AI-Designed Materials Commercialisation Adviser

The mandate

One question has resisted four board cycles: should the venture license its materials-discovery engine, enter chemistry-specific co-development agreements, or finance selected compounds through pilot manufacture and qualification? Each route values the same model predictions differently, and current papers blur computational novelty, laboratory reproducibility, manufacturability, patent position and a customer’s willingness to fund validation.

The adviser will reserve three working days each month: one evidence review with scientists and commercial leaders, one chair or partner-strategy session, and one day for paper preparation and individual challenge. Five Science and Investment Committee meetings are included; a time-sensitive licensing or portfolio question receives an initial view within two business days, while transaction execution requires separate scope.

The appointment runs for twelve months and concludes with approval of the following year’s discovery-to-commercialisation portfolio. The committee chair may propose one six-month renewal if a specific validation partnership remains before a board decision, but renewal needs a recorded vote and cannot compensate for unfinished management work.

This seat carries influence rather than authority. It has no line management, fiduciary vote or executive responsibility for model research, compound selection, laboratory release, patent filing, partner negotiation or capital deployment; accountable officers must own every decision and record where their conclusion differs from the adviser’s view.

University and non-competing deep-technology commitments are permissible after full disclosure. A current board seat, retainer, investment, sponsored-research arrangement or contingent fee involving a competing discovery platform, shortlisted cell maker, laboratory contractor or investor considering the venture creates a conflict that may require recusal or termination.

Why the board wants this voice

The directors can assess scientific quality and venture returns separately, but none has commercialised an AI-originated material through partner validation and industrial qualification. Management therefore counts promising predictions as pipeline value before the physical evidence and contracting pathway are clear. The chair wants a practitioner who can identify where digital learning compounds advantage and where it merely accelerates expensive experimentation.

What you will own

• Press the committee to separate model-ranking performance, wet-laboratory replication, process-window robustness, pilot yield and customer qualification when assigning portfolio confidence.
• Test whether each proposed licensing or co-development structure preserves learning rights, improvement ownership, field data access and freedom to serve adjacent markets.
• Challenge programme economics with the true cost and duration of synthesis, characterisation, scale-up, precursor sourcing, cell integration and failed qualification loops.
• Shape evidence gates that determine when a candidate remains computational research, enters funded validation, secures a partner or loses further internal capital.
• Probe the patent and know-how strategy for disclosure leakage, reproducibility, inventorship, design-around exposure and dependence on licensed training information.
• Compare platform licensing, chemistry partnerships and owned-product pathways under downside cases for model decay, laboratory variance and customer concentration.
• Frame the board’s closing portfolio memorandum with supported conviction, dissent, conflicts, expiring assumptions and the next irreversible decision for each funded candidate.

Candidate qualifications

• Took an AI-enabled materials, chemistry or molecular-discovery proposition from computational selection into reproducible laboratory evidence and a paid industrial relationship.
• Structured licensing or co-development economics where algorithms, experimental learning, patents and customer validation created different ownership claims.
• Stopped or narrowed a technically promising materials programme after scale-up, qualification or unit-economics evidence invalidated its initial investment thesis.
• Challenged model-performance claims using prospective laboratory results and can explain how selection bias distorted an apparently successful discovery portfolio.
• Advised a deep-technology board through capital allocation across platform research, pilot assets, application engineering and partner-funded programmes.
• Managed conflicts involving universities, strategic investors, cell manufacturers and specialist laboratories while protecting unpublished scientific and commercial information.

Non-negotiables

• Can protect three days monthly, attend all five Stockholm committee meetings and travel quarterly to a Nordic laboratory or partner site.
• Will disclose relevant equity, research sponsorship, licensing income, board work and partner relationships before receiving the candidate-chemistry portfolio.
• Accepts that scientists and executives retain research, safety, patent and investment decisions and may reject the advice in a documented process.
• Brings evidence of materials commercialisation beyond software licensing; generic AI strategy or battery-sector governance alone is insufficient.

1. 49 words maximum. Which AI-originated material did you help move into industrial validation, and which physical result most changed its commercial route?
2. 49 words maximum. Identify every current investment, research, board or licensing relationship that this committee should test for conflict.
3. 49 words maximum. Confirm the Stockholm cadence and state the evidence gate you would require before funding a predicted chemistry’s pilot manufacture.