The science in deep tech research is often excellent. Years of rigorous work, peer-reviewed, validated by people who know the field. The problem of breaking out of the lab and getting to market is something else entirely — somewhere in the moment the team tries to explain why it matters to someone who doesn’t already share their frame of reference.
That moment is where most research-to-market journeys quietly fall apart — also known as the valley of death.
There’s a concept in the academic literature on knowledge transfer called boundary spanning. Researchers studying how academics engage with industry found something that shouldn’t be surprising but often is: scientists and external stakeholders don’t just speak different languages. They operate under fundamentally different evaluative systems. What counts as rigour, what counts as relevance, what counts as value — these things are communicated and defined differently.
A researcher trained to defend novelty will instinctively communicate through technical depth, exactly what years of doctoral training reinforces. The problem however, is that external stakeholders, such as industry partners, investors, programme managers, public funders aren’t evaluating novelty. They’re evaluating usefulness. Applicability. Science-Market Fit. Relevance. Commercial-ability.
The gap between those two evaluative logics is where understanding breaks.
Not funding. Not market size. Not entrepreneurial confidence. Translation.
I’ve been working inside deep-tech accelerator contexts for long enough to watch this happen in real time. Teams arrive with genuine breakthroughs and can’t explain who needs it, why now, or what changes if their solution works. Because they’ve never had a structured space to stabilise that thinking before being asked to pitch it.
Most of the tools designed to help researchers commercialise their work, business model canvases, lean validation frameworks, pitch templates, assume a level of clarity that early-stage deep-tech teams frequently don’t yet have. The problem I see though, is that they’re downstream tools applied to upstream problems.
And to be clear: these tools are powerful — once the value logic is stabilised. And that means the articulation work has to happen before any of them becomes useful.
Introducing 28Digital SPIN: Explore
That’s the gap I’ve been building tools to address for the better part of a decade — first through practice, then through formal research. On June 11, I’m facilitating Session 1.2 of the 28Digital SPIN: Explore programme — “Find Your Value” — working with PhD researchers at exactly this moment in their journey.
The session runs 90 minutes and covers three interconnected phases that I use across my work with deep-tech teams: 3D Method™ 3 interacting phases, with each one addressing a different layer of the translation problem.
Discover is about understanding the external system by developing the perspective-taking capability that lets researchers see their work through someone else’s evaluative logic, not just their own.
Define is where the hard work happens: building a coherent explanation of why the idea matters, to whom, and under what conditions. This is where the Impact Innovation Canvas comes in, not as a business model tool, but as a translational scaffold. A structured way to stabilise value logic, surface ethical tensions, universalise a shared language (internal & external) and establish stakeholder coherence before any downstream commercial modelling begins.
Deliver is about whether that meaning can be translated into a language that can be understood outside of the lab. Can the team communicate across evaluative boundaries? Are they decision-ready for the conversations that come next? This is where tools like the Lean Canvas become maximally useful, further downstream but only once the underlying articulation is solid enough to model.
The value capture arc is intentional. Articulate first, then model. The sequence matters.
The SPIN: Explore programme, supported through EIT, is built around exactly the kind of structured support that early-stage researchers need before they’re ready for commercial validation. It’s a context I take seriously, and one where the work I’ve developed through research and practice fits naturally.
What I’m bringing to June 11 isn’t a startup workshop dressed up for academics. It’s a diagnosis of why the translational gap exists and a set of tools built specifically for the moment before commercial frameworks become relevant.
If the science is strong, the problem is almost never the science. It’s whether the team can yet explain why the world needs it.
Steve Mullen is the founder of Sequoralab Consulting and creator of the Impact Innovation Canvas. He works with deep-tech teams and research commercialisation programmes across Europe, including as a facilitator on the 28Digital SPIN: Explore programme.
He helps deep-tech research teams and programmes cross the articulation gap by stabilising value logic and stakeholder coherence before business-model tools make sense.