Generative AI in the daily work of a Business Analyst Training Course
This instructor-led, live training (online or onsite) is designed for individuals at beginner, intermediate, and advanced levels who want to learn how to harness Large Language Models to overcome creative obstacles, organize complex workshop data, and automate repetitive documentation tasks. In today's era of digital transformation, Generative AI (GenAI) is becoming an essential "copilot" in analytical work. This course focuses not only on the technology but also on its practical application within the Business Analysis Life Cycle. The training approach emphasizes a "human-in-the-loop" methodology, teaching participants to maintain a critical perspective and ensure the highest quality of AI-supported analytical outputs.
By the end of this training, participants will be able to:
- Grasp the true capabilities and limitations of GenAI models (e.g., hallucinations, context windows).
- Utilize AI as an advanced tool to support thinking and analytical processes.
- Learn how to create accurate requirement structures and technical documents using AI.
- Speed up conceptual work, from generating hypotheses to drafting documentation.
- Consciously assess the quality and substantive accuracy of AI-generated outputs.
- Gain knowledge on the safe and ethical use of AI tools in relation to corporate data.
Format of the Course
- Interactive lectures and discussions.
- Numerous exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This course is available as onsite live training in Uzbekistan or online live training.
Course Outline
Fundamentals and Applications of Gen AI
- Introduction to Generative AI
- What is Gen AI and how it works
- Language models and their limitations
- The role of humans in AI-assisted work
2. Gen AI for the Business Analyst
- Support in problem analysis
- Information structuring
- Drafting documents and reports
3. Prompting in practice
- How to formulate effective prompts
- Context, objectives, and constraints
- Iterative refinement of outputs
4. Supporting business analysis
- Defining business problems
- Formulating hypotheses
- Scenario analysis
5. AI-assisted analytical documentation
- Business requirements
- Process descriptions
- Workshop notes and summaries
Integrating AI into daily workflow
- Gen AI as a thinking aid, not a replacement
- Critical assessment of AI outputs
- Content verification and validation
- Avoiding thoughtless automation
2. AI in stakeholder communication
- Preparing communications and updates
- Simplifying complex content
- Tailoring messages for different audiences
3. Risks and responsibility
- Data privacy and confidentiality
- Accountability for AI-generated content
- Ethical considerations
4. Building a custom BA workflow with AI
- Practical use-case scenarios
- Integration with productivity tools
- Team best practices
Requirements
- Professional experience in a role related to data analysis, reporting, or business process support.
- Technical skills: Proficiency in MS Excel (lookup functions, pivot tables, basic charting).
- Domain knowledge: Basic understanding of Key Performance Indicators (KPIs) within one's business area and familiarity with the data lifecycle in an organization.
Audience
- Business and Systems Analysts.
- Product Owners and Product Managers.
- Business Consultants.
- Requirements Engineering Specialists.
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Testimonials (1)
Professional knowledge from the market provided by an expert
Bartlomiej Srednicki - GP Strategies Poland sp. z o.o.
Course - Fintech: A Practical Introduction for Managers
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