Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2056002673
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- J. Lambert (semantic scholar, provider refresh)
- Jerome Lambert (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Firm Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Developer Productivity: 1 paper
- Governance And Regulation: 1 paper
- Worker Satisfaction: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| AI in energy delivers only when organisations invest in people, processes and fit: broad non-specialist upskilling, transparent assurance with appeal rights, and workflow-friendly design are the three levers that convert AI tools into trusted, sustained use and credible reliability and emissions benefits.openalex | J. Lambert provider id |
2026-04-16 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.