Many life sciences companies are active and visible: they post regularly, attend events, refresh their websites and share updates. Yet a recurring issue remains:
Stakeholders can see them, but they can’t easily find or verify them when it matters.
This is the difference between visibility and discoverability — a gap that is growing as both information overload and AI-assisted search reshape how people look for scientific and commercial signals.
Why visibility is no longer enough
Recent industry insights show that life sciences organisations rely heavily on visible channels such as websites, LinkedIn and conferences, while underinvesting in the content structures that make information findable and credible at the moment of search.
At the same time, AI tools such as Gemini, Copilot and Perplexity now synthesise answers rather than listing links. These systems surface information that is:
- Structured
- Referenced
- Authored by identifiable experts
- Validated by independent publications
If your content does not meet these criteria, it is unlikely to appear in AI-generated summaries — even if your overall visibility is high.
A hypothetical example
Imagine a diagnostics start-up that is very active on social media and present at most industry events. However, when an investor searches:
“emerging biomarkers for early detection 2026”
The company does not appear in Google’s AI Overviews or in industry press summaries. It remains invisible at the moment of genuine interest, despite strong outward visibility.
The content is simply not structured for AI or scientific audiences: no abstract, no author, no references, no FAQ, no independent coverage. The issue is therefore not communication activity — but retrievability.
What improves discoverability
Based on current AI-search behaviour and evidence frameworks used in science communication, discoverability improves when content is:
- Structured (short summaries, FAQ blocks, clear claims)
- Credible (authorship, credentials, review dates)
- Referenced (PMIDs, data, comparator details)
- Validated (coverage in trusted third-party outlets, which AI engines cite disproportionately)
These elements align with the EEAT model — experience, expertise, authoritativeness and trustworthiness — now central to how AI and search systems evaluate quality.
Why this matters for biotech and medtech
For CEOs, business developers and scientists, discoverability directly supports:
- Investor research and due diligence
- Partnership evaluations
- Media interest
- Clinical and scientific credibility
- How your innovation appears in AI-assisted search
Discoverability is not about producing more content. It is about producing usable, verifiable and search-friendly content — for humans and for algorithms.
How Backstage Communication helps
At Backstage, we work with life sciences teams to turn complex science into communication that is:
- Clear and structured
- Evidence-based
- Discoverable by search and AI systems
- Validated through targeted, relevant PR
Our approach is simple: present less, explain better, and make sure the right people can find and trust your story.
