Is microbiome adoption being designed too late?
Adoption is not what happens after innovation. It is one of the conditions from which innovation should be designed.
Christian Roghi · Independent Strategic Adviser · Bridgyz
The issue
Microbiome companies usually design the science first. They choose what to measure, which signal matters, which product to build and which evidence to generate. Adoption appears later, often when the product is nearly fixed and the commercial team is asked to find the market. By then, many of the choices that determine adoption have already been made.
Why it matters
Some adoption problems cannot be solved by better marketing. A commercial team can explain a proposition. It cannot make an unimportant problem urgent, remove a workflow burden built into the product or create ownership where nobody has agreed to act. When adoption is considered too late, companies can spend years improving the science while the future conditions for use are hardening in the wrong direction.
My judgement
I use three connected design questions:
Core proposition → Adoption readiness → Adoption ecosystem
Core proposition
Are we solving a problem important enough to justify a change in behaviour?
Adoption readiness
Are the product, evidence and operating model being designed for responsible use outside ideal conditions?
Adoption ecosystem
Do the people and organisations required for adoption have a reason and ability to move? These questions belong at the beginning of innovation, not at the end.
Three signals to notice
The intended behaviour is unclear.
The team can describe the technology but not what somebody should do differently.
The product assumes the system will adapt.
Workflow, interpretation and responsibility are left for the customer to solve.
The evidence plan validates the science, not the change.
The studies prove capability without resolving whether anyone will act.
The bottom line
Adoption does not begin at launch. It begins when the problem, behaviour, product, evidence and operating environment are still choices.
The tension
Most innovation processes have a familiar sequence. Discover the science. Protect the intellectual property. Build the product. Generate evidence. Address regulation. Prepare commercialisation. Launch. Adoption is expected to follow. But adoption is not a downstream communication task. It is the outcome of decisions made throughout that sequence.
A product may require a new workflow because nobody tested the old one early enough. The evidence may fail to support the buyer because the study was designed around a scientific question. The user may receive an answer but no responsibility for acting on it. The price may be difficult to sustain because the value was never located clearly. By launch, these are no longer hypotheses. They are features of the product and business model.
Where adoption is designed
Five early choices shape much of what becomes possible later.
The problem
The first choice is not the technology. It is the problem to which the technology will be attached. A scientifically important problem may still be too infrequent, too weakly felt or too low on an organisation’s priority list to justify change. The stronger question is:
What is happening today that somebody is sufficiently motivated to improve?
The behaviour
Every adoption pathway contains a behaviour that must change. A clinician interprets new information. A laboratory changes a process. A consumer follows advice. A board redirects resources. A partner commits capability. If the intended change cannot be named, the innovation is not yet attached to adoption. “Use our platform” is not a behaviour. It is the company’s ambition.
The product
Product design determines how much change the user must absorb. Every additional step, interpretation, handover, system integration or specialist dependency increases the demand placed on the adoption environment. Early adopters may tolerate this complexity. Routine users usually will not. The design question is not only what the product can do. It is what the user must do for the product to work.
The evidence
Evidence plans are often built to validate the technology or satisfy a regulatory requirement. Those are legitimate goals. They do not automatically address the uncertainty preventing adoption. The critical evidence may concern analytical confidence, clinical utility, workflow impact, economic value or the consequences of acting on the result. If the adoption decision is unnamed, the evidence plan can become extensive and still fail to move anyone.
The operating environment
Innovation rarely enters an empty space. It enters budgets, roles, routines, incentives, standards, procurement rules and professional boundaries. The company may control the product. It does not control the complete system required for adoption. That system must be understood while the proposition can still be adapted.
Case in point: microbiome testing
Microbiome testing shows how an adoption problem can be designed long before commercial launch. The technical starting point is understandable. The company can analyse a stool sample, characterise the microbial community and produce a detailed report. From the company’s perspective, the product is the test. From the user’s perspective, the product is the decision that follows. This distinction changes the design process. If the starting question is “What can we measure?”, the report tends to reflect the analytical capability. It may contain diversity scores, relative abundance, functional interpretation and comparisons with a reference population.
If the starting question is “What should change after this result?”, different requirements appear. The measurement must be sufficiently reliable. The interpretation must be valid for the intended user. The result must identify something important enough to act on. The recommended action must be supported by evidence. Somebody must own the action and be able to evaluate whether it helped. Current commercial testing illustrates the gap. An international consensus statement published in 2025 concluded that evidence supporting the clinical usefulness of many commercial microbiome tests remained scarce. Studies have also identified substantial variability across laboratory methods and direct-to-consumer providers.
Those findings matter before launch, not only after it. They influence which claims should be made, which users should be targeted, which decisions can be supported and whether a standalone consumer report is the right product at all. Marketing cannot repair those choices later. It can explain what has been built. It cannot turn microbial description into a reliable adoption pathway.
Strategic interpretation
The three-part model helps move adoption upstream.
Core proposition
The proposition must identify more than a market and a benefit. It must connect a defined user, an important problem and a change worth making. For microbiome testing, “understand your gut” is broad and attractive. It does not establish which decision improves or why the user should sustain a different behaviour. A narrow proposition can feel smaller. It is often more adoptable because the required evidence, workflow and outcome become clearer.
Adoption readiness
Adoption readiness is not a launch checklist. It asks whether the product and company have been designed for use. Can the result be trusted? Can it be understood? Is the action clear? Is delivery consistent? Can the company support the user without permanently rescuing the process? These conditions cannot all be added at the end. They shape the product architecture, evidence programme, claims, capabilities and economics.
Adoption ecosystem
Some conditions sit outside the company. A test may depend on a laboratory, a clinician, a nutrition professional, a digital platform, a regulator and a consumer. Each participant may have a different incentive, tolerance for uncertainty and definition of value. The design task is not to control the whole ecosystem. It is to understand which participants must move, what they need and which missing condition could stop the pathway.
The future
More mature microbiome innovation will begin with adoption hypotheses alongside scientific hypotheses. Teams will ask not only whether a signal is real, but who must act, what must change, what evidence unlocks that change and what the surrounding system can realistically absorb. This does not make science subordinate to the market. It prevents scientific capability from being shaped into products that require the world to reorganise around them. The strongest future products may ask less from the user because more adoption thinking happened earlier. They will arrive with a clearer role, a narrower decision, fewer unresolved handovers and evidence designed for movement.
What changes now
Name the behaviour before the product.
Define what a user or organisation must do differently if the innovation succeeds.
Design backwards from ordinary use.
Assume limited time, imperfect attention and no permanent expert champion.
Add an adoption question to every evidence decision.
Ask which uncertainty the study must reduce and whose decision it should change.
Expose system dependencies early.
Identify the roles, workflows, incentives and capabilities outside the company on which use depends.
Keep choices reversible for longer.
Test adoption assumptions before product architecture, claims and economics become difficult to change.
Three questions for the executive team
Which early decision has the greatest influence on whether our innovation will be adopted?
What change are we expecting users or organisations to absorb without our help?
Which adoption assumption must we test before the product becomes harder to change?
The bottom line
Microbiome adoption is designed too late when it is treated as the journey from a completed product to a customer. By then, the important choices may already be embedded in the proposition, product, evidence and operating model. Adoption starts before launch because that is when the future conditions for use can still be changed.
Selected references
Proctor, E.K. et al. “Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda.” Administration and Policy in Mental Health and Mental Health Services Research, 38, 65–76, 2011.
Damschroder, L.J. et al. “The updated Consolidated Framework for Implementation Research based on user feedback.” Implementation Science, 17, 75, 2022.
Proctor, E.K. et al. “Ten years of implementation outcomes research: a scoping review.” Implementation Science, 18, 31, 2023.
Porcari, S. et al. “International consensus statement on microbiome testing in clinical practice.” The Lancet Gastroenterology & Hepatology, 10(2), 154–167, 2025.
Forry, S.P. et al. “Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols.” Scientific Reports, 14, 9785, 2024.
Servetas, S.L. et al. “Evaluating the analytical performance of direct-to-consumer gut microbiome testing services.” Communications Biology, 9, 269, 2026.