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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5124877879
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Satyadhar Joshi (openalex, provider refresh)
- Satyadhar Joshi (openalex, source metadata)
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
Claim outcomes
- Adoption Rate: 1 paper
- Organizational Efficiency: 1 paper
- Governance And Regulation: 1 paper
- Task Allocation: 1 paper
- Training Effectiveness: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Agentic generative AI can substantially raise organizational performance—often by 1.5–2.5×—but outcomes hinge on executive commitment, change management and infrastructure. A new integrated framework synthesizes empirical, modeling and practitioner evidence and flags adoption timelines (4–8 months) and implementation success rates (65–85%) while noting important uncertainties.openalex | Satyadhar Joshi provider id |
2026-02-03 | 0 |
Citation observation summary
OpenAlex 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.