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Leveraging Artificial Intelligence in Business Analytics for Strategic Decision-Making

Volkmar Kunerth, CEO IoT Business Consultants

Schedule a meeting on Calendly: 15-min slot


The rapid advancement of artificial intelligence (AI) has significantly impacted business analytics, providing valuable insights and competitive information to business planners and decision-makers. This article explores the role of AI in business intelligence (BI), data mining, and business analytics, highlighting the challenges and opportunities associated with delegating management decisions to AI. It also presents an "AI Action Guide for Risk Managers" to help identify critical areas for applying AI methods. It discusses the competencies required by risk managers in the age of AI. Furthermore, the article examines the strategic application of AI for gaining competitive advantages and provides a theoretical framework to guide management and organizational practices.

Introduction: Business intelligence systems are crucial in providing timely and quality data to managers, enabling them to comprehend their company's position relative to rivals. Analyzing changes in market share, consumer behavior, spending patterns, customer preferences, corporate capabilities, and market circumstances is possible through business intelligence tools and technology. Data mining, a nontrivial extraction of implicit, previously unknown, and potentially beneficial information from data, involves techniques such as clustering, data summarization, learning classification rules, discovering dependency networks, analyzing changes, and detecting anomalies.

Managerial Implications: The interaction between company strategy and the deployment of AI technologies has profound implications for managers. Firstly, it necessitates shifting the organizational mindset from a traditional approach to a more data-driven and analytical one. Managers need to develop a deep understanding of AI technologies, their capabilities, and their limitations. Secondly, it requires reevaluating the company's strategic objectives and alignment with the powers of AI technologies. Managers must identify areas where AI can create the most value and align the organization's resources accordingly. Lastly, it transforms the organizational culture to foster innovation, continuous learning, and adaptability.

New Models for Managerial Decision-Making: The deployment of AI technologies necessitates the development of new models for managerial decision-making. Traditional decision-making models may not be suitable for the dynamic and complex environment AI technologies create. New models must incorporate data-driven insights, predictive analytics, and real-time decision-making capabilities. Managers need to develop the ability to interpret and act upon the insights generated by AI technologies and make decisions in real time.

Transforming Organizational Culture: Successfully deploying AI technologies requires changing the organizational culture. Organizations need to foster a culture of innovation, continuous learning, and adaptability. Employees must be trained and upskilled to work with AI technologies and develop a continuous improvement mindset. Organizations must also create an environment encouraging experimentation, risk-taking, and collaboration.

Theoretical Framework: The theoretical framework presented in this section aims to identify the key areas where AI can be strategically applied to create value and gain competitive advantages. It encompasses various dimensions: operational efficiency, customer experience, innovation, and decision-making. Operational efficiency involves optimizing business processes, reducing costs, and improving productivity. Customer experience encompasses personalized offerings, enhanced customer service, and improved customer satisfaction. Innovation consists of developing new products, services, or business models. Decision-making contains data-driven insights, predictive analytics, and real-time decision-making.

Artificial intelligence (AI)

Artificial intelligence (AI) plays a crucial role in business analytics by enabling managers to delegate management decisions effectively to AI systems. This involves using AI algorithms to analyze vast amounts of data, extract meaningful insights, and make informed decisions. However, implementing AI in business analytics has its challenges. Several organizational and technical hurdles need to be addressed to ensure the effective delegation of decisions to AI. This article unpacks the core factors that may hinder or foster effective decision delegation to AI and provides an "AI Action Guide for Risk Managers" developed to support risk managers in identifying key areas to apply AI methods according to their organization's specific requirements and areas of benefit.

Organizational Hurdles:Lack of Understanding: One of the significant challenges organizations face is a need for more understanding of AI technologies and their capabilities. Many managers and decision-makers need to understand how AI algorithms work and how they can be applied to solve business problems. This lack of understanding can lead to resistance to adopting AI technologies and a lack of trust in the decisions made by AI systems.

Data Quality and Availability: The effectiveness of AI algorithms depends on the quality and availability of data. Many organizations need access to high-quality data or have the data organized in a way that AI algorithms can easily use. This can lead to inaccurate insights and suboptimal decisions.

Organizational Culture: The successful implementation of AI in business analytics requires a cultural shift within the organization. Employees need to be trained and upskilled to work with AI technologies, and there needs to be a culture of continuous learning and adaptability.

Technical Hurdles: Algorithm Complexity: AI algorithms can be complex and difficult to understand. This can make it challenging for managers and decision-makers to trust the decisions made by AI systems. Developing transparent and explainable AI algorithms that non-technical stakeholders can easily understand is essential.

Integration with Existing Systems: Integrating AI technologies with business systems and processes can be challenging. Organizations need to invest in the necessary infrastructure and develop robust integration strategies to ensure the seamless operation of AI technologies.

Scalability: As the organization grows, the amount of data generated increases, and the complexity of the business problems that need to be solved also increases. Developing scalable AI algorithms that can handle large volumes of data and complex business problems is essential.

Competencies for Risk Managers: With the progress of AI, risk managers need to position themselves as value drivers within the organization and as AI risk advisers to senior management and the board. This section discusses the essential skills and knowledge required by risk managers in the age of AI. It highlights the importance of a broad digital understanding in addition to traditional risk management skills.

Strategic Application of AI: This section explores the research on the strategic application of AI to gain competitive advantages and provides a theoretical framework that identifies areas for future research. It also discusses the managerial implications of the interaction between company strategy and the deployment of AI technologies and suggests new models for managerial decision-making and transforming organizational culture.

Conclusion: Artificial intelligence has the potential to revolutionize business analytics and strategic decision-making. However, successful adoption and use of AI require a comprehensive understanding of the technology, appropriate governance, and a varied introductory team. This article guides management and organizational practices and identifies areas for future research to comprehend how company strategy and the deployment of AI technologies interact.

Keywords: Artificial Intelligence, Business Intelligence, Data Mining, Business Analytics, Strategic Decision-Making, Risk Management.

Volkmar Kunerth CEO Accentec Technologies LLC & IoT Business Consultants Email: Website: | Phone: +1 (650) 814-3266

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