Curs Artificial Intelligence (AI) Essentials for Business Analysis

Module 1 – Foundation – Business Analysis, AI & Prompt Engineering

1.1 Business Analysis Essentials

• Quick review: BABOK® v3 Standard – Best Practices for Business Analysts
• Why BABOK standard is important in AI age
• Understanding Business Solution components using TOGAF standard
• Requirements Classification: Business Requirements, Stakeholder Requirements, Functional & Non-Functional Requirements
• Documenting project requirements by using Requirements documentation templates and standards
• The BA techniques covered in this training and why they matter
• Introduction to project approaches (BABOK® Perspectives) and their practical applications for BA
  • Agile Perspective: Applying AI in Scrum, Kanban contexts
  • Business Intelligence Perspective: Using AI for analytics requirements
  • IT Perspective: Technical requirements and solution architecture
  • Business Process Management Perspective: Process optimization with AI
• How AI fits into the Business Analysis workflow: Assist, Augment, Automate

1.2 Artificial Intelligence (AI) Technology Fundamentals

• • What Business Analysts need to know: LLMs, GenAI, and how they process BA content
  • Overview and Comparing free AI tools: ChatGPT vs Claude vs Gemini vs Copilot
  • Strengths and weaknesses of each tool for Business Analysis tasks
  • Other proprietary Business Analysis Tools with AI capabilities. Examples
  • Understanding AI limitations: hallucinations, biases, confidentiality risks

1.3 Introduction to Prompt Engineering for Business Analysts

• • How to be a BA professional with AI avoiding “AI generated workslop”
  • Anatomy of an effective BA prompt: context, role, task, constraints, format
  • Prompting patterns for BA work: Persona, Few-shot, Chain-of-thought, Template
  • Converting BA tasks into AI prompts: practical formula
  • Interactive prompting: multi-turn conversations for complex analysis
  • What to do when AI does not give you the expected answer

Exercise: Tool Comparison Challenge – Same Business Analysis task across 4 AI tools. Compare outputs, evaluate quality, identify best use cases for each tool. Team discussion

Module 2 – AI-Enhanced Requirements Elicitation Techniques

Practical application of AI to gather, analyze, and document stakeholder requirements using key requirements elicitation techniques

Facilitate ideation sessions using AI prompts

• AI as virtual brainstorming partner: generating diverse ideas (BABOK 10.5 Brainstorming)
• Prompts for divergent thinking and creative problem solving
• Organizing and categorizing brainstorming outputs
• Exercise: Generate 50 solution ideas for a business problem in 10 minutes using AI prompts

Business Concept and Project Terminology analysis – AI Enhanced

• Building domain vocabularies and concept models (BABOK 10.11 Concept Modelling)
• Creating comprehensive glossaries for projects (10.23 Glossary)
• Exercise: Generate domain model and glossary from requirements

Stakeholder analysis – Artificial Intelligence (AI)-assisted

• Identifying stakeholders for projects/initiatives (BABOK 10.43 Stakeholder List, Map, or Personas)
• Creating power/interest matrices and influence maps
• Developing detailed stakeholder personas
• Exercise: Build complete stakeholder analysis from project description

Document Analysis: AI-enabled review of existing documentation

• Extracting requirements from legacy documents, emails, meeting notes using Document Analysis technique (BABOK 10.18)
• Identifying gaps, inconsistencies, and ambiguities
• Summarizing lengthy documents for stakeholder review
• Exercise: Analyze uploaded project documents and extract 20 requirements

Job Shadowing – Using AI to structure process / activity observation findings

• Converting observation notes (BABOK 10.31) into structured findings
• Identifying patterns and insights from observation data
• Creating observation guides and templates
• Exercise: Transform raw observation notes into actionable insights

Organizing BA Workshops: Artificial Intelligence (AI) for workshop planning and facilitation

• Designing workshop agendas and activities (BABOK 10.50)
• Creating facilitation guides and scripts
• Processing workshop outputs into requirements
• Exercise: Design complete workshop package (agenda, activities, materials)

Structuring Project Requirements – efficient functional decomposition using Artificial Intelligence (AI)

• Breaking down complex functions into hierarchies (BABOK 10.22 Functional Decomposition)
• Creating function hierarchies and decomposition diagrams
• Exercise: Decompose project requirements into functional hierarchy (3 levels)

Module 3 – Requirements Analysis & Modeling with AI Prompts 

This module describes best practices about how to use Artificial Intelligence (AI) to specify, model, and validate requirements with key stakeholders.

3.1 Process Analysis and Optimization with Artificial Intelligence (AI)

Understanding and Optimizing Organization Structure – Artificial Intelligence (AI) Enhanced

• Creating organization charts and reporting structures (BABOK 10.32 Organizational Modelling)
• Mapping roles and responsibilities across teams
• Exercise: Build org model showing current and proposed structure

Process Analysis and Optimization with Artificial Intelligence (AI) Prompts

• o Analyzing current state (As-Is) processes (BABOK 10.35 Process Modelling)
  o Identifying inefficiencies, bottlenecks, and improvement opportunities (BABOK 10.34 Process Analysis)
  o Designing future state (To-Be) processes
  o Creating BPMN diagrams, flowcharts, swimlane diagrams
  o Exercise: Analyze As-Is process, identify 10 improvements, create To-Be model

Modeling complex business decisions. Business Rules AI Prompts

• Extracting business rules from requirements and processes (BABOK 10.9 Business Rules Analysis, 10. Decision Modeling)
• Categorizing rules (constraints, calculations, inferences)
• Creating Decision Table aligned with Decision Modeling Notation (DMN) Standard
• Exercise: Identify and document 30 business rules from process description

3.2 AI Prompts for IT Application/System Requirements and Specifications

AI Prompts for Mobile/Web/Desktop Application UI Development

• Using AI to create UI mockups and wireframes ( BABOK 10.36 Prototyping)
• Developing prototype specifications and user navigation flows / Storyboards
• Exercise: Create detailed UI (>5 screens) user navigation flow (Storyboarding)

AI Prompts to create Detailed Specification with Uses Cases

• Writing detailed use cases with basic and exceptions flows (BABOK 10.47 Use Cases & Scenarios)
• Creating user stories in proper format with acceptance criteria
• Converting between use cases and user stories
• Exercise: Write 3 detailed use cases from requirements

System / API Interface Analysis and Specifications AI Prompts

• Identifying and documenting system interfaces (BABOK 10.24 Interface Analysis)
• Interface documentation standards and templates
• Specifying interface requirements (data, protocols, formats)
• Creating sequence diagrams showing data interfaces between systems and objects (BABOK 10.42 Sequence Diagrams)
• Exercise: Document 3 system interfaces with detailed specifications and generate sequence diagrams for interfaces

AI Prompts to identify and document Non-Functional Requirements (NFR)

• Identifying and categorizing NFRs (performance, security, usability
• Making NFRs specific and measurable (BABOK 10.30 Non-Functional Requirements Analysis)
• Exercise: Generate 20+ non-functional requirements acros NFR categories for an IT application

3.3 Data Modelling and Business Intelligence/Reporting with Artificial Intelligence (AI)

Data modeling AI Prompts

• Creating data dictionaries with attributes and definitions (BABOK 10.12 Data Dictionary & 10.15 Data Modelling)
• Developing entity-relationship diagrams (ERD) and data models
• Exercise: Build complete data model from business requirements

Business Intelligence and Report Specifications AI Prompts

• Understanding a report structure
• Types of reports
• Developing Report Specifications
• Exercise: Build complete Report Specifications from data model

Data Flow Modeling AI Prompts

• Creating DFDs showing data movement through systems (10.13 Data Flow Diagrams)
• Identifying data stores, processes, and external entities
• Exercise: Generate DFD notation from process description

3.4 Artificial Intelligence (AI) Prompts for Agile Backlog Management

Creating User Stories AI Prompts

• Creating user stories in proper format with acceptance criteria (BABOK 10.48 user Stories)
• Defining measurable acceptance criteria from requirements (BABOK 10.1 Acceptance and Evaluation Criteria)
• Converting between use cases and user stories
• Exercise: Write 10 user stories and 3 detailed acceptance criteria from user requirements and Persona

Backlog Management AI prompts

• Creating a backlog in proper format with acceptance criteria (BABOK 10.2 Backlog Management)
• Prioritizing a backlog with different prioritization techniques (BABOK 10.33 Prioritization)
• Prioritize a backlog with relative peirority or numeric WSJF aproach
• Converting between use cases and user stories
• Exercise: Generate a prioritized product Backlog with 50 user stories user from business requirements and the list of Personas. Visualize prioritization technique used

Module 4: Integration, Ethics & Best Practices using AI

• Combining multiple techniques for comprehensive analysis
• Quality assurance: Reviewing and validating AI outputs
• Ethics and governance: When to use (and not use) AI in BA work
• Data privacy and confidentiality considerations
• Building organizational prompt libraries and standards
• Creating reusable templates and frameworks
• Common pitfalls and how to avoid them

Module 5 – Course Review

5.1 Key Takeaways & Best Practices

• Review of BABOK
• Prompt engineering mastery checklist
• When to use AI (and when not to)
• Lessons learned from group presentations

5.2 Continued Learning Resources

• AI Tools & Updates – where to stay current
• BABOK and IIBA additional resources

5.3 Q&A and Closing

• Open discussion
• Training evaluation

La finalul acestui training veți fi capabili să:

• utilizați inteligența artificiala (AI) pentru a analiza de business si obtinerea cerintelor in proiecte
• realizați activitățile de business analyst mai rapid (de 3-5 x) și mai bine (cele mai bune practici)
• Proiectarea de prompt-uri AI pentru obtinerea, modelare, analiză și documentare cerintelor
• Analiza stakeholderilor, managementul cerințelor, modelarea proceselor și descrierea cerințelor funcționale cu ajutorul inteligente artificiale AI.
• înțelegeți cum funcționează instrumentele AI pentru a le aplica mai bine în proiecte reale
• înțelegeți beneficiile și evitați riscurile utilizării tehnologiei AI

• Analiști de Business (toate nivelurile de experienta);
• Product Owners și Product Managers;
• Requirements Engineers, Systems Analysts și Data Analysists;
• Solution Architects, implicați în activitatea de stabilire a soluțiilor de proiect;
• Manageri de Proiect care desfășoară activități de analiză de business;
• Coordonatori echipa și manageri care coordonează activitatea de analiză.

• Experiență practică relevantă în analiza de business (minim 6 luni);
• Familiaritate în colectarea și documentarea cerințelor proiectelor
• Acces la instrumente AI gratuite (conturi online create de participanți înainte de training);

De preferat:

• experiență de business analyst sau familiaritate cu standardul BABOK®;
• participarea anterioară la trainingul Trilex: “Business Analysis Essentials” 

Notă: Experiența în analiza și managementul cerințelor aduce un plus in înțelegerea livrabilelor de proiect realizate cu ajutorul inteligenței artificiale (AI).
Acest training nu intră în detalii referitor la tehnicile de analiza de business, ci se bazează pe cunoștințele și experiența voastră, arătându-vă cum să folosiți inteligenta artificiala AI pentru a lucra mai eficient, mai rapid și cu rezultate mai bune în proiectele reale.
Conținutul si formatul trainingului este ușor accesibil atât persoanelor din business cat si persoanelor mai tehnice