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Launch-Ready Doesn't Mean AI-Ready
By Jasmine Gruia-Gray
Why Your Product Is Invisible to the Researchers Already Searching for It
The Romans knew Fama before they knew her consequences. The goddess of rumor and fame, she had eyes covering her body, mouths that never stopped speaking, and ears that heard everything whispered in every corner of the empire. She flew between earth and heaven, growing larger as each story spread. Fama spoke truth and lies with equal enthusiasm, and by the time you arrived at the Forum to defend yourself, she had already told everyone who you were. The Romans built no temples to Fama. You cannot negotiate with rumor. You can only feed her better information and hope she repeats it accurately.
Product Managers (PMs) launching research tools face their own Fama now, except she operates at machine speed. ChatGPT, Perplexity, and Gemini are deciding if your qPCR system, flow cytometer, or NGS platform exists before researchers ever visit your website. You cleared technical validation. You passed regulatory review. Your sales team is trained. But Fama has already whispered to 10,000 potential customers, and you have no idea what she said about you.
The Gate That Opened When You Weren't Looking
Your commercialization checklist looks complete. Sales enablement materials finished. Product documentation locked. Launch webinar scheduled. Pricing approved. Everything a PM traditionally controls before market entry.
Except the adoption loop broke at step one. Researchers are asking AI which multiplex immunoassay platform to evaluate. They're prompting ChatGPT to compare automated liquid handlers. They're using Perplexity to research single-cell analysis systems. And the AI is making recommendations based on what Fama whispered, not what your launch plan specified. Your product passed every gate in your New Product Development (NPD) process, but the new gatekeeper doesn't attend your stage-gate reviews. By the time a prospect reaches your website, Fama already decided if you're worth their time.
The Scale of the Shift
Life sciences professionals have already embraced AI-assisted decision-making:
- 80% use AI for drug discovery (Scilife, 2024)
- 76% of biotech organizations use AI for literature review (Benchling, 2025)
- Specialized research AI tools like Elicit serve 2 million researchers globally (Elicit, 2025).
ChatGPT now serves 800 million weekly users, roughly 10% of the global population, processing 2.5 billion prompts daily (Superlines, Index.dev, 2026). While specific data on researchers using ChatGPT to compare flow cytometers or evaluate qPCR systems doesn't yet exist, the trend is clear: AI referral traffic grew 388% year-over-year for Gemini and 52% for ChatGPT in late 2025 (Similarweb via Vertu, 2026).
For life sciences PMs, this means your invisible funnel, researchers discovering and evaluating products through AI before visiting your website is already processing significant volume your analytics don't capture.
What Changed at Launch
Your NPD process hasn't caught up to how researchers actually discover products:
| Traditional NPD Commercialization | AI Search Reality |
|
Train sales team, create battlecards and demo scripts. Prospects engage after awareness campaign. |
Researchers ask ChatGPT for recommendations BEFORE talking to sales. AI decides if you're in the consideration set.
Still important to train Sales, create battlecards and demo scripts! |
|
Technical specs are for post-sale support. Marketing handles pre-sale positioning. |
The sooner technical specs are available on the website, the better. AI reads technical docs AND application notes showing you understand the customer's job-to-be-done. Specs validate capability after positioning earns attention. |
|
Control your narrative through website content, sales presentations, analyst briefings. |
AI trains on review sites, Reddit, protocol forums, and LinkedIn discussions you don't control. Narrative emerges from collective mentions.
Traditional content is still valuable AND add the content that AI gobbles up. |
|
Track website traffic through standard analytics. |
AI traffic requires custom GA4 setup and much remains invisible (Backbone Media, 2025). Your funnel: AI recommendation → brand awareness → website visit appearing as 'direct.' |
|
Marketing owns pre-sales content. Product owns post-sales documentation. |
PMs partner with marketing on AI-facing content strategy. Application notes and validation protocols are now pre-sales content. |
The PM's AI Content Strategy
You can't silence Fama. But you can feed her better information. Here's how to inject AI-readable content into your NPD timeline:
Development Phase:
Build presence in industry forums as a technical expert. Contribute to protocol discussions on ResearchGate and specialized forums. Not product promotions. Answer genuine technical questions in your domain. This establishes domain authority before you have a product to sell. As your product advances and you have data safe to share publicly, present at conferences with abstracts on crawlable pages. If you're showing performance data for a newly launched single-cell isolation platform at a conference, ensure that abstract includes your company name and product category (Yes, there are some conferences that see this as overtly commercial, and don’t allow it.) clearly stated.
Launch Preparation (3-6 months before):
Create buyer's guides that help AI understand product categories and decision criteria. A guide titled "How to Select Automated Liquid Handlers for High-Throughput Screening" positions you as a category authority. Include decision factors: throughput requirements, plate formats, precision needs, workflow integration. When researchers prompt AI for selection guidance, this content trains the recommendation.
Build comparison content showing where your product fits in the competitive landscape. Focus on use cases and applications rather than head-to-head specs: "For core facilities running 384-well plate assays, automated liquid handlers fall into three categories: high-throughput systems optimized for speed, precision systems optimized for low-volume transfers, and flexible systems handling multiple plate formats. Our platform targets the flexible category."
Launch Phase:
Make your product page answer the prompt structure researchers actually use (Strivenn). When someone asks ChatGPT about protein characterization for biologics quality control, your page should contain those exact phrases in context. Launch technical documentation as public knowledge base articles, not gated downloads. Your application note on optimizing qPCR cycling parameters is pre-sales content now.
Post-Launch:
Monitor what Fama says about you monthly: prompt ChatGPT with your product category and use case. If you're not mentioned, create application-specific content, get mentioned in relevant forum discussions, and ensure your product page explicitly describes solving that job-to-be-done.
Fama Flies Whether You Feed Her or Not
The Romans couldn't control Fama's whispers. But they understood that the stories she spread came from somewhere. If you wanted Fama to speak well of you, you needed to give the marketplace better material to discuss.
PMs face the same reality with AI search. Fama now reads every protocol forum, every product review site, every technical specification sheet. She forms opinions about your qPCR system before you announce it.
Your commercialization checklist needs a new section: Generative Engine Optimization (GEO). LinkedIn's Big Ideas 2026 list positions GEO as the practice set to overtake traditional SEO this year (AI Magazine, Dec 2025). Instead of optimizing for keyword rankings in search results, GEO optimizes for inclusion and citation in AI-generated answers. As Daniel Hulme, Chief AI Officer at WPP, notes: "SEO will remain a crucial tool, but its dominance is ending. A move towards GEO is coming."
Start this work in Development, not at Launch. Fama has already started talking. Make sure she has the right information to share.
Special thanks to Matt Wilkinson: Wild, Wild West article, Sarah Stahl, Sandy Carter and Andy Crestodina for inspiring this blog topic.
Q: How do I get R&D buy-in when they're worried about sharing technical details publicly? ▼
Frame this as competitive intelligence protection, not disclosure. Share application-level performance data without methodology details. Publish use case descriptions without the engineering innovations that enable them. Show R&D examples of competitor content already indexed by AI. If established players are publishing technical comparisons and you're silent, you're invisible by default. The risk isn't disclosure; it's obscurity.
Q: Marketing says they're already doing SEO. How is this different? ▼
Traditional SEO optimizes for keyword rankings in Google search results. AI content strategy optimizes for recommendations in conversational responses. This requires structured tables AI can parse, natural language matching how customers describe their jobs-to-be-done, and comparison content that positions you within a category. SEO gets you found in search results. AI content strategy gets you recommended in answers. Partner with marketing to add AI optimization to existing SEO workflows.
Q: What if we're entering a crowded category where established players dominate AI recommendations? ▼
Target the use cases incumbents ignore. Major manufacturers dominate broad category recommendations, but under-serve specific applications. Create deep application-specific content for these niches: "Flow Cytometry for Rare CAR-T Cell Detection: Selection Guide for Immunotherapy Labs." When researchers prompt AI with specific jobs-to-be-done, your focused content outcompetes generic overview pages. Start narrow, expand strategically as you build authority.
