts that link to webinars, which link to case studies, which spark podcast discussions, seminars, presentations, social posts and so on – a web of interlinked touchpoints.

This keeps prospects orbiting in our universe, consuming content at their pace in whatever format they prefer. It’s the opposite of a one-size-fits-all funnel or drip campaign. In practical terms, it means repurposing content across channels and encouraging exploration. Over time, this builds a richer picture in their mind and multiple reinforcing impressions of our expertise.

Crucially, marketing automation ties this all together. The latest tools let us personalise at scale – sending tailored follow-ups or recommending next content based on a person’s behaviour. Automation is great for efficiency, but it should never feel robotic to the audience. We use it to scale, while manually ensuring quality control and keeping communications genuine.

The new playbook is exciting and expansive. It’s also noisy out there – everyone is vying for attention on these channels. Our job is to connect the science that matters with the people it matters to and who are in a position to take action.

For life science, that means leaning into our strength: substance. We have real science and data behind our products; our content can and should be richer than the average B2B SaaS blog post. If we meet our audience where they are – on search, social, video, podcasts – and give them credible, engaging content, we become the signal amid the noise.

AI in Marketing: Promise and Pitfalls

Let’s talk about the robot in the room: AI. As a marketing leader in an AI-driven life science company, I’m both a huge proponent of AI and a cautious critic. The promise of AI in marketing is immense. It can help us scale personalization in a way that was unimaginable a few years ago – think AI that can generate dozens of email variations tailored to different segments, or algorithms that identify patterns in customer engagement data faster than any human analyst.

When used right, AI is like a force multiplier for our teams: we can do more, faster – more campaigns, more A/B tests, more content – without a linear increase in headcount.

However, AI also comes with significant pitfalls – especially in the context of life sciences.

One of the biggest tensions in modern life sciences marketing is the temptation to use generative AI for speed and scale versus the unforgiving precision and accountability that the sector demands.

Generative AI is an extraordinary tool for summarising established knowledge, identifying patterns in large volumes of content, or reformatting information for different channels. But it is fundamentally backward-looking. It builds from what already exists, rather than breaking ground on what is new. That becomes a liability when you are marketing frontier science - technologies and discoveries so novel that they don’t yet have a strong digital footprint.

In those cases, AI models may grasp at straws, filling gaps with approximations or outright fabrications. In a consumer goods context, this might mean a sloppy product description or an off-brand tone. In life sciences, it could mean misrepresenting clinical data, overstating claims, or misleading stakeholders - mistakes that not only damage trust but could carry legal repercussions.

The challenge is compounded by the regulatory environment. Regulators expect claims to be backed by evidence and presented with careful accuracy. AI’s tendency to “hallucinate” or default to generic phrasing is not just unhelpful here - it is dangerous. A misplaced word can turn a promising discovery into an overblown claim, or worse, create liabilities for a company.

So how do we mitigate these risks while still capturing the efficiency and creative benefits of AI? A few principles are emerging as best practice:

1. Human-in-the-loop as non-negotiable
   AI can accelerate research, drafting, and ideation, but every output in life sciences marketing must be reviewed, validated, and contextualised by subject-matter experts. This isn’t optional. Human oversight ensures that nuance, regulatory compliance, and scientific integrity are preserved.
   

2. AI for scaffolding, not storytelling
   Use generative AI to structure content, extract key themes from large datasets, or draft outlines - but let experienced communicators and scientists craft the narrative. Think of AI as scaffolding that supports, rather than replaces, human expertise.
   

3. Guardrails and prompt engineering
   Training internal teams to frame prompts carefully, supply AI with validated source material, and constrain it to evidence-backed datasets helps reduce the risk of hallucination. In some cases, fine-tuning models on proprietary, verified scientific content may be the safest path.
   

4. Prioritising authenticity and trust
   In an industry where credibility is currency, polished but empty AI-generated marketing won’t cut it. The winners will be those who can harness AI’s efficiency without losing the authentic, human voice that builds trust with scientific, clinical, and investor audiences.
   

5. Building hybrid workflows
   The most forward-thinking teams are already creating hybrid processes where AI augments but never replaces human judgement. For example, AI might suggest variations of headlines or condense dense journal articles into draft summaries, which are then refined by medical writers or marketing professionals to ensure both accuracy and resonance.
   

In short: AI in life sciences marketing for pioneering companies is best treated as an assistant, not an author. It can speed up the mechanics of content production, but it cannot shoulder the responsibility of communicating pioneering science within a high-stakes, tightly regulated industry.

The companies that succeed will be those who embrace the productivity benefits of AI, while doubling down on human expertise, rigorous quality control, and a commitment to scientific truth.

Moreover, as AI generates more content in the wild, customers are growing skeptical. People are increasingly aware that the article they’re reading or the product review they see might have been machine-written. This puts a premium on transparency and authenticity from brands. Building trust is already one of the biggest challenges scientists and marketers face in an AI-shaped world. If your audience suspects that all your content is just auto-generated fluff, you’ll lose them. We all want to feel a human behind the message – someone who truly understands our problems and is accountable for the solutions.

So, use AI to do the heavy lifting, but put humans in charge of the steering and the storytelling.

That said, ignoring AI is not an option if you want to remain competitive. Pilot new AI tools and share best practices. Run internal hackathons to experiment with AI in your workflows.

As Naomi Walkland, a marketing leader, aptly said, “AI will make marketing faster… but it cannot replace the sense of connection people look for from a brand”. Trust, creativity, taste – those remain deeply human domains.

From Funnels to Ecosystems: Lessons from Precision Medicine

One of the key shifts in my marketing thinking today is the move from the old funnel mindset to an ecosystem or “web” model of engagement.

Traditional marketing funnels imagine a linear journey: you pour “leads” in at the top and hope some drop out the bottom as customers. But real customer behavior – especially in life sciences – is far from linear or predictable.

Buyers hop around: today they attend your webinar, tomorrow they read a peer’s blog mentioning you, next week they see you at a conference, and six months later they’re suddenly ready to talk. It’s messy and interconnected, more like a neural network than a straight line. I’ve come to see our marketing efforts as an ecosystem of touchpoints, all interconnected and reinforcing each other, rather than the stepwise conveyor belt of the funnel method.

This ecosystem approach shares similarities with the complexity of disease biology. Disease biology in chronic and complex diseases is non-linear, the cause is combinatorial – multiple genes and environmental factors interact to cause an outcome. Similarly, in marketing today, it’s usually not one single ad or one email that “causes” a prospect to buy. It’s the combination of interactions over time – the aggregate impression left by many small touches, sometimes over years – that drives decisions.

At PrecisionLife, our researchers use hypothesis-free combinatorial analytics to find patterns of factors that together drive disease. Modern life sciences marketing, requires a combinatorial mindset as well.

Instead of asking “Which single campaign got us this lead?” or “Which is the one tactic that converts?” we ask “What mix of touches moved this account over time?” Success comes from nurturing a network of engagements. Every piece of that network matters – remove one and the whole picture might not have come together. In essence, marketing success today is about repeated, distributed touchpoints across a network of engagement, not a one-and-done funnel drop.

To draw another parallel with precision medicine: In healthcare, there’s the concept of patient stratification – segmenting patients into subgroups based on their disease biology, so treatment can be tailored. Similarly, we have market segmentation in marketing: you stratify your audience (by role, by need, by behavior) to personalize your approach.

Another parallel: Precision medicine aims to deliver the right therapy to the right patient (often focusing narrowly on a genetic subtype of a disease). That’s akin to account-based marketing (ABM) in B2B, where you focus narrowly on high-value target accounts with very tailored campaigns. In fact, ABM has been my go-to strategy for big enterprise deals for over a decade – we assemble tiger teams to understand everything about one account and create highly specific content for just them. It’s marketing’s version of a targeted therapy.

No surprise, ABM is at last becoming table stakes for B2B marketers, just as precision medicine is becoming standard in care.

And finally, ecosystem marketing – building that web of many touchpoints – is analogous to combinatorial analytics: looking at the whole network rather than single pathways. In disease, that yields deeper insights and more personalized interventions. In marketing, it yields a resilient brand presence and more organic, self-driven buyer journeys.

Embracing this ecosystem mindset requires some changes. Break down silos between marketing programs and view everything holistically. Content teams, product teams, communications teams – all must collaborate so that the message from the conference booth aligns with what someone reads on the blog the week after.

We also have to get comfortable with nonlinear metrics. Instead of obsessing that every lead came directly from source X, Y or Z, we look at influence across the journey.

Multi-touch attribution models (imperfect as they are) can help demonstrate how different channels contribute. I celebrate things like a prospect mentioning “I’ve been seeing your content everywhere” even if they can’t recall exactly where. That diffuse awareness is a hallmark of the ecosystem approach working.

Switching from funnel-thinking to ecosystem-thinking is challenging, especially when boards and bosses still love straightforward funnel reports. Time must be spent educating stakeholders that a repeat exposure model is more realistic. It’s less about moving someone down predefined stages and more about ensuring your brand is consistently present in the right places when they are ready to engage.

The new rule here is: build presence, not just pipeline. Create a marketing environment that prospects can live in and learn from, rather than trying to trap them in a funnel. It might sound a bit grandiose and it certainly requires thorough planning, time, resources and dedication again, but it pays off. Some of the best leads I’ve ever landed came through what I call “gravity” – the sheer pull of an ecosystem that drew them in over time, rather than a single push. And those leads are more likely to close faster and stay longer.

The Modern Marketing Team

None of this is possible without the right team. The days when one person could handle “marcomms” are long gone. As the scope of life science marketing expands, so do the skill sets needed on our teams.

The stereotype of the marketer as just a creative wordsmith or event planner is long gone (if it ever was true). In my experience, building a high-performing marketing team today means assembling a diverse multi-disciplinary group – part scientist, part storyteller, part media production, part data analyst, part strategist. When done right, the team operates like an R&D unit in its own right, experimenting and innovating in how we engage the market.

One evolution I’ve led is actively hiring for scientific literacy. In a domain like biotech or precision medicine, you simply must have people who get the science. Some of the best marketers I’ve worked with were former bench scientists or had advanced degrees in biology, who then discovered a passion for communication.

Beyond subject matter expertise, product marketing has become a pivotal function in my teams. Product marketers act as translators between the lab and the market – distilling technical features into customer-centric benefits and narratives and executing strategies to their verticals.

In life sciences, this is especially crucial because the products are complex. A common mistake is companies leading their marketing with technical specs (“Our assay has X sensitivity and Y throughput”) without explaining why those specs matter. A great product marketer should ensure features connect to value.

Hand in hand with that is a renewed emphasis on storytelling and narrative communications. In a world awash with data and info, a compelling narrative cuts through. Invest in people who can craft a story arc around technology: what is the human impact? How does it fit into the broader journey of progress in your field?

And increasingly, modern teams need to operate as their own in-house media production unit. Content creators are no longer just writing blogs or designing PDFs – they’re producing multi-format, multi-platform campaigns that span video, podcasts, social shorts, interactive tools, webinars, and immersive presentations. They tailor each asset for a specific stakeholder group: a one-minute animation for a busy clinician, a deep-dive whitepaper for a translational scientist, a podcast interview for a business development lead. The ability to match message, medium, and audience is a core competitive advantage.

Earlier in my career I built a small internal studio to stop outsourcing every piece of creative. At first it felt risky trying to produce broadcast-quality video and design in-house. But it meant we could respond faster, experiment more freely, and produce content that felt truer to our brand voice at a time when video was still a novelty. The volume and quality of what we delivered went up, but more importantly, our credibility grew with stakeholders because they began to see and hear our experts explain our science.

Content creators can become the media arm of a business, ensuring that every scientific insight is expressed in the format most likely to resonate with its intended audience. Combined with scientific literacy, product marketing, and narrative strategy, they allow the modern marketing team to provide bandwidth across verticals and support business growth.

The Rise of RevOps

In this age of AI and complex buyer ecosystems, I see the role of marketing itself within organizations transforming.

Marketing today not only generates demand but also plays air-traffic control across the customer lifecycle. We’re expected to bring insights from customer data to the product team (“Here’s what our market is asking for that we don’t offer”), to equip sales with the right content at the right time, and to support customer success with materials to drive upsells or renewals.

It’s a challenging load but more than ever, marketing has the opportunity to be the engine of growth and the glue aligning go-to-market teams. The job isn’t just to make things look pretty or get vanity metrics up, it’s to drive growth.

Enter Revenue Operations (RevOps) - an essential component of modern marketing and commercial teams. RevOps is all about breaking down the silos between marketing, sales, customer success, and product, to drive cohesive growth. Essentially, it’s a recognition that we win or lose as one team, and marketing often sits at the center of this.

With AI, the role of RevOps can become even more transformative. Predictive tools help automate workflows, score leads and accounts, forecast with greater accuracy, and flag churn risks before they materialize. Generative AI now personalizes outreach at scale, drafts talk tracks, and enriches CRM records. And Agentic AI is beginning to take on autonomous execution - running cadences, scheduling follow-ups, maintaining data hygiene, even nurturing early-stage demand - always with human oversight. This is a step change: RevOps no longer just reports on what happened, it builds dynamic, living systems that adapt in real time to support revenue growth.

The RevOps professional becomes both architect and conductor. They design the shared data layer that underpins the customer journey, set governance and definitions so everyone measures success the same way, and orchestrate the tech stack so tools work together rather than in silos. They prioritize high-impact use cases, test and scale them quickly, and enable teams to trust and use AI responsibly.

Most importantly, RevOps reframes accountability. Instead of “marketing’s numbers” versus “sales’ numbers,” it’s our number - pipeline velocity, conversion rates, retention, expansion. The measure of success is not activity but impact: a million impressions with zero pipeline is a miss; one targeted program that lands a strategic partner is a win.

Some Things Never Change

With all this talk of new rules, it’s important to emphasize what hasn’t changed – the timeless fundamentals of marketing. I’d argue these fundamentals are more critical than ever, because they keep us grounded amid rapid change.

Essentially, marketing is still about understanding people and communicating value to them effectively. So while you deploy AI tools and spin up podcasts, you can’t lose sight of core strategy: segmentation, targeting, positioning, and good storytelling.

If there’s one piece of advice I’d pass on to any marketer, it’s this: your strategy is only ever as strong as your understanding of your customers.

Segmentation and targeting – the art of knowing your audience and focusing on the right ones, remains the strategic bedrock. Constantly refine your buyer personas and market segmentation. For example, within “pharmaceutical executives” as a segment, there’s a world of difference between a Chief Medical Officer concerned about clinical outcomes and a Chief Information Officer focused on data and integration. I read recently that 42% of prospects are frustrated by impersonal, one-size-fits-all content. That’s nearly half our audience saying “talk to me.” Failing to segment and map stakeholders is not just a missed opportunity, it can alienate potential customers.

Next, positioning – carving out that unique space in the customer’s mind that only your solution occupies. In life sciences, where almost all companies lay claim to innovation, it’s even more crucial to be clear about your differentiation. I’ve often used the exercise from Geoffrey Moore’s playbook: fill in the sentence “For [target customer] who [need/problem], [our product] is [category] that [unique benefit].” It’s simple, but if you can’t do this succinctly, you have a positioning problem.

And, storytelling. Some of the greatest scientific insights have been conveyed through elegant narratives (think of Darwin’s voyage, or Rosalind Franklin’s Photograph 51 – the stories around them propelled the science into public consciousness). In marketing, our use of storytelling is what gives meaning to the data and features we promote. Storytelling is how we turn our value proposition into something people feel, not just understand. It creates memory and emotion, which drive decisions as much as logic does.

Another core strategic pillar: trust and credibility. In life sciences, credibility is currency, earned over time by being honest, backing claims with evidence, and sometimes admitting what you don’t know yet. This doesn’t change with technology. We still need references, case studies, KOL endorsements, and data transparency. If we cite a reference, it better be real. If we claim a benefit, we should have clinical and analytical evidence to back it up.

The fundamentals of good marketing strategy remain as relevant as ever. We just apply them now to new channels and with new tools. It’s quite like how in science, the scientific method still underpins research, even if the lab equipment gets smarter.

When in doubt, go back to basics: know your audience, have a clear value story, and communicate it honestly and emotionally. That formula still works.

Future Forward: Leading with Confidence, Speed, and Trust

Life science marketing is headed into an era that will be defined by even smarter technology, faster cycles, and a continual battle for trust. How do we lead amid such rapid change? I believe it comes down to embracing continuous learning, staying true to our principles, and being fearless about innovation – all while keeping humans at the centre of everything we do.

One thing I’m certain about is that AI will continue to evolve and become an even bigger part of our workflows. We’ll likely see AI “co-pilots” for every facet of marketing – AI that can brainstorm campaign ideas, AI that can auto-personalise a website for each visitor, AI that can predict market shifts. The marketing leaders of tomorrow must become adept AI conductors, orchestrating the use of AI across their teams effectively. In practice, that means developing a keen sense of where AI adds value and where human intuition prevails.

Speed still remains a competitive moat. The companies that can execute faster – whether launching campaigns, responding to trends, or pivoting strategy – will win market share. However, speed without direction is a recipe for missteps. So the challenge is staying agile without losing strategic focus. Instil agile methodologies such as something akin to sprints for campaign cycles and champion a culture that is not afraid of quick iteration. We have to get comfortable with launching pilots, seeing some fail, learning, and iterating – basically applying the lean startup mindset within marketing.

In an age where AI can fake pretty much anything, customers, whether B2B or B2C, will gravitate toward brands they feel are genuine, transparent, and value-driven. For us in life sciences, that means doubling down on ethical marketing. Being clear about how we use data, respecting privacy, and never misrepresenting what our products can do. It also means cultivating community and thought leadership, not just for the sake of leads but to genuinely contribute knowledge to our field.

I also anticipate the lines between departments continue to blur. Perhaps in the future, we won’t even have a “marketing department” in the traditional sense. We might have a Growth and Engagement team that includes marketers, sales, customer success, and product experts working as one unit from day one of a customer’s journey to beyond purchase. It’s about organising around the customer journey rather than internal functions.

Finally, a word on confidence amid change and uncertainty: it stems from purpose. I remind myself why I got into this field – to connect life-changing science with the people who need it most. When you believe in the mission, you can navigate uncertainty within greater confidence. In the past I’ve advised my teams to understand the science, yes, but also understand the impact. Visit a lab, talk to patients if you can, internalise the human impact of the products you market. The impact is what drives me to innovate in my marketing because I want the world to know about these solutions when they’re ready.

Leading in the age of AI, speed, and trust isn’t about chasing every new tool or trend. It’s about holding fast to the fundamentals – understanding your customers, telling authentic stories, and building trust – while using new technologies to move faster and reach further. The “new rules” of life science marketing aren’t a static playbook; they’re a discipline of continuous learning and adaptation.

If we get that balance right – science and storytelling, data and empathy, speed and trust – marketing doesn’t just keep pace with change, it helps shape the future of healthcare. That, to me, is the real opportunity in front of us: to connect groundbreaking science with the people who need it most, and to do it in ways that are as innovative as the discoveries themselves.

Signal over noise.

Thanks for reading Signal Over Noise
If you made it all the way here, either you’re deeply interested in the subject or you’re just too polite to stop scrolling – either way, I’m grateful.

If you think this piece is worth sharing, please do. It helps get these ideas in front of the people who can actually do something with them (or at least argue with me about them in the comments).

Precision medicine – “the right treatment for the right patient at the right time” – is the rallying cry of modern healthcare. It’s also one of the most under-explained concepts in medicine today. For researchers, it’s a scientific inevitability. For investors, it’s a bet on smarter, more predictable returns. But for clinicians, payers, and patients, it can still sound like a distant, expensive experiment.

This isn’t a question of better branding or sharper taglines. It’s about finding ways to make the science meaningful to the people who need to use it and trust it. If we can’t explain precision medicine in ways that feel relevant and actionable, we risk slowing its adoption and missing opportunities to improve care when it’s most needed.

The AI double standard

AI and precision medicine share an awkward similarity: both promise to fundamentally reshape how we solve problems. But only one of them is allowed to be vague.

AI companies can get away with sweeping claims – “transformational,” “revolutionary”, “solving everything” – without having to explain the plumbing. Precision medicine can’t.

Partly, that’s because medicine is held to a higher evidentiary standard – and rightly so. But it’s also because AI’s champions have mastered narrative framing. They speak in terms of inevitability (“It’s coming, get ready”), scale (“This will touch every industry”), and familiarity (“Your phone already uses AI every day”). They create metaphors and mental shortcuts. They sell the story before they sell the specs.

Precision medicine has mostly done the opposite – leading with technology, acronyms, and statistical models. We explain how it works, but not why it matters in human terms. James Tabery, author of Tyranny of the Gene, notes that even Gleevec – hailed as the “poster child” for personalized medicine – came with caveats. It worked brilliantly in one context, but failed to generalise. Add in aggressive pricing and you have a public narrative that quickly soured. The lesson isn’t “don’t promise too much,” it’s “explain what’s realistic, and for whom, before others define it for you.”

Until precision medicine finds a common, everyday language – the kind AI has – it will remain easier to dismiss than to adopt.

Beyond Oncology – precision’s unexplored terrain

Precision medicine has been tethered to oncology for over two decades.

That’s not a bad origin story – cancer was the logical proving ground for targeted therapies and companion diagnostics. But it’s become a communications straitjacket. Ask the average clinician or policymaker about precision medicine, and they’ll answer with a cancer example.

The reality is that the very same approaches can now be applied to cardiovascular disease, autoimmune disorders, respiratory disease, metabolic syndromes, and neurodegenerative conditions. Adam Platt, VP of Translational Science and Experimental Medicine for Respiratory and Immunology at AstraZeneca, recently observed that precision approaches in chronic disease have “lagged behind” oncology, but that new tools and datasets are closing the gap.

Take type 2 diabetes. Today’s treatments are largely trial-and-error, with patients cycling through drug classes until something works. Genomic and proteomic insights could allow us to pre-select the therapy most likely to succeed based on a patient’s metabolic subtype.

In heart failure, biomarkers could stratify patients into those who will respond to beta blockers versus those who need alternative mechanisms.

In rheumatoid arthritis, a cytokine profile could determine the most effective biologic from the outset, instead of cycling through three or four until symptoms improve.

The science is moving – but public perception is frozen in the oncology era. Until we widen the lens, we’ll keep underestimating both the scope and urgency of what precision medicine can do.

The Business Fear – demystified, demolished

Within pharma boardrooms, precision medicine can trigger a reflexive flinch:

“But won’t that shrink our addressable market?”

The concern isn’t irrational – segmentation can look like commercial self-sabotage when you’re used to dreaming about blockbusters.

The flaw in that thinking is it ignores the cost of failure. A late-stage drug that fails in Phase III wipes out not only years of sunk R&D but also billions in direct expenditure and untold opportunity costs. Compare that with a drug that works spectacularly for 30% of patients:

• Faster regulatory approval because the trial data shows a clear, high-response signal.

• Higher reimbursement odds because payers can see the cost-effectiveness in advance.

• Rapid adoption because clinicians can have companion diagnostic tools, driven by patient stratification biomarkers, to identify eligible patients.

A 30% success rate in a large patient population can be worth billions annually. And those wins have a compounding effect – success breeds further investment, strengthens pipeline confidence, and builds the brand equity needed to expand indications.

The risk of doing nothing – of clinging to an “all patients or bust” mindset – is far greater.

Alzheimer’s – breaking a monolith is progress, not defeat

Few diseases illustrate the perils of the one-size-fits-all approach better than Alzheimer’s. It’s been the graveyard of countless drug candidates, with failure rates north of 99%. Billions have been poured into therapies aimed at treating all patients, despite decades of evidence that the disease’s biology varies significantly between individuals.

Alzheimer’s is not one disease – like most complex, chronic diseases, it’s multiple biological endotypes hiding under the same clinical label: amyloid-driven, tau-driven, vascular, inflammatory, and mixed forms. A universal cure is almost certainly impossible. But targeted therapies for each subtype? That’s feasible – and transformative.

Imagine an amyloid-clearing drug for the ~30% of patients whose disease is driven primarily by amyloid plaques. A vascular-protective therapy for those whose dementia is driven by blood vessel pathology. An anti-inflammatory approach for another large subset. None of these would be a “cure” for all, but together they could help hundreds of millions – and generate tens of billions in annual revenue for their developers.

Breaking Alzheimer’s into smaller, treatable pieces isn’t giving up. It’s playing the odds intelligently.

From COVID swabs to mechanistic matchmaking

One of the overlooked legacies of the COVID-19 pandemic is the infrastructure it created for rapid, scalable, low-cost sample collection. At-home nasal and saliva swabs became routine for hundreds of millions of people. The systems to distribute, process, and return results at scale are now proven.

That same infrastructure can be repurposed to collect biological samples for precision medicine.

Instead of detecting viral RNA, these swabs could identify genetic variants, protein signatures, or metabolomic markers associated with disease mechanisms. Coupled with advanced analytics like combinatorial analysis, this allows for:

• Rapid identification of suitable patients for clinical trials.

• Earlier and more accurate diagnosis.

• Matching approved therapies to the patients most likely to benefit.

The beauty is the familiarity – patients already know how to use the tools. The challenge is explaining that what was once a COVID test could now be the gateway to a treatment plan designed uniquely for them.

Voices from the field – real stories, real gaps

The most compelling arguments for precision medicine aren’t in white papers – they’re in the lived experiences of patients and clinicians.

Take Bryce Olson, diagnosed with advanced prostate cancer. Standard treatments failed him. By sequencing his tumour, he found a trial for a PI3K inhibitor that matched his cancer’s specific mutation. It bought him more time – time that blunt-force standard of care could not. His story isn’t just about science; it’s about agency.

On the other side of the coin is Dr. Lillian Siu’s account, shared with the Financial Times. She’s faced the wrenching moment of telling a patient, “I know this drug could help you… but it isn’t covered.” It’s a reminder that the communication gap isn’t just scientific – it’s financial and systemic. Without alignment between innovation, reimbursement, and delivery, the science stops short of the patient.

Equity, trust, and the ‘Missing Ones’

Precision medicine risks becoming a two-tier system if access is uneven. Dr. Kashyap Patel has warned that precision approaches, without equitable implementation, “may live shorter lives compared to those represented in drug development.” The danger is that minority and rural populations – already underrepresented in trials – are also the last to benefit from precision diagnostics and therapies.

Building trust requires more than translating leaflets into multiple languages. It means engaging communities directly, respecting cultural perspectives, and being transparent about how data will be used. The Alaska Native Tribal Health Consortium, for example, used locally produced videos, workshops, and community champions to explain genomics in relatable terms. Participation and acceptance increased – because the message wasn’t parachuted in, it was co-created.

Leading the reframe – a communication playbook that works

• Clinicians need biomarker information embedded into decision tools, not buried in journals. They need language that turns molecular probability into patient relevance.

• Payers need a return-on-investment story. The Netherlands’ DRUP model – in which pharma covers the first cycle of an expensive targeted therapy, and insurers pay only if the patient responds – shows that creative payment structures can unlock access and make precision affordable.

• Patients need to see themselves in the story. They don’t want risk allele percentages; they want to know, “Does this mean I’ll have more good years?”

• Policymakers need the macro view – that precision medicine isn’t a boutique luxury, it’s one of the few viable strategies for controlling chronic disease costs in an aging population.

Iceland’s deCODE – trust as a force multiplier

The deCODE genetics project in Iceland remains one of the best examples of scaling precision medicine through trust. By returning actionable results – such as BRCA2 mutation status – to participants, it turned a research program into a national early-warning system. Hundreds of people took preventive action that likely saved lives.

The key was transparency: people understood what was being collected, why, and what they’d get back.

It’s proof that when people trust the system, they don’t just tolerate precision medicine – they can embrace it.

Privacy, bias, and the burden of trust

Precision medicine is data-intensive by definition. That makes it uniquely vulnerable to breaches of privacy, algorithmic bias, and unintended social consequences. These risks aren’t hypothetical – they’re already visible in examples where genetic data was used in ways participants didn’t anticipate.

Public trust will be won or lost on how openly we acknowledge and address these risks. Ignore them, and precision medicine will be seen as something done to people rather than for them.

Final thought

Precision medicine doesn’t need a new slogan – it needs a clearer conversation.

The science is already here in many areas, but too often it’s discussed as if it’s still on the horizon. We need to bring it into the present, using real examples, relatable language, and a focus on what it changes for patients, clinicians, and health systems.

That means making the benefits visible, the pathways understandable, and the trade-offs honest. The more we can do that, the faster precision medicine will move from promise to practice.

Signal over noise.

Thanks for reading Signal Over Noise
If you made it all the way here, either you’re deeply interested in the subject or you’re just too polite to stop scrolling – either way, I’m grateful.

If you think this piece is worth sharing, please do. It helps get these ideas in front of the people who can actually do something with them (or at least argue with me about them in the comments).

You can subscribe for free to get future posts direct to your inbox. No spam, no pop-up ads for shoes you never wanted, just science and stories I hope are worth your time.

And if you’ve got thoughts, questions, or the irresistible urge to tell me I’m wrong, jump into the comments – I read every one.

All content on Signal is free and open-access. The bill is paid in curiosity.

Over the next few years, the pharmaceutical industry is heading toward a financial drop-off so steep it has earned its own dramatic nickname: the patent cliff.

If you’ve never heard of it, picture a reservoir that’s been filling for years behind a dam. Pharma companies invest huge sums and years of effort to get the water level up – that’s the R&D phase. Once a drug is launched, the floodgates open, and revenue pours through. But the patent expiry is like the dam suddenly crumbling. The flow doesn’t just slow – it surges out uncontrollably, and in months what was once a deep, steady reservoir of income becomes a shallow trickle.

Between now and 2030, more than $200 billion in annual sales is at risk as some of the world’s best-selling drugs lose their patent protection. For a handful of big pharma players, that’s more than a quarter of their revenue disappearing in a few short years.

This isn’t the first patent cliff that the pharmaceutical industry has faced – but it is one of the largest and sharpest in history. And it’s not just a financial story. How companies respond will shape which diseases get attention, which innovations make it to patients, and who dominates the industry in the 2030s and beyond.

Can genomics and new precision medicine approaches bring hope, and new ways to cross the void?

The ticking patent clock

A new drug is typically protected by a patent for 20 years from the date the patent application is filed. That clock starts well before any patient gets access to the medicine.

Developing a drug – from the first promising molecule to regulatory approval – takes 10 to 12 years on average. That leaves maybe 8–10 years of exclusive market time to recoup billions in R&D spending, pay for manufacturing and distribution, and hopefully make a profit.

Once the patent expires, generic manufacturers (or biosimilar makers, in the case of biologics) can launch equivalent versions at a fraction of the cost. Prices crash, market share erodes, and brand-name sales often drop by 80% or more within months.

A company that once earned billions each year from a single drug can find that revenue reduced to a rounding error on its quarterly report almost overnight.

Why the cliff is here now

The wave we’re about to hit is the result of blockbusters launched in the early 2000s and 2010s – many of them transformative in their fields – all reaching the end of their exclusivity at around the same time.

📊 The Coming Patent Cliff — By the Numbers

Global sales at risk (2024–2030): ~$200 billion
Average patent life: 20 years from filing
Average time to market: 10–12 years (leaving ~8–10 years of exclusivity)
Typical sales drop post-patent: 80% or more within months

Top drugs losing exclusivity soon:

• Eliquis (BMS/Pfizer) - $13B annual sales - patent expiry 2026

• Keytruda (Merck) - $29.5B annual sales - patent expiry 2028

• Opdivo (BMS) - $10B annual sales - patent expiry 2028

• Humira (AbbVie) -$21B peak sales - US exclusivity ended 2023, biosimilars now launched

• Ibrance (Pfizer) - $4.7B annual sales - patent expiry 2027

Why it matters:

• Huge loss of revenue in a short period

• Pressure to fill pipelines fast

• Shift toward late-stage programs with higher probability of success

• Opportunity for precision medicine to de-risk earlier discovery

The late-stage safety net – and its limits

When faced with this kind of shortfall, it’s tempting for pharma companies to double down on late-stage development programs. After all, Phase 3 trials are expensive but the probability of success is far higher than in early discovery. You might get a new revenue stream in 3–5 years instead of a decade.

The problem is that if everyone focuses on late-stage assets, the early pipeline dries up. And without early discovery, you have no late-stage candidates 10 years from now. The industry risks becoming like a farmer who stops planting seeds because they need to sell crops immediately.

Enter precision medicine

Fortunately, we now have tools that can make early discovery and late-stage development both faster and more likely to succeed. Genomic evidence, deep mechanistic insights, and patient stratification biomarkers can help us:

• Identify the right targets earlier

• Match them to the right patients

• Avoid costly failures in Phase 2 or 3

• Move successful drugs to market faster

One of the most compelling stories of this in action comes from AstraZeneca.

The Tagrisso turnaround

What sounds like it might be a mid-90's dance move, is in fact an excellent example of precision medicine done well - shout out to for bringing it to my attention. 2003, AstraZeneca launched Iressa, an EGFR-targeted therapy for non-small cell lung cancer. Expectations were sky-high. But the drug underperformed and, within two years, it was pulled from the US market.

Instead of abandoning the program, AstraZeneca doubled down on the science. Researchers identified a subset of patients with specific EGFR mutations who responded dramatically to treatment. That insight – a classic example of precision medicine – laid the groundwork for Tagrisso, approved in 2015.

Fast forward to 2024: Tagrisso generated $6.6 billion in global sales, accounting for 12% of AstraZeneca’s total revenue. It’s now a backbone therapy for EGFR-mutated lung cancer across early-stage, locally advanced, and metastatic disease.

Tagrisso wasn’t just a win for patients. It was proof that genomic and mechanistic insights can rescue a failing program and turn it into a blockbuster.

Beyond oncology

While oncology has been the poster child for precision medicine, it’s also one of the most crowded spaces in pharma. If companies want to avoid hitting another cliff in the 2030s, they’ll need to look beyond cancer to other areas where:

• The unmet need is massive

• The science is complex

• Historical attrition rates are high

CNS and neuroscience are prime candidates. Alzheimer’s, Parkinson’s, depression, schizophrenia – these are conditions affecting millions, yet treatment options are limited and often only modestly effective. Failures in these areas have been common and costly, but that’s exactly why they need a new approach.

Precision medicine could identify molecular subtypes of these diseases, find predictive biomarkers for treatment response, and match patients to therapies more effectively. It’s the same playbook that worked in oncology – just applied to some of medicine’s toughest challenges.

More time on the clock

There’s another benefit to getting the right drug to the right patient faster: more time on market under patent.

If precision medicine can shave years off clinical development by showing clear efficacy in a targeted population, companies can win back valuable patent life that would otherwise be lost to trial delays or failures.

Even after launch, stratification biomarkers and genomic insights can support new label claims, fresh patents, and targeted subpopulation indications – effectively extending the commercial life of a drug.

The next 20 years will reward the bold

Here’s the hard truth: not every company will survive the patent cliff intact. Some will patch the hole with in-licensing deals or acquisitions, others will scramble for short-term wins and still see revenue slide.

But the winners – the ones dominating 2035’s revenue tables – will be the companies that:

1. Invest in early discovery rather than abandoning it for short-term safety

2. Use genomic evidence, mechanistic insights, and biomarkers to de-risk programs early

3. Diversify beyond oncology into high-need, high-complexity disease areas

4. Leverage precision medicine not just to improve success rates, but to accelerate time to market

This isn’t about chasing one or two mega-blockbusters. It’s about building a pipeline of precision medicines across multiple therapeutic areas, creating a steady flow of impactful, high-value drugs for decades to come.

Signal over noise.

Thanks for reading Signal Over Noise.
If you made it all the way here, either you’re deeply interested in the subject or you’re just too polite to stop scrolling – either way, I’m grateful.

If you think this piece is worth sharing, please do. It helps get these ideas in front of the people who can actually do something with them (or at least argue with me about them in the comments).

You can subscribe for free to get future posts direct to your inbox. No spam, no pop-up ads for shoes you never wanted, just science and stories I hope are worth your time.

And if you’ve got thoughts, questions, or the irresistible urge to tell me I’m wrong, jump into the comments – I read every one.

All content on Signal is free and open-access. The bill is paid in curiosity.

Every major breakthrough in biotech begins deep in a lab, yet too often its promise falters when the moment arrives to explain it to the people whose belief matters most - investors, regulators, clinicians, patients. To be crystal clear, I’m not talking about translational science that moves findings from bench to bedside. I mean the craft of explanation. The science may be excellent, but without a narrative that resonates, it can be unseen, misunderstood, or mistrusted.

I’ve watched this happen up close. A team unveils a discovery that could accelerate development timelines or help identify the patients most likely to benefit. The data are strong. The methods are sound. Then comes the outward moment - the deck, the press note, the conference slot - and the room goes cool. Not because the work is weak, but because the audience cannot find themselves inside the story.

That gap is not a soft problem. It is a strategic one. Ideas live or die on whether the people who must act can understand, remember, and believe them.

Why complexity feels safe - and why it isn’t

Complexity is a comfortable fortress. A fifteen minute talk dense with acronyms and inferential statistics can feel like rigor and seriousness to our peers. But most of the decisions that determine whether a breakthrough prospers are made by people who do not live inside that fortress. The National Academies have been blunt about this for years: effective science communication means aligning strategy with goals, tailoring messages to audiences, and addressing complexity and uncertainty head on. It is not a knowledge dump. It is a decision aid.

That is easy to agree with in principle and hard to do in practice. It demands we begin not with what we want to say, but with what our audience needs in order to decide - a different starting point than most lab meetings or manuscript drafts.

“People don’t connect with science. They connect with people.”

Paul Sutter, an astrophysicist and experienced communicator, wrote an Undark essay that I keep recommending. He argues that the public is not losing trust because they cannot recite p values, but because they do not see scientists as knowable humans with values and motives. His line that “people don’t connect with science, they connect with people” should be pinned above every research comms plan. He also calls out a structural problem many of us recognise: institutions still do not reward communication, so engagement becomes a hobby rather than part of the job.

That diagnosis resonates in biomedicine too. When scientists stay silent or stay abstruse, other stories fill the vacuum. Some are simply more charismatic. Some are wrong but compelling. And some are malicious.

The uncomfortable bit about trust

The Association of American Medical Colleges has chronicled the collision between how science evolves and how the public consumes information. Their reporting on the ivermectin saga remains a case study in how narratives run ahead of evidence, then harden.

An initial, flawed preprint spread quickly, and even after retraction, belief persisted. The AAMC captured the deeper communication tension through a quote I wish every scientific leader would internalise:

“There’s a real tension if you’re a scientist trying to communicate with the public - projecting certainty and confidence, while recognizing that you are operating with a considerable degree of uncertainty.”

That is Stephen Joffe, MD, MPH, chair of the Department of Medical Ethics and Health Policy at the UPenn Perelman School of Medicine, and he’s right.

There is a second lesson from that series. The phrase “follow the science” may have been meant as reassurance, but it often landed as absolute instruction rather than an honest signal about evolving evidence. As Aaron Carroll put it recently, scientists provide data and insight, while policy requires value judgments. Conflating the two erodes trust.

Communication that improves the science, not just the slides

One of the most useful perspectives I have encountered comes from the Alan Alda Center. Sarah Wettstadt’s piece makes a deceptively simple point: doing the work of communication forces researchers to rediscover their “why.” That reflection can sharpen hypotheses, open collaborations, and even change a project’s direction. In other words, explaining your work better can make your work better.

There is a humility in that view that I like. It is not the marketer telling the scientist to become a brand. It is a scientist reminding the field that storycraft is part of inquiry because it clarifies purpose and tests relevance.

A real-world arc: when the audience is the mechanism

If you want a commercial illustration of how explanation and focus alter outcomes, consider the long arc of gefitinib. AstraZeneca’s drug first received accelerated approval in 2003 in non small cell lung cancer, only to be withdrawn from the US market after disappointing survival data in unselected patients. A decade later the story changed. The FDA approved the use of gefitinib in 2015 for patients whose tumors carry specific EGFR mutations, alongside a companion diagnostic to find them.

The mechanism had not changed. The audience had. Matching treatment to the right molecularly defined group - and telling that story clearly - resurrected a drug.

I bring this example up not because it is unique, but because it illustrates a pattern. Many of the most meaningful advances in modern medicine will be targeted and conditional. They demand communications that explain who benefits, who does not, and why that is a feature, not a flaw.

That is not spin. That is precision.

The structural incentives we swim against

When Paul Sutter says scientists are not rewarded for engagement, he is describing a system problem that bleeds into industry as well as academia. Press releases still overstate marginal results. Papers, not conversations, remain the primary currency of career progress. Outreach is still seen as a nice-to-have.

The National Academies again: engagement is learned, incentives matter, and the deficit model - the idea that people will choose the science if we simply give them more of it - does not work on its own.

Meanwhile, public trust has been trending in the wrong direction. Pew reported last year that the share of Americans who say science has had a mostly positive effect has fallen, and confidence in scientists to act in the public interest has declined since early pandemic peaks. The nuance matters across political groups, but the headline is consistent: trust cannot be assumed.

None of this is an argument for dumbing down. It is an argument for designing how we speak so that people can decide.

What better looks like in practice

I have seen three patterns repeatedly in programs that gained belief and momentum.

First, they start with a human problem, not a platform. The most engaging early conversations are not about architectures, pipelines, or validation metrics. They are about a stubborn clinical reality or a preventable cost. Only then do they show how the science makes a difference to that reality. The Alda Center’s emphasis on starting with motivation is not just a performance trick. It is a content discipline.

Second, they define the boundaries of the claim. What is known now. What is unknown. What would change your mind. The AAMC’s reporting described this well during the pandemic. Overstating preliminary signals wins short attention and loses long trust. Setting expectations honestly is slower in the moment and faster over a program’s life.

Third, the originators do the talking. You cannot outsource conviction. External communicators can help translate, shape, and stress test, but the most persuasive voices are the people who did the work and can explain why it matters, where it breaks, and what comes next. This is not a media training footnote. It is a leadership task.

Storytelling as part of the innovation

Here is the inconvenient truth. Most of the things we want - funding, adoption, trust - are second order effects of being understood. Being understood is a product of craft. Craft requires time and feedback. And time and feedback are scarce unless we treat communication as part of the work, not an after-action report.

That means building a habit of explanation into project milestones. It means pressure testing your narrative on people who do not share your internal references. It means aligning your story with the decision on the other side of the table. And it means recognising that narrative clarity is not a cosmetic overlay on excellence. It is part of what excellence now requires.

As a field, we can do more to shift the incentives. Tenure and promotion committees can recognise public engagement. Companies can link communication quality to program gates rather than leaving it to launch. Funders can ask applicants to articulate their why to non expert reviewers and test their understanding as seriously as their power calculations. None of this requires a manifesto. It requires will.

A final note on language and honesty

I am often asked whether we should stop saying “trust the science.” I do not think the phrase is the heart of the problem, but I do think it mistakes the role of science in public life. Science narrows uncertainty and expands the set of wise choices. It does not make the choice for us. Confusing those two invites backlash. As Carroll argues, we should be transparent about what evidence can and cannot tell us, then be equally transparent about the values that inform policy. That is how adults talk to adults.

We also need to accept that some of our most valuable results will be provisional for longer than a press cycle can bear. That is fine. People do not expect omniscience. They expect candor and care.

If the science is hard, the explaining must be worthy of it

We cannot make complexity vanish. We can make it navigable. The price of doing so is attention to story, audience, boundaries, and the human reasons behind the work. When we do that, funding conversations change. Adoption conversations change. Trust changes. And sometimes the path to impact opens not because the molecule is different, but because the world finally knows who it is for.

That’s not just marketing. That’s science.

Signal over noise.

Noah

Thanks for reading Signal Over Noise.

If you made it all the way here, either you’re deeply interested in the subject or you’re just too polite to stop scrolling – either way, I’m grateful.

If you think this piece is worth sharing, please do. It helps get these ideas in front of the people who can actually do something with them (or at least argue with me about them in the comments).

You can subscribe for free to get future posts direct to your inbox. No spam, no pop-up ads for shoes you never wanted, just science and stories I hope are worth your time.

And if you’ve got thoughts, questions, or the irresistible urge to tell me I’m wrong, jump into the comments – I read every one.

All content on Signal is free and open-access. The bill is paid in curiosity.

This is a safe space for anyone who’s ever asked, “Why isn’t this being communicated better?” - whether it was while reading a dense scientific paper, a pharmaceutical press release, or a health news article that somehow managed to be both alarmist and vague.

I’m Noah, a science communicator and life sciences marketer who’s spent the last decade and some translating between scientists, patients, policymakers, and business execs - often with a raised eyebrow and a mug of coffee in hand. I’ve worked across the biotech and healthcare ecosystem, trying to get important but complex ideas out of PowerPoint decks and into the real world, where they can actually help people.

So what is Signal Over Noise?

It’s a blog - “another one?” - I hear you sigh with enthusiasm.

Well, yes - about science with meaning - not just methods. It’s about the stories, the signals in the noise, the moments when science clicks because it connects with our lives, our health, and our shared humanity (you won’t find AI in my writing, folks).

Expect reflections on breakthrough research, rants about communication failures, and real talk about what it’s like working in (and writing about) biotech, precision medicine, and preventative health.

This isn’t a publication for experts - although you’re welcome too. It’s a space for the curious. For people who want to understand how science works, why it sometimes doesn’t, and what it means when it does.

I’ll bring you:

• Commentary on the latest in biotech, pharma, and health research

• Behind-the-scenes views from the trenches of life sciences marketing

• Explorations of how communication can change the course of medicine - or stall it

• A little humour, a lot of honesty, and zero tolerance for jargon

Why subscribe?
Because science isn’t just about discovery - it’s about connection. And if you’ve ever felt like the signal’s getting lost in the noise, you’re not alone.

Thanks for tuning in - let’s find the frequency together.

Signal over noise.

– Noah