Turn AI Visibility Into Action: Build Explanation Layers For Different Stakeholders

By Ross Jackson, Ross Jackson Consulting

The second part of the fix is therefore about actionability — whether AI-generated answers help the intended stakeholder to do something appropriate with that information.

One of the biggest mistakes sponsors can make is expecting one piece of content to serve every audience.

Patients need relevance, burden, reassurance, and a safe route to verification. Physicians need referral relevance and scientific context. Sites need operational feasibility and support. Sponsors and CROs need evidence of capability and fit.

These audiences overlap, but they do not have identical needs. If the sponsor’s public content does not provide those explanation layers, AI tools will try to assemble them from whatever material is available. That may produce technically acceptable answers that still fail the user.

Clarify The "What Happens Next" Pathway

AI visibility only matters if it helps someone move toward an appropriate action.

In another recent audit, I found that the AI could explain the study once it located it, but the public ecosystem did not provide an obvious patient landing page, site finder, pre-screener, response time expectation, or referrer pathway.

That is not a minor detail. It is often where recruitment momentum is lost.

For a patient, the next step might be discussing the trial with their physician, searching for a nearby site, contacting a study team, or understanding whether they may meet basic eligibility criteria. For a physician, it might be identifying suitable patients, accessing referral information, or contacting the study team. For a site, it might be understanding feasibility, sponsor expectations, recruitment support, and whether the trial fits the site’s patient population and capabilities.

Sponsors should therefore examine whether their public materials answer practical questions such as:

• Who should consider this trial? Who should not?
• Where is it available?
• How should a patient or physician inquire?
• What information should be discussed with a treating doctor?
• What support is available for sites?
• How current is the recruitment status?
• Where is the authoritative source for more information?

This is especially important where AI tools are cautious about medical advice. A good sponsor page should not encourage inappropriate self-selection or overstate the benefits of an investigational treatment. But it can help users understand the appropriate route for further discussion.

There is also an important boundary here. Better public information should help someone decide whether to make an appropriate inquiry or seek further verification. It should not encourage an AI tool, or the person using it, to decide that an individual is eligible or that an investigational treatment is suitable for them. Eligibility still needs to be assessed by the study team, and trial information should not be presented as medical advice or as evidence that an investigational product is safe or effective.

Address The Questions That Create Friction

Some of the most important fixes are not technical at all.

In another audit, placebo and randomization emerged as pivotal patient interpretation risks. The issue was whether public materials helped patients understand the use of a placebo and randomization honestly and tolerably.

For some patients, placebo may sound like being given nothing when they are already struggling. Randomization may sound like loss of control. Double-blinding may sound secretive. Eligibility criteria may make people assume they are too ill, not ill enough, too medicated, too complicated, or otherwise unsuitable before they ever reach screening. These details, or lack of them, can present decision and inquiry issues.

Sponsors should consider creating plain English explainers for placebo, randomization, investigational status, what is known and not yet known, eligibility concerns, what happens after inquiry, travel and visit burden, reimbursement, and how urgent health concerns should be handled separately from ordinary trial exploration. The aim is to make uncertainty understandable enough that appropriate patients, caregivers, and referrers can decide whether to verify the opportunity rather than abandon it.

Make Comparison Easier

AI tools are increasingly for comparisons. A patient may ask how one investigational option differs from another. A physician may ask what trials are available in a disease area. A site may ask whether a study appears more or less burdensome than similar trials. A sponsor or CRO may ask which organizations appear most relevant for a given indication or geography. If sponsors do not provide clear differentiators, AI systems may either produce generic comparisons or draw heavily from third-party sources.

That does not mean sponsors should write promotional comparison content. It means they should make factual distinctions — mechanism of action, target population, trial phase and design, geography, visit burden, route and frequency of administration, key inclusion and exclusion considerations, available evidence, recruitment status, and participating site information — easy to understand.

For sites, CROs, and vendors, the same principle applies in a different way. Public materials should make therapeutic experience, operational capabilities, geographic coverage, recruitment support, patient access, case examples, and differentiators easier to evaluate.

AI tools can only summarize and compare what the information environment gives them.

Prioritize Fixes By Recruitment Impact

Not every AI visibility gap deserves the same level of attention. Sponsors should prioritize fixes based on practical impact not content volume.

High-priority fixes are those likely to influence patient understanding, physician referral, site engagement, recruitment momentum, or stakeholder trust. These might include unclear trial status, missing site locations, confusing eligibility language, absent patient summaries, weak physician referral information, or public pages that fail to explain the basic participant pathway.

Medium-priority fixes may involve improving content architecture, internal linking, FAQ sections, summaries of posters and publications, and pages that explain differentiators more clearly.

Longer-term fixes may involve broader authority building, such as publications, advocacy partnerships, conference visibility, or earned media.

Technical improvements also have a place. Clear page titles, headings, structured content, transcripts, internal links, updated dates, and appropriate structured data can all help make content easier to retrieve and interpret. But technical fixes should support the underlying communication strategy not replace it.

Where possible, sponsors should also measure what happens after the click or inquiry by tracking pre-screener completion, referral completion, contact response time, qualified inquiries, site follow-up, and reasons people abandon the process. The point is not to attribute every recruitment outcome to AI but to identify whether the information pathway is creating avoidable friction. The central question should be: Which improvement is most likely to help the right stakeholder make a better-informed decision?

Retest After Making Changes

AI visibility is not a one-off exercise. After improving source material, sponsors should retest the same stakeholder scenarios across the same AI tools.

Did the trial surface more consistently? Did the answer rely on better sources? Was the explanation clearer? Were patients, physicians, or sites given a more useful route forward? Were inaccurate or incomplete interpretations reduced? Did competitor comparisons become more balanced?

The point is not to expect perfect control over AI-generated answers. Sponsors will not control every source or every model output, but they can improve the information environment from which those answers are generated.

Retesting is especially important before recruitment launch, after major protocol amendments, after new data readouts, before conference activity, when entering new geographies, and when recruitment underperforms.

Conclusion

AI visibility is not about manipulating AI systems but about making clinical trial information clearer, more accessible, more current, and more useful for the people who may increasingly encounter that information through AI-generated answers.

The practical fixes are often familiar — better source material, clearer stakeholder-specific explanations, stronger patient and physician pathways, accessible summaries of complex assets, improved content structure, and more credible authority signals.

In practice, the best AI visibility fixes often look like good clinical trial communication — clear answers, current evidence, audience-specific pathways, and content structured so both humans and AI systems can use it.

The goal is to be visible and to be understood well enough that the right stakeholder can take the right next step.

About The Author:

Ross Jackson is a patient recruitment specialist and author of the books The Patient Recruitment Conundrum and Patient Recruitment for Clinical Trials using Facebook Ads.

Having started out with digital marketing in 1998, Ross quickly developed a specialty in the healthcare niche, evolving into a focus on clinical trials and the problems of patient recruitment and retention.

Over the years Ross branched out from the purely digital and now operates in an advisory capacity helping sponsors, CROs, sites, solutions providers, and others in the industry to improve their patient recruitment and retention capabilities — having advised and consulted on over 100 successful projects.