The British Academy for Training and Development offers this training program titled: "AI-Powered Decision-Making for Business Leaders", aimed at empowering executives and senior leaders to understand how to leverage artificial intelligence tools to enhance the quality and effectiveness of their strategic decision-making.

In today’s complex and fast-paced business environment, relying solely on human analysis is no longer sufficient. Organizations are increasingly adopting AI as a core enabler for data-driven decision-making.

This program explores key concepts linking AI to decision-making, showcasing real-world applications that allow leaders to employ technologies such as machine learning, predictive analytics, and intelligent decision support systems to make faster, more informed choices in competitive settings. Participants will gain a clear understanding of how to evaluate the outcomes and risks of intelligent systems and how to ensure their responsible use in alignment with the organization’s values and goals.

Who Should Attend?

• CEOs and General Managers in both public and private sector organizations.

• Strategic leaders and board members.

• Business Development and Digital Transformation Managers.

• Decision-makers seeking to integrate AI into their strategies.

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• Understand the relationship between AI and decision-making in business contexts.

• Identify intelligent tools and technologies that support decision-making.

• Use AI to analyze data and make predictive, data-informed decisions.

• Evaluate the benefits and risks of AI-powered decision-making systems.

• Enhance their leadership capabilities in driving smart transformation across the organization.

• Introduction to AI and Decision-Making

  • Definition and types of artificial intelligence in the business environment

  • Differences between human and AI-assisted decision-making

  • The role of AI in developing modern decision-making models

• Data as the Foundation of AI

  • Types of data used in intelligent systems

  • The importance of data quality and its impact on decision outcomes

  • Data analytics tools and their role in supporting executive management

• Intelligent Decision Support Systems (DSS)

  • Concepts and evolution of Decision Support Systems

  • Applications of AI in operational and strategic decision-making

  • Integration of DSS with Business Intelligence (BI) systems

• Predictive Analytics and Scenario Planning

  • Using machine learning to identify patterns and forecast outcomes

  • Building “What-if” scenarios for more precise decision-making

  • Predictive modeling techniques across various industries and sectors

• Ethical Decision-Making Using AI

  • Ethical principles associated with the use of AI

  • Algorithmic bias and its impact on decision outcomes

  • Mechanisms to review and improve fairness and transparency in intelligent systems

• AI Governance in Organizations

  • Developing internal policies for managing AI applications

  • Oversight and accountability when using automated decision-making systems

  • Aligning AI initiatives with organizational goals and strategies

• Integrating AI into Institutional Decision-Making

  • Stages of AI-assisted decision-making

  • Case studies of successful AI integration across sectors

  • The leadership role in promoting AI adoption

• Digital Transformation and Data-Driven Leadership

  • The difference between digital transformation and AI adoption

  • Leadership competencies in the era of big data and intelligent decisions

  • Creating a culture of analytics and innovation within the organization

• AI-Supported Decision-Making Under Uncertainty

  • Strategies to adapt to unexpected changes using smart models

  • Probabilistic analysis and risk assessment in unstable conditions

  • Leveraging AI to strengthen organizational resilience

• Roadmap for AI Implementation in Decision-Making

  • Practical steps for identifying high-priority areas for AI adoption

  • Common challenges and strategies to overcome them

  • Developing a sustainable action plan to implement AI in the workplace