The Future of Science Marketing: Winning Visibility in the Age of AI

Deborah Cockerill

Founder and Managing Partner

For years, science marketing has been built around visibility in search. The approach was straightforward: optimise content for search engines, drive clicks and guide prospects through a digital journey that ends in an enquiry.

That strategy needs to change

Increasingly, scientists, procurement teams, and technical decision-makers are using AI tools to research products, compare technologies and evaluate suppliers. Rather than browsing through pages of search results, buyers are asking conversational questions and receiving synthesised recommendations, pulling together web content, user reviews, and expert advice into one tailored answer

Science and technology companies can no longer rely on strong Google rankings alone. They must also become trusted sources that AI systems can confidently recommend.

From SEO to LLMO: Optimising for AI Discovery

Search Engine Optimisation (SEO) still matters, but a new discipline is emerging: Large Language Model Optimisation (LLMO).

Large language models don’t simply index webpages. They synthesise information from multiple sources and present concise recommendations. To be included in those recommendations, your information needs to be:

Easily discoverable by AI systems
Structured and clearly organised
Supported by credible evidence
Consistent across every digital touchpoint

Scientific websites should increasingly incorporate structured data, clear product specifications, question-and-answer formats, and accessible technical resources that AI systems can interpret accurately.

The goal is no longer simply to appear in search results. The goal is to be included in the AI-generated shortlist.

Trust and Authority Matter More Than Ever

Science has always depended on evidence and credibility, and AI systems operate in much the same way.

When evaluating information, generative AI tools tend to prioritise signals of authority and verification. This means organisations that invest in evidence-based content have a significant advantage.

Key trust signals include:

Peer-reviewed publications
Clinical studies and validation data
Conference presentations and scientific posters
Citations in academic literature
References from independent experts and respected industry publications

Companies should also pay close attention to the way in which their technologies are described across the wider digital ecosystem. Third-party references increasingly influence how AI systems understand and position scientific products and services.

In the AI era, reputation management becomes knowledge management.

Marketing Must Become More Conversational

Technical buyers rarely search using generic terms.

A laboratory manager may not search for a “mass spectrometer.” Instead, they may ask:

“Which mass spectrometer offers high sensitivity for metabolomics workflows with limited sample volumes?”

AI interfaces encourage highly specific, conversational questions that reveal genuine purchasing intent.

This shift has important implications for science marketing.

Rather than optimising exclusively around broad keywords, organisations should create content that directly addresses real-world technical questions:

How does this technology compare with alternatives?
What applications is it best suited for?
What limitations should buyers understand?
Which environments and workflows does it support?

Landing pages, FAQs and educational resources should increasingly mirror the language and questions technical audiences naturally ask.

Data Consistency Is Now a Competitive Advantage

Many scientific organisations have accumulated years of technical content across websites, PDFs, product brochures and legacy pages.

Unfortunately, inconsistent information can create problems.

If specifications differ between sources, AI systems may struggle to determine which information is correct. In some cases, conflicting information can reduce confidence in the reliability of a product altogether.

This makes content governance increasingly important.

Scientific companies should ensure that:

Product specifications are standardised
Compatibility information is consistent
Dimensions and performance metrics match across all assets
Pricing and configuration information is clearly structured
Older documentation is reviewed and updated regularly

The cleaner and more consistent your information ecosystem becomes, the easier it is for both humans and AI systems to trust your content.

The New Approach to Science Marketing

Traditional Approach	AI-Era Approach
Focus on search rankings	Focus on inclusion in AI-generated recommendations
Keyword-heavy blogs and gated content	Structured, accessible knowledge resources
Domain authority and backlinks	Evidence, citations and third-party validation
Click → Browse → Compare → Enquire	Ask → Receive Recommendation → Verify → Enquire

What This Means for Science Organisations Approach to Science Marketing

AI is not replacing scientific buying decisions. Scientists, engineers and procurement teams still require evidence, validation and due diligence.

However, AI is increasingly becoming the first stage of discovery.

The organisations that succeed will be those that treat their websites not simply as marketing channels, but as trusted knowledge repositories built with accuracy, transparency and structure at their core.

In the AI era, the question is no longer:

“How do we rank higher?”

It’s becoming:

“How do we become the source that AI systems trust enough to recommend?”