AI For Management & Decision Makers

INTRODUCTION
In a world where innovation drives success, harnessing the power of Artificial Intelligence (AI) is no longer an option but a strategic imperative. Despite the immense potential of AI, senior management and key decision-makers often grapple with several challenges. Many are uncertain about the extent of AI's capabilities, are unsure which AI solutions best suit their specific needs, and face reluctance or hesitancy to adopt AI-driven changes within their organizations. This lack of understanding and hesitancy can impede the potential for growth and innovation within the company, causing missed opportunities for efficiency gains and competitive advantages.

This intensive two-day "AI For Management & Decision Makers" course closes the gap between AI's untapped potential and practical application within organizations. Tailored for senior management and decision-makers, it offers a deep understanding of AI intricacies, diverse business applications, and strategic implementation techniques.

Participants will gain a comprehensive AI overview, insights into various AI technologies, successful case studies, and guidance on integrating AI into existing workflows. Equipped with this knowledge, attendees can confidently adopt AI, drive positive change, and stay competitive in a dynamic business landscape.


LEARNING OUTCOMES
Upon completing the course, participants should be able to:

1. Define the core concepts of AI and differentiate between various types and applications of AI in different industries.
2. Identify and explain the key AI technologies and techniques, including Machine Learning, Deep Learning, NLP, and Computer Vision.
3. Understand the pivotal role of data in AI implementation, including data collection, quality, and governance.
4. Evaluate the ethical implications of AI deployment and apply ethical considerations to AI projects.
5. Formulate an AI strategy aligned with organizational objectives, considering readiness, resources, and potential ROI.
6. Navigate the AI project lifecycle, from inception to deployment, managing teams, risks, and challenges effectively.
7. Analyze and address ethical and legal concerns surrounding AI implementation within their organization.
8. Implement practical steps for deploying AI solutions, monitoring performance, and fostering a culture conducive to AI adoption.
9. Identify emerging trends in AI and devise strategies for continuous learning and adaptation in the rapidly evolving AI landscape.

• Overview of AI and its impact on businesses
• Different types of AI: narrow vs. general AI
• Emerging trends and innovations in AI
• Real-world applications and success stories in various industries

• Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Deep Learning and Neural Networks
• Natural Language Processing (NLP) and Computer Vision
• Large Language Model
• Overview of readily existing AI frameworks and tools in the market

• Importance of data in AI implementation
• Data quality, collection, and pre-processing
• Interpreting the Confusion Matrix
• Data governance and ethical considerations in AI

• Integrating AI into business strategy and operations
• Identifying AI opportunities and challenges in the corporate landscape
• Assessing organizational readiness for AI adoption
• Formulating an AI strategy aligned with corporate goals
• Case studies of successful AI integration in companies

• Identifying and defining crucial selection criteria for AI vendor evaluation
• Designing a comprehensive weighing matrix for evaluating vendor proposals.
• Applying the weighing matrix to objectively evaluate and compare vendor proposals

• ROI metrics for AI: defining and measuring success beyond financial gains.
• Quantitative and qualitative evaluation criteria for assessing AI project success.
• Calculating and interpreting ROI for AI projects: cost reduction, revenue generation, and intangible benefits.

• Understanding the AI project lifecycle
• Team composition and skills required for AI projects
• Risk management and overcoming common pitfalls in AI implementation
• Best practices for monitoring, evaluating, and iterating AI systems
• Change management and fostering a culture conducive to AI adoption

• Ethical considerations in AI development and deployment
• Ensuring fairness, transparency, and accountability in AI systems
• Legal compliance and regulatory frameworks related to AI

• Senior Management
• Team Leader
• Head of Department
• Key-decision Makers
• IT Department
• C-Suites

• PowerPoint Presentation
• Interactive Group Activities
• Case Study
• Group Discussions
• Simulation