TLDR: Sixty per cent of searches now end without clicks—users get answers directly from AI systems. Life sciences companies are responding by expanding beyond traditional SEO into Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO). Real implementations include: targeting 20% citation presence for top questions, optimising Wikipedia as an AI knowledge source, and shifting from traffic metrics to share-of-voice measurements.
The Zero-Click Dilemma
A mid-sized European pharmaceutical manufacturer presented troubling data to senior leadership: despite improving search rankings, click-through rates had collapsed. Website traffic was declining even as SEO metrics suggested success.
The culprit was the fundamental evolution of information access. When a healthcare professional asks an AI assistant about treatment protocols, they receive a synthesised answer from multiple sources. The traditional pathway—search result, click, website visit—has been short-circuited.
One global speciality pharmaceutical company discovered they were measuring just 5-12% of their true digital influence. The remaining 88-95% occurred in AI-generated summaries, voice assistant responses, and algorithmic answer engines that traditional analytics couldn’t track.
From SEO to AEO and GEO
The industry is responding by expanding into Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO).
AEO structures content so AI systems can extract and present direct answers. Instead of optimising for a single claim, organisations must address hundreds of contextualised queries that healthcare professionals and patients actually ask.
GEO ensures content becomes part of the reference material that large language models consult when formulating responses, building domain authority through citations and comprehensive, expert-validated content.
A large pharmaceutical corporation assessed its digital maturity and found significant gaps. Despite market leadership and decades of clinical evidence, they had “low maturity in SEO” and lacked any approach to optimising content for AI visibility—a blind spot that could cost them thought leadership.
Real-World Implementation
Several organisations have begun pilot programmes:
One company identified the top 500 questions patients and prescribers ask about each therapeutic area, established baseline visibility measurements across AI platforms, and began systematically creating optimised content. The focus shifted from campaign-driven marketing to sustainable content production.
Another organisation’s scorecard established specific targets: achieving 20% presence as a citation source for the top 50 questions in their therapeutic area—measuring influence rather than traffic, authority rather than clicks.
A mid-sized manufacturer incorporated Wikipedia content optimisation and AEO into their scientific affairs work streams. Recognising Wikipedia as a primary knowledge source for AI systems, they invested in ensuring accurate, comprehensive, well-cited information in a carefully regulated, non-promotional space.
The Technical and Cultural Shift
Technically, organisations must adopt structured data markup and create content in formats that AI systems can parse effectively. Rather than marketing claims, organisations need comprehensive FAQ sections, clear terminology definitions, and expert-authored articles that address the full spectrum of questions stakeholders ask.
The cultural shift is harder. Marketing teams must adopt a content-first mindset with multi-year horizons. The question changes from “How do we promote this product?” to “How do we become the definitive source of information in this therapeutic area?”
One digital healthcare leader captured it: “This isn’t just SEO, it’s a dramatic change in the way we are doing marketing.”
Measuring What Matters
As visibility migrates to AI-mediated experiences, measurement frameworks must evolve. New approaches track “share of voice” across prioritised questions and monitor citation frequency across generative platforms. Google Search Console is emerging as more valuable than Google Analytics, as impression data reveals visibility even when clicks don’t occur.
The impact can be dramatic. One digital consultancy found that AEO became their primary traffic source within 18 months.
A cannabis dispensary in New Jersey published a detailed product review optimised for answer engines. The article became an “answer bomb”—appearing in response to dozens of related queries. The dispensary used it as an internal benchmark: comprehensive product details, clear dosing guidance, comparison frameworks, and FAQ-style sections that AI systems could easily parse and cite. Traffic from AI-generated answers exceeded traditional organic search within six months.
Looking Forward
Organisations that adapt now will maintain visibility and influence. Those that continue optimising primarily for traditional search risk becoming invisible to the audiences they aim to serve.
For life sciences companies, this is about ensuring accurate, evidence-based information reaches healthcare professionals and patients at the moment they’re seeking answers. Optimisation for AI platforms becomes a public health imperative as much as a business strategy.
Companies succeeding in this transition invest in long-term content strategies, understand that authority matters more than promotion, and recognise that future visibility will be won by becoming genuinely useful sources of trusted information.
