Knowing how to prompt an AI tool is no longer a differentiator — using it to actually deliver your work is.
Business analysts are increasingly expected to produce more, faster: dashboards in hours rather than days, working prototypes rather than static specifications, and automations that remove routine tasks from the team's backlog entirely. Those who can do this are shaping how their companies adopt AI. Those who can't risk watching their responsibilities migrate to colleagues — or to the tools themselves.
This practical course is the next step after "Applied AI for Business Analysts." It moves beyond prompts and assistants into the areas where AI creates the most visible business value: data analysis and reporting, modeling and prototyping, and end-to-end process automation on no-code platforms.
By the end of the course, you will have built a dashboard, a working prototype, and a real automation of a process you choose yourself — and you will know how to repeat these results in your day-to-day work.
The course is designed for:
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Business analysts who already use AI for documentation and analysis and now want to deliver tangible artifacts — reports, dashboards, prototypes, and automations.
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Analysts working with data and reporting who want to accelerate analysis, reverse-engineer legacy reports, and generate dashboard concepts with AI support.
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Product owners and product analysts who need to validate ideas quickly through AI-assisted modeling and rapid prototyping.
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Professionals on digital transformation and automation initiatives who need a practical understanding of no-code platforms and where AI fits into them.
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Team leaders and project managers with analytical responsibilities who want to identify automation opportunities and pilot them without waiting for development capacity.
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Graduates of "Applied AI for Business Analysts" or equivalent, who are ready to go deeper and apply AI to concrete deliverables rather than general tasks.
During the course, you will work on real cases — your own where possible — and leave with reusable approaches for analytics, modeling, and automation. You will learn how to choose the right tool for each task, where to keep a human in the loop, and how to turn AI from a helpful assistant into a production-grade part of your workflow.
Training course structure "Applied AI for BA:
Automation, Modeling & Analytics"
15-18
participants per group
16 hours
of lectures and hands-on practice
2
Home tasks
Instructor
Dariia Danovska
Lead Business Analyst / Technical Product Manager. CCBA-certified.
AI Specialist. AI automation expert and strategist.
15+ years of experience in business analysis and product management, working on AI projects since 2019.
Focused on implementing AI solutions for businesses — from discovery and strategy through hands-on delivery of automation, analytics, and AI-driven products. Combines deep analytical craft with practical, production-grade experience, bringing AI into real business processes.
Course cost "Applied AI for BA:Automation, Modeling & Analytics"
Individuals:
10,000 - 11,000 UAH
when paying before August 31, 2026 – 10,000 UAH /$280
when paying after August 31, 2026 – 11,000 UAH /$300
Legal entities:
11,000 - 12,000 UAH
when paying before August 31, 2026 – 10,000 UAH /$280
when paying after August 31, 2026 – 11,000 UAH /$300
Course dates "Applied AI for BA:
Automation, Modeling & Analytics"
September 28 - October 21, 2026, Monday and Wednesday
19:00 - 21:00
Session 1. AI in Data Analysis — Working with Raw Data
Format: Lecture + Practice
Topics:
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AI as a data analysis partner for the business analyst
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Types of analytical tasks suitable for AI support
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Strengths, limitations, and quality-control checkpoints
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Working with raw data using AI
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Cleaning, normalizing, and structuring datasets
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Exploratory analysis and pattern detection
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Generating insights and narrative summaries from numbers
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Practice: Using AI to analyze a sample dataset
Session 2. Reports, Dashboards, and Reverse Engineering
Format: Lecture
Topics:
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AI-enabled BI tools: Power BI Copilot, Tableau AI, ChatGPT Data Analyst — and their real limits
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Governance at AI speed: metric definitions and lineage that outlive the tool
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Reverse engineering as adjudication: recover → verify → risk-tier → decide
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Documentation as a durable contract that survives a reorg
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Practice: three teams, three inherited legacy exports — recover the hidden logic
Session 3. Modeling with AI — From Text to Diagrams
Format: Lecture + Practice
Topics:
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Turning requirements text into BPMN, user journeys, and data models
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Working with Mermaid, PlantUML, and diagram-native AI tools
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Prompt patterns that produce a diagram you can defend
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Verifying models against reality: what AI hallucinates in flows
Session 4. Rapid Prototyping with AI
Format: Lecture + Practice
Topics:
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Rapid prototyping with AI
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Generating wireframes and low-fidelity mockups from requirements
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Producing interactive prototypes with tools such as Uizard, Figma AI
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Quality and traceability
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Keeping models, prototypes, and requirements aligned
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Common pitfalls and how to avoid AI-generated inconsistencies
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Practice: Building a clickable prototype
Session 5. Process Automation — Overview of No-Code Platforms
Format: Lecture + Practice
Topics:
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No-code and low-code platforms
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Workflow automation: Zapier, Make, n8n, Power Automate
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AI-native automation: agents, events, triggers etc.
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Designing automations that are safe and maintainable
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Human-in-the-loop checkpoints
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Error handling, logging, and monitoring
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Session 6. Process Automation — Student-Selected Use Case
Format: Lecture + Practice
Topics:
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Where workflows end and agents begin: decomposition, tools, memory, autonomy
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The MCP picture and agent frameworks in practice (n8n AI, custom GPTs)
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Event-driven vs schedule-driven, and when to choose each
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Human-in-the-loop for agents: approvals, escalation, kill switches
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Practice: design one agentic workflow that replaces a real manual process
Session 7. Process Automation — Student-Selected Use Case
Format: Lecture + Practice
Topics:
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What vibe coding means for a BA: AI writes the code; you own the intent and acceptance criteria
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The tool stack: Claude Code, Cursor, Copilot — matching tool to task
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Prompting for outcome and constraints, not implementation
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When to vibe-code vs when to bring engineering in
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Practice: each participant builds a small working tool for a real internal problem
Session 8. Process Automation — Student-Selected Use Case
Format: Lecture + Practice
Topics:
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Each participant presents a real process from their own work
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Live design of the solution: which sessions' techniques apply, in what order
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Post-course roadmap: what to try in week 1, month 1, quarter 1
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Course wrap-up and Q&A
Kateryna Stepantsova
Raiffeisen Bank
Business/System Analyst
I completed the comprehensive business analysis training (BABOK 3.0) "Business Analysis Essentials (Theory+Practice)" by Denys Gobov and am very satisfied with the results. The key features of the course are the large number of practical sessions in mini-groups, the lecturer's individual approach to the participants, highly professional delivery of the material, and numerous real-world examples shared by the instructor. Thank you, Denys, for a great course and an excellent presentation of the material!
FAQ
How do I pay for the course?
- Payment is made via bank transfer; the bank details and invoice will be sent to you after registration.
What happens if I miss a session?
- While missing a session is highly discouraged, we record all lessons and provide access to the recordings.
What is the language of instruction?
- The working language of instruction is Ukranian. The presentation slides are also in English.
Will I receive a certificate?
- Yes, you will receive a certificate from Art of Business Analysis confirming that you completed the training.
Who will lead the sessions and grade the homework?
- Daria Danovska, the author of this course.