Artificial Intelligence (AI) Business Analysis Training and Placement Program

Artificial Intelligence (AI) is transforming how businesses operate—and business analysts are at the forefront of this evolution. AI is no longer a futuristic concept, it's a core enabler of innovation across industries such as finance, healthcare, retail, logistics, insurance and many more.

To remain relevant and future-ready, business analysts must adapt and acquire the skills necessary to work on AI-powered solutions. Skillcubator’s ‘Artificial Intelligence Business Analysis’ Training and Placement Program is a comprehensive, career-focused program designed to equip aspiring and/or experienced business analysts with the skills, tools, and industry exposure needed to thrive in AI-enabled environments.

This instructor-led, project-based training program offers a unique blend of business analysis and AI. Participants are trained to act as key enablers in AI solution development, facilitating requirement elicitation, defining AI use cases, interpreting data-driven insights, and supporting cross-functional AI teams. Furthermore, this program bridges the gap between traditional business analysis and modern AI technologies, empowering participants to elicit, analyze, and manage AI-related business and software requirements with precision.

From understanding machine learning models and data pipelines to collaborating with data scientists and stakeholders, this course prepares you for end-to-end involvement in AI solution development.

If you are looking to transform into a business analyst role leveraging Artificial Intelligence (AI) technology with a goal to build a rewarding career in information technology domain, this program is the best fit for you. NO PRIOR TECHNICAL BACKGROUND IS REQURIED AND NO CODING REQUIRED to transition into ‘Artificial Intelligence Business Analyst’ role.

You want to switch into Information Technology field and start your career in the field of business analysis.
You are already working in Information Technology field as either developer, tester, technical writer etc. but want to transition into a business analyst role.
You are an entry-level business analyst or lack that confidence and courage to work as a business analyst.
You want to augment your business analyst career by learning Artificial Intelligence (AI) concepts and tools.
You want to work on a more challenging project, which involves usage of AI technology.

Understand the core principles of Business Analysis, Artificial Intelligence, Machine Learning, and their business implications.
Learn how to identify, define, and prioritize AI use cases that deliver business value.
Build expertise in eliciting, analyzing, documenting solution-level requirements from AI perspective.
Gain skills in framing problem statements, documenting business/software requirements, and translating them into AI system specifications.
Acquire proficiency in tools and techniques such as business process modeling (BPMN), data flow diagrams, Unified Modeling Language (UML), Structured Query Language (SQL) etc.
Collaborate effectively with data scientists, data engineers, and stakeholders in Agile and hybrid project environments.
Navigate ethical, legal, and regulatory concerns related to AI including bias, transparency, and responsible AI practices

1.0 Business Analysis Fundamentals

What is business analysis?
Who is a business analyst?
Importance of business analyst.
Real-world business analyst use cases.
A typical day of a business analyst.

2.0 Project Management Overview

What is a project?
What is project management?
Understanding the ‘Iron Triangle’.
What are the different phases of a project?
Understanding various knowledge areas and processes within each of them.
Reviewing key project management artifacts.
Project scope v/s Product scope.
Creating ‘Project Plan’ (schedule) using Gantter tool.

3.0 Key Management Concepts

Value chain.
Lean methodology.
Kanban.
Poka-Yoke.
Core Competency.
Kaizen.
Business Process Re-engineering.
Business Process Improvement.

4.0 Understanding Stakeholder Landscape

Primary v/s Secondary stakeholder.
Active v/s Passive stakeholder.
Understanding various types of stakeholders via a live case study.
What are the different organizational structures?
Creating ‘RACI’ matrix using MS Excel.
Creating ‘Organizational Structures’ using MS Visio, Lucid Chart, Gliffy, or Visual Paradigm.

5.0 Understanding different types of requirements

What is a requirement?
Understanding different types of requirements.
Business need.
Stakeholder requirement.
Functional.
Non-Functional.
Interface.
Graphical User Interface.
Business rules.

6.0 What is Software Development Lifecycle?

Understanding the different phases of an SDLC.
Role of a business analyst in each of the SDLC phases.
Learning and comparing (a) Predictive (b) Iterative and Incremental and (c) Adaptive SDLC models.

7.0 Reviewing Different Software Engineering Models

Waterfall.
Spiral.
Rapid Application Development (RAD).
Rational Unified Process (RUP).
SCRUM (More emphasis will be given on SCRUM).

8.0 Requirements Lifecycle

Requirements elicitation.
Requirements analysis.
Requirements documentation.
Solution assessment.
Requirements management.

9.0 Requirements Elicitation

What is requirements elicitation?
Understanding various requirements elicitation techniques.
Brainstorming.
Focus Group.
Requirements Workshops.
Interviews.
Questionnaire/Survey.
Document Analysis.
Prototyping.
Pros and Cons of each of the requirements elicitation techniques.

10.0 Business Requirements Analysis

SWOT analysis technique.
RCA (Root Cause Analysis) technique.
Five whys.
Fishbone (Ishikawa).
Business Process Management.
What is Business Process Management (BPM)?
What is Business Process Modeling?
Understanding various business process modeling notations.
How to create a business process model?
Creating ‘As-Is’ (Current State) and ‘To-Be’ (Future State) business process model using MS Visio/Lucid chart/Gliffy tools.
Creating ‘Root Cause Analysis’ diagram using MS Visio/Lucid chart/Gliffy tools.

11.0 System Requirements Analysis

What is System Context Diagram?
Creating System Context Diagram using MS Visio/Lucid chart/Gliffy tools (Hands-On Exercise).
Object Oriented Concepts.
What is Unified Modeling Language (UML)?
Use Case Diagrams.
Activity Diagrams.
State Chart/State Machine Diagrams.
Sequence Diagrams.
Creating all UML models (mentioned above) using MS Visio/Lucid chart/Gliffy tools.
What is ‘Functional Requirements Document’ (FRD) and writing FRD using MS Word.
What is a Use Case Specification?
Writing a Use Case Specification Document using MS Word (Hands-On Exercise).
Creating Data Dictionary using MS Word (Hands-On Exercise).
Creating Business Rules Document using MS Excel (Hands-On Exercise).

12.00 Scrum

What is ‘Agile Software Development?
Understanding various ‘Agile Software Development’ practices/models.
What is SCRUM?
What is ‘Agile Manifesto’?
Different roles in a SCRUM.
Role of a business analyst in a SCRUM.
Understanding various ‘Ceremonies’ in a SCRUM.
Writing Features-Epics-User stories using JIRA tool (Hands-On Exercise).
Writing ‘Acceptance Criteria’ using Gherkin syntax (Given-When-Then) using JIRA tool (Hands-On Exercise).
How to estimate a user story and add user story attributes in a JIRA tool (Hands-On Exercise)?
What is team velocity?
Understanding burndown/burnup chart and how to create using MS Excel and/or JIRA tool.
How to initiate, execute and close sprints/iterations using JIRA tool using JIRA tool? (Hands-On Exercise).
Various techniques on splitting an EPIC into small user stories.
How to create wireframes/mockups using Balsamiq tool?

13.0 Structured Query Language (SQL)

What is database?
What is a table (column/row) in a database?
What is Entity Relationship Diagram (ERD)?
How to create an ERD diagram using Visio? (Hands-On Exercise).
Understanding database schema.
What is datatype?
What is SQL?
Write SQL queries (Hands-On Exercise).

14.0 Requirements Management

What is requirements management?
Understanding ‘Requirements Management Plan’ (RMP)
What is Requirements Traceability Matrix (RTM)?
Creating an RTM using DOORS/ MS Excel tool (Hands-On Exercise).

15.0 Requirements Estimation

Why to estimate requirements and importance of estimating software requirements?
Overview of different software requirements estimating models.
Understanding ‘Industry Best Practices’ to estimate a requirement.

16.0 Configuration Management

What is configuration management?
Benefits of configuration management.
Applying CM using tortoise SVN tool (Hands-On Exercise).

17.0 Change Control

What is change control board (CCB)?
Role of a business analyst in a CCB process.

18.0 Quality Assurance

What is quality assurance?
Different types of testing models.
Role of a business analyst in quality assurance/testing phase.

19.0 Miscellaneous Topics

What is Service-Oriented Architecture?
What is Cloud Computing and various cloud computing models?
What is Business Process Model and Notation (BPMN)?
Creating BPMN model using MS Visio tool (Hands-On Exercise).

1.0 AI Fundamentals

What is AI?
AI v/s Traditional Programming.
What is Machine Learning?
What is Data Science.

2.0 Understanding Data

What is data?
Structured v/s Unstructured data.
Data Lifecycle.
Data Processing.
Feature Processing.

3.0 Machine Learning (ML) Deep Dive

ML Problem Types.
Supervised vs Unsupervised vs Reinforcement Learning.
Training & testing.
Performance Evaluation.
Machine Learning strengths and limitations

4.0 Deep Learning

What is Deep Learning?
Anatomy of the Neural Network.
Deep Learning Terminologies.
Challenges.

5.0 AI Project Workflow

Machine Learning Workflow (MLOps).
Data Science Workflow.

6.0 Computer Vision

What is Computer Vision.
Image Processing.
Optical Character Recognition (OCR).
Object Detection.
Facial Recognition.

7.0 Natural Language Processing

What is NLP?
Text Processing.
Sentiment Analysis.
Chatbots.

8.0 Generative AI (GenAI) Foundation

What is GenAI?
Popular models: GPT, DALL·E, Stable Diffusion.
Defining and building GenAI system.
Prompt Engineering.
Hallucinations and misinformation risks

9.0 Large Language Model (LLM)

Pre-training.
LLM customizations-RAG and Fine-tuning.
LangChain.
Reinforcement Learning from Human Feedback (RLHF).
LLMOps.
Model size & Costing of LLMs.

10.0 AI Generated Visuals

How AI generates images (Stable Diffusion, DALL·E, Midjourney).
AI in branding & advertising (social media creatives, design automation).
AI-driven video production & animation Activity.

11.0 AI Generated Voice and Speech

Text-to-speech, voice cloning & narration.
Conversational AI.

12.0 Collaborating with AI Teams

Understanding team roles.
Common tools and platforms.
Establishing effective workflow

13.0 AI Workflow Automation with Zapier

Automating Repetitive Business Tasks.
AI & No-Code Automation Tools.
Optimizing AI workflows.

14.0 AI Ethics, Bias and Compliance

Addressing bias in AI models.
Ethical considerations in AI projects.
AI compliance & regulatory frameworks.
AI security risks.

15.0 End-to-End Case Study (any 3)

Sales and Marketing.
Customer Service.
Data Analytics.
Healthcare.
Finance.

Final Project Practical problem-solving of a real-world AI business challenge from inception to completion.

Training Program as per Latest Industry Demand

IIBA Endorsed Education Provider

Access to Learning Management System (LMS)

Free PSM-I and PSPO-I training included in the package

40 PDUs/CDUs

IIBA Certified Instructors with 20 plus years of experience

Plenty of case studies, In-Class exercises, quizzes, and take-home assignments
10 Plus Industry-Standard tools
Personalized Resume, LinkedIn Profile makeover and Cover Letter
Course aligned to IIBA’s BABOK 3.0 and PMI’s body of knowledge
Comprehensive Capstone project
Experiential learning through case studies

Business Analysis Software

Microsoft Office (Word, Excel, PowerPoint).
Microsoft Visio, Gliffy, Lucidchart.
Bizagi, Camunda, Bonita, Signavio, Draw.io.
Microsoft Project, Product Plan, Gantter.
Balsamiq, Mockflow.
Jira.
Confluence.
Tortoise SVN.
Oracle.
Kanbantool, backlog.com, or similar Kanban tool.

Artificial Intelligence Software

Deep Learning

TensorFlow Playground.

Computer Vision

Google Vision API.

NLP

MonkeyLearn.
AI Chatbots.
Claude AI.
Perplexity.
Copilot.
ChatGPT.

LLM Tuning

OpenAI Playground.

AI Workflow Automation

Zapier.

Conversational AI

DiagFlow.
Rasa.

Data Analysis

Tableau.
Julius AI.

No-Code ML Models

KNIME.
Akkio.

This program is designed to equip aspiring business analysts with the skills and tools needed to work on AI-driven software projects. It combines conventional business analysis practices with emerging AI technologies, helping participants bridge the gap between business needs and artificial intelligence and machine learning technologies.

Absolutely No. This program is structured for non-technical and semi-technical audiences. Concepts are explained in business-friendly terms with real-world examples, which can be applied in real-world scenarios. Also, there is NO CODING involved to become an Artificial Intelligence Business Analyst.

The future landscape for Artificial Intelligence (AI) professionals is exceptionally promising, marked by rapid industry growth, escalating demand across sectors, and significant salary prospects. Below is an overview of the anticipated scope for AI professionals:

Explosive Industry Growth

Market Expansion: The global AI market is projected to reach $1.81 trillion by 2030, growing at a compound annual growth rate (CAGR) of approximately 36% from 2024 to 2030.

Economic Impact: AI is expected to contribute $15.7 trillion to the global economy by 2030, underscoring its transformative potential across industries.

Surging Demand Across Industries

Diverse Applications: AI professionals are increasingly in demand in sectors such as:

Healthcare (predictive diagnostics, medical imaging, drug discovery).
Finance (fraud detection, algorithmic trading, customer personalization).
Retail & eCommerce (recommendation engines, demand forecasting).
Manufacturing (predictive maintenance, automation).
Transportation (self-driving tech, route optimization).
Education, HR, Legal, and Cybersecurity.

Lucrative Salary Prospects

Competitive Compensation: AI roles command substantial salaries. For example, experienced AI professional can earn between $170,000 and $200,000, with total compensation potentially exceeding $250,000 annually at top tech companies.

Wage Premiums: AI-skilled workers see an average 56% wage premium, a significant increase from the previous year’s 25%, indicating the high value placed on AI expertise.

Diverse and Evolving Career Paths

Expanding Roles: The AI field offers a variety of career opportunities, including roles such as AI Business Analyst, Data Scientist, AI Product Manager, and NLP Engineer.

Leadership Opportunities: There is a notable increase in demand for AI leadership positions, with a 40% to 60% growth in such roles, reflecting the strategic importance of AI in organizational decision-making.

Professionals can grow into roles like:

Machine Learning Engineer.
AI Business Analyst (Non-Technical).
Data Scientist.
Data Analyst.
AI Product Manager (Non-Technical).
AI Research Scientist.
Robotics Engineer.
NLP Engineer.
Computer Vision Specialist.

Truly speaking, there are no prerequisites to become a successful Artificial Intelligence (AI) business analyst. But it would be nice to have the following to become a successful business analyst:

Basic computing skills (MS Office like Word, Excel and PowerPoint).
Decent communication skills (verbal and writing in English).
Comfortable talking to people.
Normal analytical skills.

If you have these three things, we guarantee to transform you into an AI business analyst.

This program is ideal for:

Aspiring individuals, seeking to start their career as a Business Analysts in AI domain.
Business Analysts seeking to specialize in AI projects.
IT professionals who are looking to transition into AI business roles.
Data Analysts and Project Managers wanting to expand into AI systems.
Non-technical professionals looking to understand AI from a business perspective.

The topics covered in this training are based on business analysis domain and artificial intelligence domain. For more details, please see ‘Course Outline sections’.

Business Analysis:

View Business Analysis course outline

Artificial Intelligence:

View Artificial Intelligence course outline

Yes. The program includes case studies, live projects, and practical assignments simulating real-world AI implementation from a business analyst’s perspective.

The training runs for 10 to 12 weeks, with flexible weekend and evening schedules to suit working professionals.

Yes. Skillcubator offers comprehensive placement support, including:

Professional resume preparation
Cover Letters.
Unlimited mock interviews.
LinkedIn optimization.
Resume marketing through our in-house recruiters and partner network.
Background check support.
On-the-project mentoring, coaching and guidance.

Yes. Skillcubator Training Institute is a ‘Premium-Level Endorsed Education Provider (EPP) from IIBA.org. After successfully completing the training program, participants will receive a ‘Certificate of Completion’ from Skillcubator with an IIBA seal on it, which can be shared on LinkedIn and included in resumes.

Please visit the course page to know more about the fee structure and enrollment options. Alternatively, you can contact Skillcubator directly through www.skillcubator.com for detailed pricing, enrollment assistance, and upcoming batch information.

Please click on the below URL to learn more about the tools/software used for this training:

View Software / Tools used for this training

No. There is no additional cost to install and use the tools/software that will be used for this training.

This course will be taught by certified professionals in business analysis and artificial intelligence/machine learning domain with 15 plus years of corporate experience

1500 USD +5.3% Sales Tax

COURSE DELIVERY OPTION

Live Online.
In-Person.
Private Team Training.

PREREQUISITES

Truly speaking, there are no prerequisites to become a successful Artificial Intelligence (AI) business analyst. But it would be nice to have the following to become a successful business analyst: