Generative AI for Business Analytics: Decision-Making, Model Governance, and Performance Measurement
DOI:
https://doi.org/10.56830/WRBA03202603Keywords:
Business Analytics, Decision Making, Governance, Artificial Intelligence, Performance MeasurementAbstract
Generative Artificial Intelligence (AI) is changing the landscape of business analytics by automating tasks such as data generation, report writing, and causal analysis that have traditionally been carried out by data scientists. Its use increases the efficiency of decision-making processes, provides strategic and operational support, and balances the risks associated with automation bias and explainability. The effective use of generative AI relies on multidisciplinary collaboration among data scientists, AI specialists, and business stakeholders, as well as robust governance frameworks that ensure regulatory compliance, transparency, and accountability. To get the most benefit from AI investments and align model outputs with business outcomes requires high-quality data and continuous performance evaluation using A/B testing and causal inference techniques. Architectural patterns for generative AI include support for data analytics, application development, and orchestrated deployment, even though computational costs are a consideration. Organizational readiness in terms of data assets, analytical capability, and ethical standards is very important for adoption as well as change management. New challenges are coming up that relate to improving how models can be understood, supporting better teamwork between humans and AI, and sharing models across different organizations to solve problems related to limited resources. This changing situation highlights a major shift in how business analytics works, where generative AI speeds up not just decision-making but also changes practices in governance, knowledge transfer, and protection of operations, which represents a large step forward in using AI for gaining insight into businesses and influencing their results.
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