Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
38 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Automating the Enterprise with Foundation Models (2405.03710v1)

Published 3 May 2024 in cs.SE, cs.AI, and cs.LG
Automating the Enterprise with Foundation Models

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

PDF HTML Abstract

Understanding the Automation of Enterprise Workflows Through Multimodal Foundation Models

Introduction to Multimodal Foundation Models in Automation

In the field of automating business workflows, traditional methods have often stumbled due to a trio of challenges: high setup costs, brittle execution, and labor-intensive maintenance. Enter the promising new approach of deploying multimodal foundation models (FMs) like GPT-4, which are geared towards reducing these hurdles significantly while enhancing the accuracy and efficiency of workflow automation.

The Core Challenges of Traditional RPA

Robotic Process Automation (RPA) has been the go-to technology for enterprise workflow automation, yet it faces significant limitations:

  • High setup costs: RPA requires detailed mapping and scripting by skilled specialists, leading to prolonged and costly setup phases.
  • Brittle execution: Limited by rigid rule-based programming, RPA systems struggle with minor variations in input or interfaces, contributing to low initial accuracy and demanding continuous adjustments.
  • Burdensome maintenance: Continuous human supervision is necessary to manage and correct RPA bots, negating some of the intended efficiency gains.

Advantages of Multimodal Foundation Models

The adoption of multimodal FMs can potentially revolutionize this space. These models showcase an inherent ability to understand and navigate graphical user interfaces (GUIs), plan sequences of actions, and adapt to new workflows with minimal human intervention. The research unveils a system dubbed "ECLAIR" that leverages such models. Key capabilities highlighted are:

  • Learning from demonstrations: ECLAIR achieves impressive results in understanding workflows by observing demonstrations, showing 93% accuracy in recognizing workflow steps from video key frames.
  • Efficient execution: While initializing from just a natural language description, ECLAIR can effectively plan and suggest necessary actions, improving task completion rates significantly from baseline models.
  • Self-monitoring and validation: The capability to self-validate its actions enables ECLAIR to operate with reduced human oversight, achieving high precision and recall in identifying correctly completed tasks.

Practical Implications and Future Prospects

The development of ECLAIR hints at a future where enterprise workflows are not only automated more comprehensively but also with greater adaptability and lower overheads. This could translate to substantial productivity boosts and cost reductions in industries reliant on complex digital workflows.

Shortcomings and Development Path

Despite the promising advancements, ECLAIR and similar systems need further refinement to completely replace traditional RPA:

  • Complex decision-making: The system currently struggles with tasks requiring intricate decision-making or those with very dynamic GUI elements.
  • Full independence from human oversight: While ECLAIR reduces the need for human intervention, certain tasks still necessitate manual handling, especially in sensitive areas needing precise verification.

Impending Enhancements

Future improvements could focus on enhancing the decision algorithms to handle more nuanced tasks and employing more advanced training techniques that allow the models to learn from a broader range of demonstrations with even less specificity required in the training data.

Conclusion

As multimodal FMs continue to evolve, the potential to automate a broader spectrum of workflows at reduced costs and increased reliabilities looms on the horizon. This could mark a pivotal turn in how companies approach process automation, potentially transforming the landscape of enterprise operations technology.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (92)
  1. Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 (2022).
  2. Automation Anywhere. 2020. https://www.automationanywhere.com/company/press-room/global-research-reveals-worlds-most-hated-office-tasks
  3. The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study. In NeurIPS 2023 Foundation Models for Decision Making Workshop.
  4. Automated discovery of process models from event logs: Review and benchmark. IEEE transactions on knowledge and data engineering 31, 4 (2018), 686–705.
  5. David Autor. 2014. Polanyi’s paradox and the shape of employment growth. Technical Report. National Bureau of Economic Research.
  6. Maintaining database integrity with refinement types. In European Conference on Object-Oriented Programming. Springer, 484–509.
  7. Introducing our Multimodal Models. https://www.adept.ai/blog/fuyu-8b
  8. Matthew Bayley and Ed Levine. 2013. Hospital revenue cycle operations: opportunities created by the ACA. Management (2013).
  9. Querying with access patterns and integrity constraints. Proceedings of the VLDB Endowment 8, 6 (2015), 690–701.
  10. Amanda Bergson-Shilcock and Roderick Taylor. 2023. Closing the Digital” Skill” Divide: The Payoff for Workers, Business, and the Economy. National Skills Coalition (2023).
  11. Alessandro Berti and Mahnaz Sadat Qafari. 2023. Leveraging Large Language Models (LLMs) for Process Mining (Technical Report). arXiv preprint arXiv:2307.12701 (2023).
  12. Collaborative data analytics with DataHub. In Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, Vol. 8. NIH Public Access, 1916.
  13. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).
  14. Generative AI at work. Technical Report. National Bureau of Economic Research.
  15. Fabio Casati and Ming-Chien Shan. 2000. Process automation as the foundation for e-business. In VLDB. Citeseer, 688–691.
  16. From Robotic Process Automation to Intelligent Process Automation: –Emerging Trends–. In Business Process Management: Blockchain and Robotic Process Automation Forum: BPM 2020 Blockchain and RPA Forum, Seville, Spain, September 13–18, 2020, Proceedings 18. Springer, 215–228.
  17. The economic potential of generative AI The next productivity frontier The economic potential of generative AI: The next productivity frontier.
  18. Intelligent methods for business rule processing: State-of-the-art. arXiv preprint arXiv:2311.11775 (2023).
  19. Laila Dahabiyeh and Omar Mowafi. 2023. Challenges of using RPA in auditing: A socio-technical systems approach. Intelligent Systems in Accounting, Finance and Management (2023).
  20. Mind2Web: Towards a Generalist Agent for the Web. arXiv:2306.06070 [cs.CL]
  21. Towards a unified agent with foundation models. arXiv preprint arXiv:2307.09668 (2023).
  22. AI-augmented business process management systems: a research manifesto. ACM Transactions on Management Information Systems 14, 1 (2023), 1–19.
  23. How well can large language models explain business processes? arXiv preprint arXiv:2401.12846 (2024).
  24. Dahlia Fernandez and Aini Aman. 2021. The challenges of implementing robotic process automation in global business services. International Journal of Business and Society 22, 3 (2021), 1269–1282.
  25. Drive like a human: Rethinking autonomous driving with large language models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 910–919.
  26. Multimodal Web Navigation with Instruction-Finetuned Foundation Models. arXiv preprint arXiv:2305.11854 (2023).
  27. An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and parallel Databases 3 (1995), 119–153.
  28. Large Language Models can accomplish Business Process Management Tasks. In International Conference on Business Process Management. Springer, 453–465.
  29. A real-world webagent with planning, long context understanding, and program synthesis. arXiv preprint arXiv:2307.12856 (2023).
  30. WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models. arXiv:2401.13919 [cs.CL]
  31. From revenue cycle management to revenue excellence.
  32. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352 (2023).
  33. CogAgent: A Visual Language Model for GUI Agents. arXiv preprint arXiv:2312.08914 (2023).
  34. Data management perspectives on business process management: tutorial overview. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 943–948.
  35. A data-driven approach for learning to control computers. In International Conference on Machine Learning. PMLR, 9466–9482.
  36. Robotic process automation: systematic literature review. In Business Process Management: Blockchain and Central and Eastern Europe Forum: BPM 2019 Blockchain and CEE Forum, Vienna, Austria, September 1–6, 2019, Proceedings 17. Springer, 280–295.
  37. ADEPT: An agent-based approach to business process management. ACM Sigmod Record 27, 4 (1998), 32–39.
  38. How can we know what language models know? Transactions of the Association for Computational Linguistics 8 (2020), 423–438.
  39. CHORUS: Foundation Models for Unified Data Discovery and Exploration. arXiv preprint arXiv:2306.09610 (2023).
  40. Victor Kilanko. 2023. Leveraging Artificial Intelligence for Enhanced Revenue Cycle Management in the United States. International Journal of Scientific Advances 4, 4 (2023), 505–14.
  41. Robotic process mining: vision and challenges. Business & Information Systems Engineering 63 (2021), 301–314.
  42. Xavier Lhuer. 2016. The next acronym you need to know about: RPA (robotic process automation). (2016).
  43. More agents is all you need. arXiv preprint arXiv:2402.05120 (2024).
  44. Interactive task and concept learning from natural language instructions and gui demonstrations. arXiv preprint arXiv:1909.00031 (2019).
  45. Encouraging divergent thinking in large language models through multi-agent debate. arXiv preprint arXiv:2305.19118 (2023).
  46. Demonstration of collaborative and interactive workflow-based data analytics in texera. Proceedings of the VLDB Endowment 15, 12 (2022), 3738–3741.
  47. Bolaa: Benchmarking and orchestrating llm-augmented autonomous agents. arXiv preprint arXiv:2308.05960 (2023).
  48. Query-based workload forecasting for self-driving database management systems. In Proceedings of the 2018 International Conference on Management of Data. 631–645.
  49. Interrupt Handling Schemes in Operating Systems. Springer.
  50. Process automation using RPA–a literature review. Procedia Computer Science 219 (2023), 244–254.
  51. Towards large language model-based personal agents in the enterprise: Current trends and open problems. In Findings of the Association for Computational Linguistics: EMNLP 2023. 6909–6921.
  52. Can Foundation Models Wrangle Your Data? Proceedings of the VLDB Endowment 16, 4 (2022), 738–746.
  53. R OpenAI. 2023. GPT-4 technical report. arXiv (2023), 2303–08774.
  54. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022), 27730–27744.
  55. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 1–22.
  56. Self-Driving Database Management Systems.. In CIDR, Vol. 4. 1.
  57. Make your database system dream of electric sheep: towards self-driving operation. Proceedings of the VLDB Endowment 14, 12 (2021), 3211–3221.
  58. Prototyping and implementing Robotic Process Automation in accounting firms: Benefits, challenges and opportunities to audit automation. International Journal of Accounting Information Systems 51 (2023), 100641.
  59. R1. 2022. Healthcare Financial Trends Report. https://www.r1rcm.com/news/healthcare-trends-and-data-show-clinical-shortage-tip-of-the-iceberg
  60. Worker skill estimation in team-based tasks. Proceedings of the VLDB Endowment 8, 11 (2015), 1142–1153.
  61. Lars Reinkemeyer. 2020. Process mining in action. Process Mining in Action Principles, Use Cases and Outloook (2020).
  62. A Case for Business Process-Specific Foundation Models. In International Conference on Business Process Management. Springer, 44–56.
  63. Tara Safavi and Danai Koutra. 2021. Relational world knowledge representation in contextual language models: A review. arXiv preprint arXiv:2104.05837 (2021).
  64. Invoice processing using robotic process automation. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol 6, 2 (2020), 216–223.
  65. Henriika Sarilo-Kankaanranta and Lauri Frank. 2021. The Slow Adoption Rate of Software Robotics in Accounting and Payroll Services and the Role of Resistance to Change in Innovation-Decision Process. In Conference of the Italian Chapter of AIS. Springer, 201–216.
  66. Business process cockpit. In VLDB’02: Proceedings of the 28th International Conference on Very Large Databases. Elsevier, 880–883.
  67. Fred Schulte and Erika Fry. 2019. Death by 1,000 clicks: Where electronic health records went wrong. Kaiser Health News 18 (2019).
  68. From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces. arXiv preprint arXiv:2306.00245 (2023).
  69. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face. arXiv:2303.17580 [cs.CL]
  70. Reflexion: Language Agents with Verbal Reinforcement Learning.(2023). arXiv preprint cs.AI/2303.11366 (2023).
  71. Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. arXiv preprint arXiv:2305.14975 (2023).
  72. UIPath. 2022. UiPath Certified RPA Associate v1.0 - EXAM Description.pdf. https://start.uipath.com/rs/995-XLT-886/images/UiPath%20Certified%20RPA%20Associate%20v1.0%20-%20EXAM%20Description.pdf
  73. Wil MP Van der Aalst. 2014. Process mining in the large: a tutorial. Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures 3 (2014), 33–76.
  74. Large Language Models for Business Process Management: Opportunities and Challenges. arXiv preprint arXiv:2304.04309 (2023).
  75. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291 (2023).
  76. Cogvlm: Visual expert for pretrained language models. arXiv preprint arXiv:2311.03079 (2023).
  77. Jarvis-1: Open-world multi-task agents with memory-augmented multimodal language models. arXiv preprint arXiv:2311.05997 (2023).
  78. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 (2022), 24824–24837.
  79. Judith Wewerka and Manfred Reichert. 2020. Robotic Process Automation–A Systematic Literature Review and Assessment Framework. arXiv preprint arXiv:2012.11951 (2020).
  80. WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–14.
  81. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155 (2023).
  82. OS-Copilot: Towards Generalist Computer Agents with Self-Improvement. arXiv preprint arXiv:2402.07456 (2024).
  83. Gpt-4v in wonderland: Large multimodal models for zero-shot smartphone gui navigation. arXiv preprint arXiv:2311.07562 (2023).
  84. Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v. arXiv preprint arXiv:2310.11441 (2023).
  85. AppAgent: Multimodal Agents as Smartphone Users. arXiv preprint arXiv:2312.13771 (2023).
  86. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629 (2022).
  87. ProAgent: From Robotic Process Automation to Agentic Process Automation. arXiv preprint arXiv:2311.10751 (2023).
  88. Agflow: Agent-based cross-enterprise workflow management system. In VLDB. 697–698.
  89. UFO: A UI-Focused Agent for Windows OS Interaction. arXiv preprint arXiv:2402.07939 (2024).
  90. Vision-Language Models for Vision Tasks: A Survey. arXiv:2304.00685 [cs.CV]
  91. GPT-4V(ision) is a Generalist Web Agent, if Grounded. arXiv:2401.01614 [cs.IR]
  92. Webarena: A realistic web environment for building autonomous agents. arXiv preprint arXiv:2307.13854 (2023).
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Michael Wornow (12 papers)
  2. Avanika Narayan (13 papers)
  3. Krista Opsahl-Ong (3 papers)
  4. Quinn McIntyre (2 papers)
  5. Nigam H. Shah (39 papers)
  6. Christopher Re (23 papers)
Citations (4)
View on Semantic Scholar
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - HazyResearch/eclair-agents (62 stars)