Generative AI for Business Professionals

Why Attend

Generative AI provides business professionals with new ways to research information, develop content, analyze documents, generate ideas, support decisions, and improve knowledge-intensive processes. Creating meaningful business value from these technologies requires more than knowing how to use an AI chatbot. Professionals must understand how to select appropriate use cases, provide effective instructions, evaluate AI-generated information, redesign workflows, and manage the risks associated with enterprise AI adoption.

This Generative AI for Business Professionals course develops practical capabilities for applying tools such as ChatGPT, Microsoft Copilot, Gemini, and other generative AI platforms to business activities. Participants learn prompt engineering, AI-assisted research and analysis, content generation, workflow design, responsible AI practices, and methods for evaluating productivity and business impact.

Professional Problems This Course Is Designed to Solve

• Low productivity in knowledge-intensive work: Reducing time spent drafting, summarizing, researching, restructuring, and processing business information.
• Inconsistent generative AI results: Improving output quality through structured prompts, context management, examples, constraints, and iterative refinement.
• Information overload: Using generative AI to summarize documents, compare information, extract key points, and organize large volumes of business content.
• Limited integration of AI into business processes: Moving beyond isolated chatbot use toward repeatable AI-assisted workflows connected to real job responsibilities.
• Difficulty identifying valuable AI use cases: Evaluating where generative AI can improve speed, quality, customer experience, creativity, or decision support.
• Unreliable AI-generated information: Managing hallucinations, bias, unsupported claims, outdated information, and other limitations through verification and human review.
• Generative AI governance risks: Protecting confidential information, intellectual property, personal data, and organizational knowledge while promoting responsible AI adoption.

Course Methodology

The programme combines practical demonstrations, guided AI exercises, prompt engineering workshops, business scenarios, document analysis activities, case studies, and workflow redesign exercises. Participants use realistic business tasks to explore how generative AI can support communication, research, analysis, problem-solving, decision preparation, and process improvement while maintaining appropriate human oversight.

Course Objectives

By the end of this course, participants will be able to:

• Understand generative AI, large language models, and their business applications
• Identify high-value generative AI use cases across different organizational functions
• Develop effective prompts for professional and analytical tasks
• Use generative AI to research, summarize, analyze, and structure business information
• Improve business writing, communication, and content development with AI assistance
• Apply generative AI to problem-solving, brainstorming, and decision preparation
• Design repeatable human-AI workflows for knowledge-intensive business processes
• Evaluate AI-generated outputs for accuracy, relevance, bias, and reliability
• Manage confidentiality, intellectual property, and responsible AI risks
• Develop practical approaches for measuring generative AI productivity and business value

Target Audience

This course is suitable for:

• Managers and team leaders
• Business analysts and consultants
• Finance and commercial professionals
• Human resources professionals
• Marketing and communications teams
• Project and programme professionals
• Operations and administrative professionals
• Business professionals responsible for digital transformation and process improvement

Target Competencies

Participants will develop competencies in:

• Generative AI literacy
• Prompt engineering
• AI-assisted research
• Business content generation
• AI-supported analysis
• Business problem-solving
• Workflow redesign
• AI output verification
• Responsible AI
• Human-AI collaboration