Evidence-first · Triple impact · Decision traceability

Turn an existing BMC into a greener, circular and socially responsible model.

AI proposes. Evidence validates. Entrepreneurs decide.

9 × 3Economic, environmental and social analysis

Human-ledEvery AI proposal is accepted, modified or rejected

Evidence gateNo impact claim becomes validated without a source

Research decision-support prototype — not a sustainability certification or automated audit.

1 — Conventional Business Model Canvas

Describe the existing model. At least three completed blocks are required; richer input produces a more useful analysis.

0/9 blocks completedAim for concise, factual descriptions. Never enter personal, confidential or commercially sensitive information.

Customer Segments Value Proposition Channels Customer Relationships Revenue Streams Key Resources Key Activities Key Partners Cost Structure

2 — Green Blind Spot Scanner

3 — 9 × 3 Transformation Matrix

AI coverage0/9blocks analysed

Human decisions0/9accepted, modified or rejected

Circular levers0/4selected by entrepreneur

Evidence maturity0/1claims validated with a source

BMC Block	Economic	Environmental	Social	Decision

4 — Circularity Explorer

Quick Green Wins

Reduce waste, monitor energy, improve sourcing, add measurable targets.

Circular Redesign

Reuse, repair, recovery, end-of-life and circular revenue logic.

Deep Impact Transformation

Revisit value proposition, stakeholders, social value and measurable impact.

Selected pathway: —

Waste → input? Repair / reuse / maintenance? End-of-life recovery? Access/service instead of ownership?

5 — Greenwashing Guard & Evidence Gate

Claim to test

StatusHYPOTHESIS

Evidence requiredSupplier data · price · technical compatibility · lifecycle evidence

AI Suggestion→Entrepreneur Hypothesis→Field Evidence→Validated Decision

Evidence or observation Source / reference Date collected

6 — Before / After

7 — Green Transformation Passport

8 — Research Protocol

Research question

Can generative AI help young entrepreneurs systematically retrofit conventional business models toward greener, more circular and socially responsible configurations while preserving commercial coherence, entrepreneurial ownership and evidence discipline?

Research gap

Existing literature provides sustainable and circular business-model frameworks, while recent work explores AI-assisted entrepreneurship. BMC2Green AI studies the traceable retrofit process from an existing conventional BMC, with human decisions and explicit evidence gates.

Empirical design

Retrospective audit of conventional BMCs to measure Green Blind Spots.
Prospective before/after study using BMC2Green AI.
If feasible: conventional support vs generic LLM vs BMC2Green AI.

Documented field base

500+ projects supported through PEE/4C mechanisms
45 startups supported through Souk Ettanmia I–III
15 projects through MEDSt@rts
YADOUM 2026: 30 eco-entrepreneurs produced a GBMC
Fondation Orange GBMC 2026: 10 participants evaluated
Fondation Orange GBP 2025: 8 participants; 6 plans submitted/scored
At least 3 documented Green BMC certification sessions before April 2026

These figures describe the field base. They do not prove the effectiveness of BMC2Green AI.

Selected references

Bocken et al. (2014), sustainable business model archetypes.
Joyce & Paquin (2016), Triple Layered Business Model Canvas. DOI: 10.1016/j.jclepro.2016.06.067
Geissdoerfer, Vladimirova & Evans (2018). DOI: 10.1016/j.jclepro.2018.06.240
Awana et al., circular start-up barriers. DOI: 10.1002/bse.3608
Doshi et al., LLM business-model evaluation limits. DOI: 10.1002/smj.3677

Re-check bibliographic details before final submission.