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:
2450987978
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Peter Stone (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Org Design: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Firm Productivity: 1 paper
- Other: 1 paper
- Skill Acquisition: 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 significantly improves business decision-making across forecasting, operations and customer management, but its benefits are contingent on data quality, explainability, workforce skills and effective governance; successful firms will pair AI tools with strong oversight and organizational change rather than relying on automation alone.openalex | Peter Stone provider id |
2026-07-17 | 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.