Eliciting Business Intelligence (BI) requirements is challenging, especially in dynamic business environments. This paper introduces AutoBIR, an AI-driven system that uses semantic search and Large Language Models (LLMs) to automate BI specification and prototyping. Through a conversational interface, it translates user inputs into analytic code, descriptions, and data dependencies while generating test-case reports with optional visuals. AutoBIR refines BI reporting via feedback, accelerating data-driven decision-making. We also explore the broader potential of generative AI in transforming BI development, illustrating its role in enhancing data engineering practice for large-scale, evolving systems.

Regulations constitute a rich source of requirements for software systems, especially so for information systems that handle sensitive data. However, there has been little attention paid to regulators and their requirements for managing the regulatory lifecycle. This paper presents a study of the state-of-practice for regulators in Canada by examining seven Government of Canada (GoC) agencies responsible for regulations. In each case, we attempt to capture the context within which regulations are created, the motivation behind these regulations, and the practices related to their design, enforcement, and review. Our aims are to understand how regulators currently design, monitor, and assess regulations and other regulatory instruments in their respective domains, and to identify opportunities where information system (IS) solutions can be applied to improve practice. Our field study involved reviewing publicly available information and conducting informal interviews. Together, these activities helped us understand key regulators' activities and concerns, as well as important challenges they currently face. In this paper, we summarize our findings and explain the implications for the use of ISs to improve the practice of regulatory management in the form of a research agenda.

With regulations influencing almost all societal interactions, divergent views about their relevance and performance exist today. In consideration of the time and cost implication involved in complying, regulated parties often complain of overregulation. Governments, motivated to ensure the safety, security and prosperity of their communities, insist on effective and efficient regulations and advocate for more. Additionally, citizens and interest groups advocate for more or fewer regulations depending on perceived benefits to them. This scenario puts pressure on regulators mandated to administer regulations. They are required to be transparent and accountable in showing the performance of regulations while accommodating the concerns of different stakeholders involved. In this regard, this doctoral research proposes a framework that regulators can use to show the performance of their regulations through modeling and measuring regulations and evaluating and reporting their outcomes. The Regulator-Oriented Regulatory Intelligence Framework (RORIF) adopts a regulatory intelligence approach that involves the use of data and takes advantage of the capabilities of existing Business Intelligence and analytical tools.