Five Customer Discovery Models in Deep Tech
A case-based guide to discovering deep tech markets where adoption is more complex than demand.
In deep tech, customer discovery is not only about finding people who like what you built. It is about understanding what has to happen f or the technology to be adopted in the real world.
That may sound obvious. But in practice, many founders still approach customer discovery as if the main question were, “Do customers want this?” In deep tech, that question is usually too small. A company may want it. An engineer may admire it. A pilot partner may agree to test it. And yet nothing moves. Not because the technology is weak, but because the path to adoption is still unclear.
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This guide is written for deep tech founders who are trying to understand markets that do not behave like software markets.
In such sectors, the challenge is often less about discovering whether a user finds a product interesting and more about understanding whether an organization can qualify, adopt, scale, and continue to buy a new technology over time.
The guide uses the phrase customer discovery broadly. In this context, customer discovery includes application discovery, buyer and stakeholder mapping, validation of adoption criteria, pilot learning, and ecosystem sensing.
That broader framing tends to be more faithful to the realities of chemistry, advanced materials, industrial equipment, and other sectors in which technical performance alone rarely determines commercial success.
What this guide is designed to help with:
Clarifying which kind of discovery problem a deep tech company is actually facing.
Showing how customer discovery and commercialization patterns appear across deep-tech markets, including industrial incumbents, hard-tech pioneers, and category-defining scale-ups.
Translating those patterns into founder-friendly field practices that may be used without the budget or infrastructure of a large organization.
Offering an alternative to software-centric commercialization playbooks.
Why Deep Tech customer discovery works differently
A scene familiar to many deep tech founders begins with a real technical breakthrough and an awkward commercial conversation.
A team leaves the lab with a membrane that performs better, a coating that lasts longer, a catalyst that reduces energy use, an excipient that improves delivery, or a process platform that changes yield and purity.
The early reactions are often encouraging. Prospective customers may say the work is impressive. Investors may say the science looks promising. Yet the company still struggles to answer what sounds like a deceptively simple question: where, exactly, should this technology enter the market?
That question tends to remain unresolved because most mainstream customer-discovery advice was built for businesses in which the product is easy to demonstrate, the user can test it quickly, and adoption is relatively low risk. In software, a founder may often learn a great deal from a short conversation, a trial, or a lightweight pilot. In deep tech, the situation is usually more layered. A buyer may not be the user. An engineer may shape the specification without controlling the budget. A quality team may stop the project long after a commercial champion has expressed enthusiasm. A pilot may create useful technical evidence while revealing almost nothing about who will authorize scale-up. In other words, the learning task is broader than simple demand sensing.
This broader view is reflected in the U.S. Department of Energy’s Adoption Readiness Levels framework, which is explicitly designed to complement Technology Readiness Levels.
The ARL framework describes commercialization as an exercise in identifying and addressing adoption risks early and often. It evaluates not only technical promise but also issues such as market acceptance, resources, and license to operate. That distinction is particularly important in hard-tech sectors, because a technology may be technically mature and still be commercially fragile if the adoption pathway is unclear.
The same idea appears in Vanessa Chan’s work on materials commercialization. In a 2025 lecture at MIT, she emphasized that new materials frequently begin at the start of the value chain and therefore depend on adoption by multiple downstream actors before they can become mainstream.
That may sound obvious to people inside hardware or materials science, but it is surprisingly easy to forget when founders are surrounded by software analogies.
A product does not move from invention to adoption simply because a market ‘likes’ it. It moves when enough actors in the chain can understand it, test it, trust it, and fit it into existing or newly designed systems.
Established industrial firms tend to behave accordingly. They often build application centers, technical centers, innovation centers, pilot facilities, and co-creation environments because they know that customer discovery in their world is not a single interview problem.
They are not just selling. They are learning how the market learns. This guide is built on that observation.
It starts from a simple idea: some of the most useful customer discovery models for deep tech can be distilled from commercialization patterns visible across deep-tech markets, including industrial incumbents, hard-tech pioneers, and category-defining scale-ups.
Not copied literally, but translated. These companies have spent years learning how new technologies move through qualification, stakeholder alignment, pilot work, and ecosystem formation. Their contexts are different from those of startups, but the commercial logic is often highly reusable.
The five models are: