AI-Driven Decision Making in Management

Neha Sanjay Ahuja

SBS Swiss Business School Research Conference (SBS-RC) from SBS Swiss Business School

Abstract: Several organizations are directing Artificial Intelligence (AI) driven expertise to assist and analyze data insights, identify gaps, and transform their decision-making proficiency, especially under high pressure and tight timelines. This research article investigates the impact of AI on decision-making and its consequences for individuals, companies, and society. Decision-making is crucial for achieving organizational goals. Accurate data, reports, and decisions enhance business predictions, transform strategies, enable timely mid-stage implementation reviews, facilitate quick decisions, boost productivity, and lead businesses toward success and future growth. This article also examines how AI transforms internal operations across various departments, from transport to consultation management, predicting demands, adjusting supply and data levels, optimizing results, and reducing operating costs. AI software enables data-driven choices, improves customer targeting, enhances development, and customizes reports for precise strategic decisions. The article provides an overview of AI mechanisms such as automation efficiency and complex management for multilayered issues beyond human capacity, succession planning, and risk monitoring, benefiting departments like HR, Finance, Sales, and Marketing, and industries including Healthcare, Finance, Consulting, Transportation, and Food & Beverage, as well as government authorities in process automation. Various technologies and tools globally facilitate data-driven decision-making. This article highlights the positive impact of AI on management operations and company success while acknowledging that incorrect decisions may disappoint organizations. As AI enhances decision-making, challenges like ethical concerns, algorithmic biases, social implications, and the Human-AI partnership need addressing. Data privacy, transparency, accountability, and explainability are essential for reputation management. Companies must prioritize ethical AI practices and transparency, ensuring unbiased algorithms. This article focuses on governance, regulations, and policies to mitigate biases and ensure AI aligns with organizational goals, with an emphasis on improving AI functionality.

Keywords: Artificial Intelligence; transformation; complex management; quick business decision; timelines; increase productivity; cost reduction; business success; augmentation; accuracy; ethical and social concern; data protection; transparency; bias; Government regulations (search for similar items in EconPapers)
Pages: 6 pages
Date: 2024-10
New Economics Papers: this item is included in nep-cmp and nep-ict
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Published in book of proceedings of SBS Swiss Business School Research Conference 2024, pages 7-12

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