Across the dynamic business landscape today, enterprises face an ever-increasing range of challenges. These include the constantly evolving regulatory environment, the growing demand for personalization within software applications, and the heightened emphasis on governance. In response to these multifaceted demands, large enterprises have been adopting automation that spans from the optimization of core business processes to the enhancement of customer experiences. Indeed, Artificial Intelligence (AI) has emerged as a pivotal element of modern software systems. In this context, data plays an indispensable role. AI-centric software systems based on supervised learning and operating at an industrial scale require large volumes of training data to perform effectively. Moreover, the incorporation of generative AI has led to a growing demand for adequate evaluation benchmarks. Our experience in this field has revealed that the requirement for large datasets for training and evaluation introduces a host of intricate challenges. This book chapter explores the evolving landscape of Software Engineering (SE) in general, and Requirements Engineering (RE) in particular, in this era marked by AI integration. We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems. The chapter provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary for effectively building solutions with NLP at their cores. We also reflect on how these text data-centric tasks sit together with the traditional RE process. We also highlight new RE tasks that may be necessary for handling the increasingly important text data-centricity involved in developing software systems.

翻译：在当今动态的商业环境中，企业面临日益增多的挑战，包括不断演变的监管环境、软件应用中对个性化需求的增长，以及治理要求的显著提升。为应对这些复合需求，大型企业正在采用自动化技术，涵盖从核心业务流程优化到客户体验增强的各个方面。事实上，人工智能已成为现代软件系统的关键要素。在此背景下，数据发挥着不可或缺的作用。基于监督学习且以工业规模运行的以AI为核心的软件系统，需要大量训练数据才能有效运行。此外，生成式AI的引入导致对充分评估基准的需求日益增长。我们在该领域的经验表明，训练和评估所需的大规模数据集会引发一系列复杂挑战。本章探讨了在AI集成这一时代背景下，软件工程（SE）整体及需求工程（RE）领域正在演变的格局。我们将讨论在将自然语言处理（NLP）和生成式AI融入企业关键软件系统时出现的挑战。本章提供实用的见解、解决方案及示例，帮助读者掌握有效构建以自然语言处理为核心的解决方案所需的知识和工具。同时，我们还将反思这些以文本数据为中心的任务如何与传统需求工程流程相融合，并着重指出为应对开发软件系统中日益重要的文本数据中心化可能所需的新型需求工程任务。