Deep-tech ventures, such as those developing digital twins, artificial intelligence, or blockchain
solutions, often struggle to connect their innovative technologies with clear, actionable market
opportunities. Unlike single-product start-ups that address well-defined customer pain points, deeptech ventures are often driven by technological innovation in search of practical applications. This “solution looking for a problem“ scenario hinders commercialisation, as many ventures fail to establish a direct link between their technologies and specific, solvable problems.

This thesis tackles this challenge by proposing a systematic Problem-Solution Fit (PSF) process
tailored to deep-tech ventures. The PSF process helps to identify, prioritise, and validate problem
areas, bridging the gap between technological potential and market relevance. The study was
conducted in collaboration with Docklab, a venture lab exploring commercialisation for NexTwin, a
digital twin technology. The research combined a literature review, case study analysis, semistructured interviews and an emerging method called a “Reverse Hackathon”.
The research highlights three key obstacles that hinder Problem-Solution Fit in deep-tech ventures.
First, stakeholder engagement is difficult in multistakeholder environments due to conflicting
priorities, trust issues, and fragmented data ecosystems. Second, integration challenges arise
from legacy systems, high adoption costs, and the perceived complexity of advanced technologies.
Third, ventures face strategic tensions between market-pull and tech-push approaches, creating
uncertainty in balancing their innovative potential with practical industry needs. These insights informed the development of the PSF process, which includes three phases: exploration, validation, and decision-making. Tools like stakeholder mapping and assumption mapping help to identify actionable problem areas, while the Reverse Hackathon serves as a central activity to
uncover hidden industry pain points and validate priorities. By prioritising actionability, stakeholder
alignment, scalability, and market relevance, the process provides a structured path for deep-tech
ventures to transition from abstract innovations to market-ready solutions.

The Docklab case study revealed critical lessons. While the Reverse Hackathon generated three actionable problem areas and direct client leads, internal challenges, such as reactive outreach
and broad value propositions, prevented these opportunities from being pursued. This highlighted
the importance of focus, proactive stakeholder engagement, and sector-specific value propositions
for deep-tech ventures. The process demonstrated its effectiveness in identifying and prioritising
problem areas while underscoring the need for refined commercialisation strategies.

This research contributes to academic literature and practice by extending frameworks like Romme et al.’s (2023) Reverse Hackathon to focus on earlystage PSF in tech-push contexts. The PSF process
provides a replicable methodology for navigating ambiguous markets, enabling collaboration, and
aligning technological capabilities with market needs. Its interdisciplinary design, integrating
sociology, cognitive science, and design thinking, enhances its adaptability across industries.
For practitioners, the PSF process offers actionable tools to reduce false starts, allocate resources
efficiently, and align innovations with stakeholder priorities. Although developed in the Docklab
context, the process is scalable and adaptable, offering a valuable framework for ventures addressing the “solution looking for a problem” dilemma. By achieving PSF, ventures can navigate uncertainty, build stakeholder trust, and establish a foundation for sustainable market success.