New Research Briefing: Aligning Science, Engineering, and Business in Deeptech Startups

ESCP’s Deeptech Entrepreneurship Research Briefings continue with a second issue focusing on a core challenge at the heart of Deeptech ventures: how founding teams align scientific ambition, engineering constraints, and commercial imperatives within a single organisation.

Drawing on qualitative interviews with founders and senior team members across four startups in artificial intelligence, biotechnology, MedTech, and industrial sensing, this Research Briefing examines how teams coordinate under conditions of high uncertainty, long development cycles, and capital intensity.

The findings show that Deeptech startups operate at the intersection of three distinct domains, science, engineering, and business, each with its own language, timelines, and success criteria. Misalignment between these domains is not an exception but a structural challenge, often leading to delays, internal tension, and strategic drift.

Early team structures are typically built on trust, emerging from prior academic or professional relationships. While this facilitates initial collaboration and fundraising, roles tend to evolve informally, often resulting in blurred responsibilities and increasing coordination costs as the company grows.

A recurring pattern across cases is the centralisation of business responsibilities in a single individual, combined with the emergence of hybrid roles. Scientists frequently expand into commercial functions, reducing friction between domains but also creating significant cognitive and emotional load. These boundary-spanning roles become critical to the venture’s progress.

One of the strongest insights from the study is the importance of shared language as a coordination mechanism. Rather than relying solely on formal structures, teams develop ongoing alignment practices, such as regular exchanges, cross-functional meetings, and common frameworks such as Technology Readiness Levels (TRLs). They help bridge disciplinary gaps and synchronise expectations internally and with investors.

Across all interviews, trust emerges as a foundation. Teams rely on expert judgement without full mutual understanding, making transparency, communication, and clear priorities essential to maintaining coordination under pressure.

Finally, recruitment practices reveal that adaptability and cultural fit consistently outweigh pure technical expertise. Deeptech teams prioritise individuals who can operate across boundaries, tolerate uncertainty, and contribute to collective progress over extended timelines.

This second issue highlights that Deeptech success is not only a matter of technological excellence, but of organisational design. The most effective teams function as translation systems that actively align science, engineering, and business through hybrid leadership, shared frameworks, and trust-based coordination.