A Splice of Life Science Marketing

Measuring What Matters: OKRs, KPIs and Stage Gates

Three years into a development program, R and D says it won, commercial says it lost, and regulatory says everything was fine. They were all measuring different things, and nobody caught it at the gate where it mattered. This episode is for life science product managers, commercial leaders, and regulatory affairs leads running stage-gated development programs. Jasmine Gruia-Gray explains why most teams write their failure mode into the program at the first gate by treating success criteria as a flat KPI list. She walks through nesting objectives, key results, and KPIs into one picture at different altitudes, why the bet has to be agreed in the room rather than circulated as a draft, and how the gate itself becomes the forcing function that earlier OKR rollouts never had. The one idea to remember: a regulatory milestone is not an objective. The 510(k) clearance date is a milestone on the critical path. The market position after clearance is the objective. What you will learn: * Why a flat list of KPIs called "success criteria" guarantees three functions will disagree about whether the program won. * How to nest the objective, three key results, and the function-owned KPIs into one picture at three altitudes. * Why agreeing the bet in the room beats circulating a draft that everyone signs and nobody owns. * What makes a gate-centred OKR structurally different from the planning-exercise rollouts that quietly died after Q1. * How to handle a competitor entering your segment six months in without reopening the objective. * The single first move for a product manager whose template has only a KPI field. Chapters: * [00:17] Where teams write the failure mode in at MS1 * [01:42] Nesting OKRs and KPIs instead of running parallel tracks * [02:34] Why the bet has to be agreed in the room * [03:38] What makes this different from OKR rollouts that died * [04:20] A regulatory milestone is not an objective * [05:06] When a competitor enters and the bet looks stale * [06:21] The product manager's first move at MS1 * [07:06] The same structure at every gate * [08:03] Where to find the full blog and book a consultation If this helped, watch to the end, subscribe for more life science marketing and commercialisation strategy, and read the full blog plus worked examples at strivenn.com.

Malignant Uncertainty and the Buyer Who Left the Room

Uncertainty is the system, and life science marketers who build buyer presence into every decision are better equipped to navigate it. Your buyer was in the room at the start of the process. By the time the brief lands in legal, they have left. For life science product marketers, commercial leads, and anyone navigating a launch in a market that stopped following the old rules. Matt Wilkinson and Jasmine Gruia-Gray unpack Mark Schaefer's concept of malignant uncertainty - the structural fog that makes every market assumption feel provisional. They test whether synthetic customers are a genuine solution to buyer presence erosion or a sophisticated way to lose an argument with authority. The conversation lands on a harder, more useful truth: the technology is downstream of the belief. Key idea: Uncertainty is not a flaw in the system - it is the system, and smart marketers build buyer presence into every decision. What you will learn: * Why Mark Schaefer's three types of uncertainty (objective, epistemic, and subjective) reframe how life science marketers should think about the fog they are already in * How synthetic customers make buyer insight quotable, queryable, and present through approval cycles - and why they are not a substitute for culture change * Why the real reason buyer presence erodes in approval cycles is a prioritisation and power problem, not a data problem * How to tell whether your synthetic customer is still accurate or has become an expensive echo chamber * What to do practically when you are six months from a launch and the rules have structurally shifted * What Mark Schaefer means by "leaders dispense hope" and why it is not the same as optimism Chapters: * [00:00] Introduction * [00:27] Mark Schaefer and malignant uncertainty * [01:54] The three types of uncertainty: objective, epistemic, and subjective * [02:53] COVID, survivorship bias, and customer centricity * [04:36] The emotional substrate beneath the surface request * [05:24] Synthetic customers and buyer presence in approval cycles * [06:00] Steel-manning the sceptic: is this just a sophisticated way to lose? * [07:53] The stale data problem: synthetic customers trained on yesterday's market * [09:23] Technology is downstream of belief * [10:56] Practical moves six months from a launch * [12:21] Leaders dispense hope * [13:02] A closing exercise for listeners Keywords: synthetic customers, buyer presence, life science marketing, malignant uncertainty, Mark Schaefer, voice of customer, commercial strategy, approval cycle, product launch, customer centricity, AI marketing tools, VUCA uncertainty Subscribe to A Splice of Life Science Marketing for fortnightly conversations at the intersection of commercial strategy and AI. Read Matt's blog post on buyer presence here [https://strivenn.com/thinking/uncertainty-is-the-way]. Find us on LinkedIn and visit strivenn.com/thinking [https://strivenn.com/thinking/] for more.

S2: Ep 17 When Did Your AI Stack Become Infrastructure?

Life science CEOs embedding AI in compliance workflows face regulatory switching costs, not just technical ones, when models change. Shownotes: You didn't make one big decision to hand control of your compliance workflow to an AI vendor. You made five small ones, and each felt completely reasonable at the time. By the time the model update arrived, the exit cost wasn't a sprint of prompt re-engineering. It was a revalidation programme. This episode is for CEOs and commercial leaders at life science tools companies who are scaling AI across their teams and have not yet drawn the line between experimental workflow and validated process. Matt and Jasmine walk through the story of Henry, a composite built from real conversations with life science tool CEOs, who adopted AI-first operations, hit a model deprecation event, and discovered that the productivity gains he had built his headcount decisions on were sitting on infrastructure he did not control. The conversation unpacks the five decisions that created the problem, the control layer architecture that solves it, and the two-column framework every CEO should run this week. The core idea: embedding AI inside a validated compliance workflow does not make you more productive. It makes you dependent. And the switching cost is not technical. It is regulatory. What you will learn: - Why each of Henry's five AI adoption decisions felt low-risk and why together they created a structural dependency - What changes the moment AI enters a GXP-adjacent validated process and why that is a different category of commitment - What a control layer is, why it matters, and how tools like Open Web UI sit in that role - How to split every AI tool you use into two buckets: validated process or experimental workflow - Why the humans who understood the process before AI ran it are not optional infrastructure - What question to ask before embedding any AI tool in a compliance workflow: if this changed tomorrow, could I swap it in a week? Keywords: AI governance life sciences, validated process AI, GXP AI risk, AI infrastructure life science CEO, model deprecation compliance, control layer AI, AI workflow switching costs, life science marketing AI, regulatory AI risk, AI stack governance, life science tools company AI, AI compliance workflow Subscribe to A Splice of Life Science Marketing for sharp, commercially grounded conversations on strategy, AI, and go-to-market for life science brands.

S2: Ep 16 Falsification Logic and the Invisible Buyer

Scientist buyers use falsification logic -- one weak claim destroys your whole case -- so claim selection and buyer presence beat validation volume every time. Shownotes Your claims list passed legal, survived MS3, and still didn't land. The problem wasn't your evidence -- it was which claim was leading and whether your buyer was still in the room when you chose it. This episode is for life science marketers, product managers, and commercial leaders building claims hierarchies for scientist buyers. Jasmine and Matt unpack why scientist buyers apply falsification logic to commercial claims -- meaning one weak point invalidates everything before it -- while most commercial teams build on accumulation logic. They explore how organisational gravity edits the buyer out of decisions before anything reaches the field, and how a synthetic customer built from real voice-of-customer data can keep buyer presence active throughout the review process. Key idea: Your buyer has already left the room before the claims list is written, edited out by organisational gravity -- and a synthetic customer keeps them present throughout. * Why accumulation logic and falsification logic produce opposite commercial outcomes for the same claims list * How a single weak claim destroys scientist buyer confidence regardless of how many strong ones precede it * Why claim selection is a commercial judgement, not a validation problem * How organisational gravity pulls messaging toward what is safest rather than what the buyer needs * What a synthetic customer is, what it is built from, and what it cannot replace * How to test whether your buyer has already left the room before your next review cycle Keywords: life science marketing, scientist buyers, falsification logic, claims hierarchy, MS3 review, synthetic customer, organisational gravity, product marketing, voice of customer, commercial claims, accumulation logic, buyer presence If this episode changed how you think about your next review cycle, subscribe to A Splice of Life Science Marketing and visit strivenn.com for the full blog posts referenced in this episode. We answer every message.

S2: ep15: Your Next Buyer Might Be an Algorithm. Is Your Brand Ready?

AI agents are shortlisting life science suppliers before humans get involved - brands invisible to AI are losing demand they cannot measure. Shownotes: Your next buyer might never visit your website. AI agents are already shortlisting suppliers, summarising product pages, and filtering out brands with poor machine-readable content - before any human in procurement gets involved. For life science marketers and commercial leaders who want to understand what the shift to AI-mediated discovery actually means for their brand right now. Matt Wilkinson's blog post "Your Next Buyer Might Be an Algorithm. Is Your Brand Ready?" sparked a sharp debate between Matt and Jasmine Gruia-Gray. The conversation moves from the Meta acquisition of Moltbook and OpenAI's hire of the OpenClaw engineer through share of model measurement, Generative Engine Optimisation, prompt injection risk, and the first mover argument - testing where the evidence is solid and where the hype needs qualifying. Key idea: AI agents are increasingly making shortlisting decisions before humans get involved - life science brands with no AI visibility strategy are losing demand they cannot even measure. What you will learn: * What the Meta acquisition of Moltbook and OpenAI's OpenClaw hire signal about the commercial infrastructure being built for AI agents * What "share of model" means as a concept - and the honest measurement constraints that come with it * How Generative Engine Optimisation differs from SEO and which version is deliverable for a small marketing team * How prompt injection works, what Microsoft Defender found in 60 days of monitoring, and where the real competitive risk sits * Why citation compression means AI visibility has no page two - and what Strivenn's SLAS 2026 data reveals about where life science companies currently stand * The first mover argument examined critically - including the risk-adjusted case for acting now even with infrastructure still years from maturity Chapters:[00:42] Introduction and framing[02:45] Share of model - what it is and the measurement challenge[06:17] Attribution constraints and the agent monitoring opportunity[08:02] GEO versus SEO - overlap, divergence, and what is deliverable[10:04] Cross-functional dependencies and schema implementation reality[12:44] Prompt injection risk - competitive threat or reputational hazard?[15:35] Building authority versus near-term competitive exposure[18:32] First mover advantage - the honest version of the investment case[20:26] Citation compression and the cost of waiting[22:48] Practical next steps Keywords: AI discoverability, life science marketing, share of model, generative engine optimisation, GEO, prompt injection, AI agents, B2AI, citation compression, agentic AI, AI recommendation visibility, life science commercial strategy If this episode shifted how you think about AI visibility for your brand, subscribe to A Splice of Life Science Marketing for new episodes every fortnight. Read the full blog post and explore the AI Discoverability Hub for primary research, frameworks, and a practical audit at strivenn.com.