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Automating the Enterprise with Foundation Models (2405.03710v1)

Published 3 May 2024 in cs.SE, cs.AI, and cs.LG

Abstract: Automating enterprise workflows could unlock $4 trillion/year in productivity gains. Despite being of interest to the data management community for decades, the ultimate vision of end-to-end workflow automation has remained elusive. Current solutions rely on process mining and robotic process automation (RPA), in which a bot is hard-coded to follow a set of predefined rules for completing a workflow. Through case studies of a hospital and large B2B enterprise, we find that the adoption of RPA has been inhibited by high set-up costs (12-18 months), unreliable execution (60% initial accuracy), and burdensome maintenance (requiring multiple FTEs). Multimodal foundation models (FMs) such as GPT-4 offer a promising new approach for end-to-end workflow automation given their generalized reasoning and planning abilities. To study these capabilities we propose ECLAIR, a system to automate enterprise workflows with minimal human supervision. We conduct initial experiments showing that multimodal FMs can address the limitations of traditional RPA with (1) near-human-level understanding of workflows (93% accuracy on a workflow understanding task) and (2) instant set-up with minimal technical barrier (based solely on a natural language description of a workflow, ECLAIR achieves end-to-end completion rates of 40%). We identify human-AI collaboration, validation, and self-improvement as open challenges, and suggest ways they can be solved with data management techniques. Code is available at: https://github.com/HazyResearch/eclair-agents

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Citations (4)
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Summary

  • The paper presents a novel system, ECLAIR, that harnesses multimodal foundation models to automate enterprise workflows with minimal human oversight.
  • It demonstrates how integrating visual demonstrations and textual guidelines improves workflow documentation accuracy (precision ~0.94, recall ~0.95) versus traditional RPA.
  • The study highlights the ability of ECLAIR to double execution rates and reduce manual intervention by addressing RPA’s brittleness and high maintenance costs.

Automating the Enterprise with Foundation Models

The paper "Automating the Enterprise with Foundation Models" (2405.03710) presents a compelling exploration into the application of Multimodal Foundation Models (FMs) for the automation of enterprise workflows. The authors identify the potential for significant productivity gains and address the limitations of traditional Robotic Process Automation (RPA) methods, proposing an innovative system named ECLAIR (Enterprise Scale AI for Workflows) that seeks to leverage the capabilities of multimodal FMs to achieve automated enterprise workflows with minimal human supervision.

Introduction

Digital workflows are pervasive in modern economic structures, with a significant proportion of jobs involving repetitive, manual digital tasks. These tasks, often peripheral to core job functions, represent a substantial opportunity for automation, projected to translate into approximately \$4 trillion in annual productivity gains. Current solutions, mainly relying on RPA, have struggled due to high setup costs, brittle execution, and expensive maintenance necessitating multiple full-time equivalents (FTEs) dedicated to their upkeep. In many industry sectors, the limitations of RPA, stemming from its inflexible, rule-based nature, render it insufficient for achieving holistic workflow automation. Figure 1

Figure 1: Differences between and traditional RPA. **ECLAIR

* uses FMs to learn expertise via video demonstrations (left), navigate GUIs given written documentation (center), and audit completed workflows (right).*

Traditional RPAs are reliant on process mining to encode human expertise into strict rules for software agents to follow. High setup costs, lack of adaptability, and the need for continuous maintenance have restricted broader adoption, limiting the scalability and broader application across enterprises.

Introduction to

The paper introduces a novel system, ECLAIR ("Enterprise Scale AI for Workflows"), leveraging the capabilities of multimodal Foundation Models (FMs) like GPT-4. With attributes such as high-level reasoning, planning, and visual understanding, FMs offer potential solutions to the limitations of conventional RPA systems. Figure 1

Figure 1: Differences between **ECLAIR

* and traditional RPA. ECLAIR uses FMs to learn expertise via video demonstrations (left), navigate GUIs given written documentation (center), and audit completed workflows (right).*

Multimodal Foundation Models Overcoming RPA Limitations

The paper identifies several critical shortcomings in traditional RPA deployments which FMs are uniquely positioned to overcome:

  1. High Set-Up Costs: RPA requires manual, rule-based workflow scripting and significant setup time, often over a year and at substantial costs as evidenced by a case paper involving a hospital deployment.
  2. Brittle Execution: RPA's reliance on static rules leads to frequent failures whenever input conditions vary, resulting in initial accuracy as low as 60% in case studies.
  3. Maintenance Demand: Continuous monitoring by full-time employees (FTEs) is often necessary for RPA efficacy, making it burdensome for enterprises.

Demonstrating and Learning from Workflows

ECLAIR endeavors to approximate human-level workflow understanding through multimodal FMs, efficiently minimizing costs associated with traditional RPA.

Experimentation in Workflow Understanding

The paper evaluated GPT-4's ability to infer and document workflow steps based on multimodal inputs including workflow descriptions, visual demonstrations, and annotations. As highlighted by the results in Table 1, incorporating both visual (key frames) and action logs when prompting GPT-4 leads to SOPs with high accuracy and completeness rates (precision of 0.94, recall of 0.95, and SOP correctness of 0.93), albeit still facing challenges related to the hallucination of incorrect steps and errors in grounding action descriptions to precise GUI elements. While early results are promising, enhanced models that can process video inputs directly may provide further improvements.

Execution and Workflow Automation

The execute phase presented a mixed performance when tasking multimodal FMs with accurately predicting actions and grounding those into GUI commands.

Action Suggestion and Grounding

The research demonstrated that offering SOP-based guidance doubled execution rates for workflows sampled from WebArena, with GPT-4 achieving a 0.92 next-action suggestion accuracy and a 40% completion accuracy. Challenges persist in translating high-level action suggestions into precise GUI interactions (Table 2). Enhancements can be made through integrating bounding box detection, leveraging models such as YOLO, which have proven to improve grounding accuracy substantially. Figure 2

Figure 2: **ECLAIR

* can automate entirely new categories of workflows, including those that require complex decision-making or are knowledge-intensive. Examples show real-world hospital workflows.*

Validation and Self-Improvement

For seamless deployment, validation at both individual step and workflow levels is critical. Despite challenges in step-level validation due to issues in detecting discrete interface interactions, FMs have demonstrated efficacy in determining step-by-step correctness and overall workflow completion (Table 3). The development of a database of integrity constraints and techniques from self-adjusting databases could further optimize ECLAIR's capacity for error correction and self-monitoring.

Conclusions

The implementation of ECLAIR, when expanded and refined, heralds a shift in automating comprehensive workflow processes, circumventing the existing limitations of setup costs, execution brittleness, and high maintenance seen with RPA. Future research should investigate combining multiple specialized agents to cater to complex, nuanced workflows and incorporating knowledge accumulation for task automation at enterprise scales, as delineated by recent economic forecasts. Enhancing both GUI element grounding and self-monitoring of multimodal FMs are pivotal areas for further development.

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