From the hash-bound evidence releases
Recorded claims
Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.
For tasks inside the model's capability frontier, AI-assisted consultants completed work more than 25% faster, produced outputs rated more than 40% higher in quality, and completed over 12% more tasks than controls.
Scope: Consultants and the experiment's within-frontier tasks; no direct firm-outcome estimate.
On the task intentionally placed outside the model frontier, AI access made participants less likely to reach the correct answer, demonstrating that average gains reverse when workers misapply the system beyond its reliable task boundary.
Scope: One designed outside-frontier consulting task and one model era.
The Crossref-deposited abstract reports that AI assistance improved performance on management-consulting tasks within its capability frontier but reduced correctness on a managerial task outside that frontier.
Scope: abstract-level evidence from a field experiment with knowledge workers
The abstract reports uneven effects of AI assistance across management-consulting knowledge tasks, improving performance within some task boundaries while harming it outside them.
Scope: abstract-level
Structured relationships
In-corpus citations
Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.
Cites (11)
- Why Providing Humans with Interpretable Algorithms May, Counterintuitively, Lead to Lower Decision-Making Performance
- GenAI as a Power Persuader: How Professionals Get Persuasion Bombed When They Attempt to Validate LLMs
- The Uneven Impact of Generative AI on Entrepreneurial Performance
- Losing Touch: An Embodiment Perspective on Coordination in Robotic Surgery
- The Crowdless Future? Generative AI and Creative Problem-Solving
- Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
- Artificial Intelligence and Management: The Automation-Augmentation Paradox
- “Collaborating” with AI: Taking a System View to Explore the Future of Work
- To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis
- The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market
- Cyborgs, Centaurs and Self-Automators: The Three Modes of Human-GenAI Knowledge Work and Their Implications for Skilling and the Future of Expertise
Cited by (2)
Boundaries
Limitations & independence
Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.
Recorded limitations
- 758 consultants and selected consulting tasks
- Abstract-level Crossref metadata only; full text was not inspected.
- Artificially classified inside/outside-frontier tasks
- Crossref abstract-level metadata only; the publisher surface returned HTTP 403; full text was not inspected
- Crossref supplied only year and month for the issue publication date
- One model generation
- Task quality is not firm financial performance
- The publisher surface was not queried after the lane’s quarantined dynamic-challenge response; no independent publisher-page verification was attempted.
Source independence
Academic field experiment with BCG consultants; focal professional-services setting and a time-specific model.