利用GenAI推进Uber的发票文档处理
In today’s fast-paced business environment, efficiently managing operational tasks is vital for maintaining workflows. Uber, with its large network of suppliers worldwide, faces considerable challenges in processing a high volume of invoices daily. Invoice processing is a critical function for Uber’s financial operations, directly impacting the efficiency and accuracy of our accounts payable processes.
在当今快节奏的商业环境中，高效管理运营任务对于维护工作流程至关重要。Uber凭借其全球庞大的供应商网络，在每天处理大量发票时面临着相当大的挑战。发票处理是Uber财务运营的关键功能，直接影响到我们应付账款流程的效率和准确性。
This blog explores how we used GenAI to solve this problem, setting a new standard in financial operations management.
这篇博客探讨了我们如何使用GenAI解决这个问题，为财务运营管理设定了新的标准。
The traditional invoice processing approach relies on manual data entry or automation like RPA (Robotic Process Automation), Excel uploads, and rule-based systems. While Uber has automation solutions like RPA and self-serve supplier platforms, a significant portion of invoices still require manual handling. We believed that we could improve the existing process as the old process has proven inadequate in speed, accuracy, and cost-effectiveness.
传统的发票处理方法依赖于手动数据输入或像RPA（机器人流程自动化）、Excel上传和基于规则的系统等自动化。虽然Uber有像RPA和自助供应商平台这样的自动化解决方案，但仍有相当一部分发票需要手动处理。我们相信可以改善现有流程，因为旧流程在速度、准确性和成本效益方面已被证明不足。
To address inefficiencies in existing invoicing mechanisms, we implemented a GenAI-powered invoice automation system. This innovation uses advanced ML and NLP (Natural Language Processing) to automate and improve the invoice processing workflow, significantly reducing manual intervention and operational costs. The result is a robust and scalable system that accelerates invoicing processes, minimizes errors and improves the user experience.
为了解决现有发票机制中的低效问题，我们实施了一个基于GenAI的发票自动化系统。该创新利用先进的机器学习和自然语言处理（NLP）来自动化和改善发票处理工作流程，显著减少人工干预和运营成本。结果是一个强大且可扩展的系统，加速了发票处理流程，最小化了错误并改善了用户体验。
Uber relies on external suppliers for goods and services. A supplier first onboards to Uber based on requirements. Uber employees then raise a PO (Purchase Order) against the suppliers representing ...