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:
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.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.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.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.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.
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