  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.
