Automating the Enterprise with Foundation Models
Summary: ECLAIR uses multimodal foundation models to automate enterprise workflows end-to-end, replacing brittle RPA by interpreting natural-language workflow descriptions for instant setup. Experiments report 93% workflow-understanding accuracy and 40% zero-shot completion; highlights human-AI validation, monitoring, and self-improvement as core data-management research problems. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael Wornow (Stanford University)
- 2. Avanika Narayan (Stanford University)
- 3. Krista Opsahl-Ong (Stanford University)
- 4. Quinn McIntyre (Stanford University)
- 5. Nigam Shah (Stanford University)
- 6. Christopher Ré (Stanford University)
BibTeX Citation
@article{wornow_vldb24,
title = {{Automating the Enterprise with Foundation Models}},
author = {Wornow, Michael and Narayan, Avanika and Opsahl-Ong, Krista and McIntyre, Quinn and Shah, Nigam and Ré, Christopher},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {2805--2812},
doi = {10.14778/3681954.3681964},
url = {https://doi.org/10.14778/3681954.3681964},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,906 | Unveiling Challenges for LLMs in Enterprise Data Engineering | 2026 | VLDB | 5.3483178e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 7 | 3,536 | How Large Language Models Will Disrupt Data Management | 2023 | VLDB |
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