information systems

How can compliance ecosystems be designed as self-healing systems, resilient to breaches and capable of automatically preventing recurrences? Recent advances in agentic AI suggest technological solutions, even within current regulations. A case study in global risk governance for data centers demonstrates the research design, compliance ecosystem architecture, and three-dimensional self-healing anatomy: support, government, and control. Self-healing compliance ecosystems allow dynamic consumption of data in indicated modes and are self-healing in the enabling way of autonomic loops, embracing monitoring, remediation, and feedback. The design-supporting analysis suggests action-oriented responses to incidents, disaster recovery, and business continuity, while substantial performance improvements and lessons learned contribute to compliance resilience. Agentic AI allows an adaptive compliance ecosystem acting on behalf of a stakeholder body, enabling a self-healing compliance ecosystem.

The retail and consumer sector is a major contributor to global economic output but faces challenges in terms of uncertain consumer behavior, fast-changing technology, and retail convenience. Accurate demand forecasting is of paramount importance in the overall retail decision-making process. It is a prerequisite of broader application areas, such as stock control, merchandising, and pricing. Inadequate demand forecasting can lead to lost margin opportunities in terms of out-of-stock scenarios, as well as heavy discounting of goods in response to overstocking. In online retail markets, convenience disappears if goods are not available when the consumer wants or a store closes, as goods sell out at a full price. In a world of volatile trading conditions, the forecast of future demand is more difficult and less reliable than it used to be, due to both internal business complexity and increased external market disruption. Demand signal data need to be incorporated into the forecasting process, and it is necessary to use the techniques available to improve the efficiency and speed of the demand forecasting process to enhance the level of operational control. To cater to all these challenges and opportunities, recent academic research has covered a wide range of themes in retail demand forecasting. Retailers themselves are investing heavily in information systems, data management, analytical, and forecasting capabilities. Software providers offer a broad array of demand forecasting tools, some of which are cloud-hosted. Options are attractive as they allow access to broader data and more sophisticated models. Some business problems require solutions that are not covered by off-the-shelf software and therefore may require more customization to be suitable for enterprise implementation. Such solutions could aim at automating the demand forecasting process, leveraging a broad range of external data sources, creating outside views of future demand for bricks-and-mortar retail space, as well as seamless integration with operations and other decision-making cycles. Such AI-driven demand forecasting solutions can achieve positive outcomes, such as enhanced customer experience and supply chain optimization