# Industry Specific Commercialisation Playbook

> Tailor the commercialisation strategy and expectations by industry: life sciences, deep tech, software, societal/humanities-based innovations.

- Skill: `pilot2service/industry-specific-commercialisation-playbook` (Agent Skill)
- Install (CLI): `npx skillmds@latest add pilot2service/industry-specific-commercialisation-playbook`
- Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/industry-specific-commercialisation-playbook/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: Pilot2Service (https://skillmd.com/u/pilot2service)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/pilot2service/industry-specific-commercialisation-playbook

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# Industry-Specific Commercialisation Playbook

## Purpose

Tailor the commercialisation strategy and expectations by industry: life
sciences, deep tech, software, societal/humanities-based innovations.

## Based on

the owner's published commercialisation guide (owner, 2025). Chapter
"Different Paths by Industry" (pp. 29-34) — case examples Vaccitech/BioNTech,
IQM Finland/ArtiQ, DeepMind, the UK's Aspect programme.

- Life sciences: a long (10+ years), expensive, regulated pathway; often
  partnership/acquisition by a larger player later
- Deep tech: prototype and pilot production stages, specialised equipment,
  strong IP protection as an advantage
- Software: the fastest cycle, low capital need, often nothing patentable
- Social sciences/humanities: IP-light, "lean spin-out" approach (the UK's
  Aspect programme)

## Method

1. Life sciences (pharma/biotech): expect a 10+ year pathway and significant
   capital; focus on early-stage evidence (preclinical/Phase 1) and
   partnership/acquisition later. University equity can justifiably be
   higher (up to c. 25%).
2. Deep tech / engineering: expect prototype and pilot production stages,
   specialised equipment and facilities. Strong patent protection is a
   competitive advantage. Seek deep industry expertise for advisors — a
   software-background mentor is not enough.
3. Software and digital innovations: the fastest development cycle, low
   initial capital, often nothing patentable (know-how/copyright/first-mover
   advantage). University equity is typically lower (≤10%). Speed and
   continuous iteration are decisive, not the original IP.
4. Social sciences/humanities/"impact" spin-outs: often IP-light, low capital
   need, fast revenue generation through consulting or a service. "Lean
   spin-out": minimal initial investment, fast customer interaction,
   iterative refinement.
5. Identify the regulatory environment early in every industry (FDA/EMA,
   financial regulation, energy certifications, education sector procurement
   rules) and secure an experienced advisor.
6. Summarise your own industry "playbook": pharma = patent–prototype–
   publish–partner (long, capital-intensive); deep tech = patent–prototype–
   prove in pilots–seek strategic investors; software = build fast–acquire
   users–pivot–scale; societal = prove impact–seek blended funding (grants +
   revenue).
7. Study prior spin-outs in your own field — which pathway did they take?
   (see `../../references/case-studies.md`).

## Gotchas

- The four archetypes (life sciences, deep tech, software, social
  science/humanities) are illustrative buckets from the guide's case
  examples, not an exhaustive taxonomy — a result that straddles two (e.g.
  an AI-driven diagnostic device) should default to the higher-friction
  pathway's regulatory and IP assumptions, not the faster one's.
- The equity benchmarks (≤10% software, up to ~25% life sciences) are
  indicative ranges, not your institution's actual terms — use them as a
  sanity check on a number the TTO proposes, then confirm the real figure
  via `tto-engagement-strategy` and `ip-disclosure-and-ownership-check`.
- Step 2 explicitly says a software-background mentor is "not enough" for
  deep tech — satisfying the advisor requirement with whoever's already on
  the team, rather than someone with the specific industry expertise,
  misses the point of the step.
- The timelines and cost figures are explicitly indicative (see "does NOT
  do") — quoting "10+ years" or "fastest cycle" to a funder as a commitment
  rather than a planning heuristic overstates what the guide's examples
  actually support.
- Step 7 (study prior spin-outs in your field) only transfers cleanly when
  the comparison case shares both sub-domain and regulatory regime — a
  similar-looking spin-out under a different regulator (e.g. FDA vs. EMA)
  can lead to copying a pathway that doesn't actually apply.

## What this skill does NOT do

- Does not perform regulatory classification for you (e.g. EU AI Act
  classification of an AI system) — use separate regulatory expertise.
- Does not give a precise timeline or cost estimate — the examples are
  indicative, not guarantees.

## Continue from here

- Next in this pack: `../funding-pathway-design/SKILL.md` — Build a funding
  strategy that combines non-dilutive and dilutive funding in the right
  order, and manage runway.
- Pack's shared guardrails: `../../CLAUDE.md`
- Overview of the full journey: `../commercialisation-journey-roadmap/SKILL.md`

## References

- `../../references/case-studies.md` — 7 spin-out examples from different
  industries and regions
- `../../references/terminology.md` — the handbook's glossary
- `../../references/sources.md` — the handbook's own source references
- `../../CLAUDE.md` — the pack's shared guardrails