S2 Ep20: AI Search Reads Structure, Not Content Quality

S2 Ep21: A Brand is Trust

Pride in the output is the human test AI cannot replace, and it is what protects life science brands. Somewhere in your marketing stack right now, an AI is publishing faster than anyone can check it. This episode is about the quiet way that speed erodes trust before you notice it has gone. Who this is for: CEOs, commercial leaders and marketers at life science tools and diagnostics companies who are scaling content with AI and cannot afford to be caught wrong. Matt Wilkinson and Jasmine Gruia-Gray unpack why brand is trust, why a single drifted claim costs more in front of a scientist than in any consumer market, and how a claim-by-claim fact-check table, discernment and synthetic customers keep AI output dependable rather than merely fast. Matt also shares the story behind his Marketing Week CX50 recognition as one of the top ten life sciences marketers in the UK, and why his first instinct was to assume it was a phishing scam.

S2 Ep20: AI Search Reads Structure, Not Content Quality

For life science marketers: why AI engines judge your pages on structure and schema, and how to stop yours being misread.We ran an AI search audit on its own website and the tool recommended building three pillar pages that already existed. The cause was five characters at the end of a URL, which made three of the company's most important pages invisible to the AI engines that buyers now use to build shortlists.This conversation is for life science marketing leaders, brand managers and commercial teams who are investing in serious content and want that content to be found by AI search.Matt Wilkinson and Jasmine Gruia-Gray work through a real diagnostic on strivenn.com, from the misclassified pillar pages to the slug and schema gaps behind them. They cover why AI engines read structural signals rather than content quality, what citation compression means for visibility, and how to move the fix out of the SEO backlog and into a revenue conversation.Key idea: AI engines classify pages by structural signals like URL slugs and schema, so even your best content stays invisible if the architecture reads as tactical.What you will learn:How a single branded URL suffix can make an authority page read as a throwaway campaign asset to an AI crawlerWhy content quality and content visibility are coming apart as AI search growsWhat citation compression is and why being absent from a three to five brand result set matters more than rankingHow to run the diagnostic yourself with Claude Cowork instead of paying for an SEO agencyHow to reframe a schema fix as a revenue visibility project so it clears the developer backlogThe first move for a brand manager who runs the test and finds their pages missingChapters:[00:19] The audit that made us stop and think[01:53] What the SEO audit got wrong[02:43] Five characters at the end of a URL[03:20] AI reads a different set of signals[04:07] The minority-behaviour objection[06:01] Why the ROI case is future-looking[06:47] Confidence in the audit and fixing schema at scale[08:15] The backlog failure mode[09:19] Reframing the fix as revenue visibility[10:08] The brand manager's first move[10:56] The unconsidered set[11:26] Building in publicKeywords: AI search, AI discoverability, life science marketing, citation compression, schema markup, URL structure, generative engine optimisation, buyer consideration set, answer engine optimisation, HubSpot, B2B buying behaviour, StrivennWatch the full conversation, subscribe to A Splice of Life Science Marketing, and read the full blog at https://strivenn.com/thinking/i-ran-an-ai-seo-audit-on-my-own-site

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.