SB
Spandana Bala
Business Analyst
Hyderabad · India
Now at · Microsoft
Class 014 · BUSINESS ANALYST & AI AGENTS · GENAI-NATIVE BA
Business Analyst
+ AI
Agents
Master end-to-end Business Analysis with Agentic AI. Lead stakeholder discovery with Power/Interest grids and RACI, ship BRD / FDD / RTM documentation, build Power BI dashboards for insights, and deploy a BA Coding Agent that drafts requirements and acceptance criteria.
3mo
duration
30+
modules
4.7/5
class rating
100k+
enrolled
Where our Business Analyst alumni work
Microsoft
Amazon
SalesforceAI Engineer
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every BA grad walks away with.
01
Agent-Ready BA skills
Complete BA fluency — BRD, FDD, BPMN, MoSCoW, RACI, RTM — plus Testing for BA, Power BI dashboards, and an LLM requirements layer with LangGraph, Claude Agent SDK, and MCP.
02
A shipped project
A production-deployed BA Coding Agent that drafts BRDs from interview transcripts, generates acceptance criteria, and builds RTMs via MCP into Jira and Confluence — with a public verification URL.
03
Verifiable credential
2026 Agent-Ready rubric mapped to IIBA ECBA, CCBA, CBAP, AAC, plus PL-300 and Scrum.org PSPO I, graded 1–5, with a public verification URL recruiters can check in 30 seconds.
04
Direct placement pipeline
GitHub + LinkedIn portfolio rewrite, BA-tuned resume rebuild, and warm intros into our 1,000+ hiring partners actively staffing Business Analyst, Product Owner, and AI BA roles.
5 MONTHS · FOUR PHASES · ONE BA AGENT
From “writes user stories” to ships AI-augmented business solutions..
Weeks 1–2 · Foundations
IT & AI Foundations + BA Mindset
- Application lifecycle, Agile/Scrum, and cloud computing models
- Introduction to AI, ML, Generative AI, and Agentic AI
- Modern BA role bridging business needs and technical solutions
- BA toolchain — Azure Boards, JIRA, Confluence, Google Sheets
YOU SHIPA configured BA toolchain — Azure Boards, Jira, Confluence — plus your first stakeholder analysis for the banking reference programme.
Weeks 3–10 · Business Analysis Core
Stakeholder Collaboration → Requirements → Process → Documentation
- Stakeholder analysis, RACI, personas, and empathy mapping
- Eliciting and prioritising with MoSCoW, WSJF, and RICE
- BPMN, SIPOC, and Value Stream Mapping for As-Is/To-Be design
- BRD, FDD, NFRs, User Story Mapping, and RTM documentation
YOU SHIPA complete BA artefact pack — stakeholder report, RACI, BPMN As-Is/To-Be, BRD, FDD, NFRs, User Story Map, and RTM.
Weeks 11–14 · Testing for BA + Power BI
Testing for BA + Power BI for Data-Driven BAs
- Testing levels — Unit, Integration, System, UAT for BAs
- Test design techniques — ECP, BVA, Decision Tables, State Transition
- UAT enablement, defect management, and RTM linking
- Power BI with Power Query, DAX, and Row-Level Security
YOU SHIPA UAT enablement package — test plans, defect logs, RTM linking — plus a Power BI dashboard suite with DAX and star-schema modelling.
Weeks 15–20 · GenAI + Agentic AI
Master the 2026 GenAI + Agentic AI stack — and ship a BA Coding Agent that drafts BRDs from interviews, generates acceptance criteria, and builds RTMs autonomously.
Engineer with LLM APIs from OpenAI, Anthropic, Google GenAI, and DeepSeek. Master prompt engineering (zero-shot, few-shot, CoT, ReAct) and context engineering — the 2026 frontier discipline. Build production RAG pipelines with ChromaDB and pgvector over your requirements repos, stakeholder interviews, and process documentation. Master the 2026 production agent stack — LangGraph 1.0 (#1 production default), Claude Agent SDK (#2 MCP-native), CrewAI (#3 multi-agent crews). Wire it all through the Model Context Protocol (MCP) — 200+ server implementations, 97M+ monthly SDK downloads. Final project — a deployed BA Coding Agent with MCP servers exposing Jira/Azure Boards, Confluence, and your requirements library. The BA’s force multiplier.
Partner orgs
(2026)52
Projects
deployed310+
→ Placement
offers87%
Course curriculum
Seven sections. 65+ modules. The AI-native Business Analyst stack.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock for every modern Business
Analyst — application lifecycle, Agile/Scrum, computing infrastructure, AI/ML/Generative/Agentic AI
fundamentals, and real-world digital systems. Sets the context for everything that follows in the BA
+ AI engineering stack.
Application fundamentals — what applications are, their types, web architecture
Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python,
Java, Node.js)
Database Systems — SQL (PostgreSQL, MySQL) and NoSQL (MongoDB)
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
Where the BA fits in every SDLC phase — not just at the start
The modern BA contributes throughout the SDLC — discovery in Planning,
requirements in Analysis, validation in Design, support in Implementation, UAT in Testing
Methodology Evolution — Waterfall vs Agile, the Agile mindset
Popular frameworks — Scrum, Kanban, Extreme Programming (XP), hybrid approaches
Scrum Roles — Product Owner, Scrum Master, Development Team
Scrum Events — Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective
Scrum Artifacts — Product Backlog, Sprint Backlog, Increment deliverables
User Stories — Epics, Themes, Acceptance Criteria — the BA's primary output
Estimating user stories with story points or t-shirt sizing
Backlog management with Google Sheets and Azure Boards
In Agile, the BA is often the Product Owner's right hand — translating business
intent into user stories the development team can actually build
CPU Technology — general-purpose computing, sequential operations, multi-core
parallel processing
GPU Technology — parallel processing for AI training, data processing
IaaS — Infrastructure as a Service — what your applications run on
PaaS — Platform as a Service — what your developers deploy to
SaaS — Software as a Service — what your users actually interact with
Understanding the cloud is essential — most modern BAs work on applications
deployed across these models, and you need the vocabulary to write meaningful NFRs
AI is reshaping BA practice in 2026 — from AI-generated requirements to
autonomous BRD drafting to RAG-powered stakeholder intelligence
Machine Learning — algorithms that improve through experience
Deep Learning — neural networks for complex pattern recognition
Generative AI — systems that generate requirements, test cases, process diagrams
Large Language Models — LLMs that draft BRDs, generate acceptance criteria,
summarise stakeholder interviews
Agentic AI — autonomous systems that plan, reason, act, and learn — the future of
BA practice
Customer Relationship Management — CRM platforms (Salesforce, Dynamics, HubSpot)
— the most common BA project domain
Human Resource Management Systems — HRMS implementations (Workday, SAP
SuccessFactors) — sensitive data, regulatory complexity
Retail & E-Commerce — omnichannel commerce — payments, inventory, supply chain BA
work
Healthcare Applications — regulated workloads — HIPAA in US, DPDP in India,
patient data handling
Industry domain knowledge is the BA's superpower — generalist BAs do generic
work; specialist BAs in BFSI/healthcare/retail command 2x salaries
02
Foundations of Business Analysis in a Digital World
The heart of BA fluency — six modules covering the entire discipline of modern
Business Analysis. The modern Business Analyst operates at the intersection of technology, business
strategy, and stakeholder collaboration. This role demands mastery of agile methodologies,
requirement elicitation techniques, and emerging AI tools. By the end of this section, you can lead
the BA workstream of any digital transformation programme.
Bridge business needs with technical solutions through analysis and facilitation
The four BA conversations — Why? What? How? When?
BA vs Business Systems Analyst vs Product Owner vs Functional Consultant
Career pathways — IIBA-aligned progression
Embrace iterative development and continuous stakeholder collaboration
Value working software over comprehensive documentation (without losing rigour)
Respond to change over following a plan
Master Scrum roles, events, and artifacts for effective sprint delivery
BA's contributions in each Scrum event and working with the Product Owner
User Stories — capture requirements with epics, themes, and acceptance criteria
The "As a [user], I want [capability], so that [benefit]" pattern
INVEST criteria — Independent, Negotiable, Valuable, Estimable, Small, Testable
Definition of Ready vs Definition of Done
Google Sheets, Microsoft Excel — backlog management, traceability
Azure Boards, Jira, Confluence — enterprise standard
Lucidchart, draw.io, Visio — process diagramming
GenAI tools for requirement management — Microsoft Copilot, Claude, ChatGPT
ALM understanding — application lifecycle from conception to retirement, where BA
work fits, hand-offs to development, testing, operations
Discovery phase — explore the problem space and identify opportunities through
structured investigation
Distinguish problems from symptoms and frame the right business question before
chasing solutions
Power/Interest Grid — map stakeholders by their influence and interest levels
Influence/Impact Matrix — assess each stakeholder's capacity to affect outcomes
RACI Matrix — define clear accountability (Responsible, Accountable, Consulted,
Informed) for each deliverable
Stakeholder communication strategies for each quadrant
User Personas — create detailed profiles representing key user segments and their
goals
Empathy Mapping — capture what stakeholders think, feel, say, and do
Reveal unspoken needs and pain points that drive better solutions
Persona-driven requirement validation
Discovery interviews to understand context
Contextual inquiry — observing users in their environment
Stand-up conversations for quick clarifications
Active listening techniques and bias avoidance
Workshop facilitation — lead collaborative sessions that generate consensus,
prioritise features, and validate requirements
Working with cross-functional teams
Workshop preparation and outcome documentation
GenAI for Stakeholder Management — analyse stakeholder feedback, generate
interview questions, and synthesise insights from multiple sources
MoSCoW Method — Must have, Should have, Could have, Won't have (this time)
Stakeholder alignment through clear categorisation
WSJF — Weighted Shortest Job First — balances business value, time criticality,
and risk reduction against job size
The SAFe-recommended prioritisation framework for Agile teams
Calculating WSJF scores for backlog items
RICE Scoring — evaluate features using Reach, Impact, Confidence, and Effort
Data-driven prioritisation favoured by product teams
When to use RICE vs WSJF vs MoSCoW
Traceability — link requirements to business objectives, test cases, and
deliverables ensuring complete coverage and impact analysis
Versioning — track requirement changes over time with clear version control and
change history
Validation — confirm requirements are complete, consistent, testable, and aligned
with stakeholder expectations
Alignment with OKRs — ensure every requirement contributes to measurable
Objectives and Key Results
GenAI for Requirements — analyse requirement patterns, identify gaps, suggest
acceptance criteria, and generate traceability matrices automatically
Prioritisation is the most political activity in BA work — let the framework
defend your recommendations rather than your opinion
BPMN (Business Process Model and Notation) — create standardised process diagrams
Activities, events, gateways, swim-lanes
BPMN 2.0 notation conventions
Sub-processes and call activities
As-Is process mapping — document current state processes to establish baseline
understanding
Capture the messy reality, not the idealised version
Identify pain points, waiting times, handoffs
SIPOC Analysis — define Suppliers, Inputs, Process, Outputs, and Customers for
clarity
High-level view that aligns stakeholders before diving into detail
Value Stream Mapping — visualise material and information flow to identify waste
and bottlenecks
Lean principles applied to information work
Calculate value-added time vs total lead time
Identify the eight wastes (DOWNTIME)
To-Be Design — create optimised future state with automation and efficiency
improvements
Where to apply RPA, workflow automation, AI
Customer Journey Mapping — visualise end-to-end customer experience across
touchpoints
Identify pain points and opportunities at each interaction stage
Cross-channel journey orchestration
Identify repetitive, rule-based tasks suitable for automation using RPA, workflow
engines, or AI-powered solutions
Build the business case for automation
Hands-on — create As-Is BPMN diagrams, design To-Be processes with automation,
conduct Value Stream Mapping workshops, and identify five automation opportunities in real
scenarios
BRD (Business Requirements Document) — captures high-level business objectives
and success criteria
FDD (Functional Design Document) — details system behaviour, features, and
technical specifications
Integration Specifications — define API requirements and system integration
scenarios
NFRs (Non-Functional Requirements) — specify performance, security, scalability,
and reliability requirements
User Story Mapping — transform user stories into detailed functional requirements
with clear acceptance criteria and edge cases
Data Modelling — design entity relationships, schema structures, and data flow
diagrams that support functional needs
Traceability Matrix (RTM) — link requirements to objectives, test cases, and
deliverables ensuring complete coverage
Review & Sign-Off — facilitate stakeholder reviews and obtain formal approval
before development begins
GenAI for Documentation — accelerate requirement writing with AI-generated
templates, acceptance criteria suggestions, and automated traceability matrix creation
The BRD answers "What and Why", the FDD answers "How", the RTM ensures you
haven't lost anything between the two
Wireframing — low-fidelity layouts showing structure and content placement
Wireframing tools — Balsamiq, Figma, draw.io
When to use wireframes — early validation, stakeholder alignment
Black-and-white intentionally — focus on structure not styling
Mockups — high-fidelity designs with colours, typography, and branding
Mockup tools — Figma, Adobe XD, Sketch
Working with designers — the BA's role in design reviews
Brand guidelines alignment
Prototypes — interactive models for usability testing and stakeholder validation
Prototype tools — Figma prototypes, InVision, Marvel
When prototyping pays back — high-ambiguity features, novel workflows
Conducting usability tests with prototypes
The BA who can wireframe is twice as effective in workshops — sketches resolve
ambiguity faster than any paragraph
03
Testing for Business Analyst
The skill that separates good BAs from great ones — knowing how the requirements
you wrote will actually be tested. Four modules covering testing levels, functional and
non-functional types, test design techniques, and the BA's central role in UAT enablement. By the
end of this section, you can write acceptance criteria that testers love, facilitate flawless UAT
cycles, and triage defects with severity/priority discipline.
Unit Testing — test individual components using frameworks like JUnit, NUnit,
pytest
Integration Testing — verify component interactions using incremental or big bang
approaches
System Testing — validate complete system functionality and non-functional
requirements
UAT (User Acceptance Testing) — business users validate system meets requirements
through alpha and beta testing
GUI Testing — visual design verification, functional elements testing, content
and interaction validation
Database Testing — CRUD operations validation, data integrity testing, SQL basics
for testers
Object Properties Testing — verifying UI element states, attributes, and
behaviours
Error Handling Validation — validating error messages, computation accuracy, edge
cases
Calculations and Computations — financial calculations, totals, tax, discounts
validation
Links Testing — internal, external, anchor, email links
Cookie and Session Testing — session management, persistence, expiry
Usability Testing — assess user-friendliness, measure satisfaction metrics,
ensure intuitive navigation
The BA's role in usability validation
As a BA, your acceptance criteria are what testers turn into test cases — write
them with testing in mind
Load Testing — verify system behaviour under expected user loads
Stress Testing — test system limits and breaking points
Spike Testing — validate response to sudden traffic increases
Endurance Testing — assess stability over extended periods
Volume Testing — test with large data volumes
Scalability Testing — verify system can scale to meet demand
Security Testing — validate authentication, authorisation, and encryption
mechanisms protect against vulnerabilities
Penetration testing concepts for BAs
Compliance testing — DPDP, GDPR, HIPAA
Compatibility Testing — verify functionality across different hardware, operating
systems, and browsers
Recovery Testing — assess system's ability to recover from crashes, hardware
failures, and other disasters
Installation Testing — test fresh installation, upgrades, uninstall, and
reinstall scenarios
Regression Testing — verify existing functionality after changes (unit, regional,
full)
Smoke & Sanity Testing — build verification and focused testing of specific
functionality
Exploratory Testing — unscripted testing to discover unexpected issues
NFRs are where BA careers are made or broken — vague NFRs cause production
incidents that get traced back to the BA
Equivalence Class Partitioning — divide inputs into valid and invalid classes,
testing one value from each partition
Boundary Value Analysis — test values at boundaries and just beyond to catch edge
case defects
Decision Table Testing — map conditions, actions, and rules for complex business
logic scenarios
State Transition Testing — model states, events, and transitions using diagrams
and tables
Error Guessing — leverage experience to predict likely defect areas
Test Plan — define scope, approach, resources, schedule, and deliverables for
testing activities
Use Cases & Scenarios — document actor interactions, actions, and goals that
inform test case creation
Test Cases — detailed steps, test data, expected results, and traceability to
requirements
RTM & Defect Tracking — Requirements Traceability Matrix and bug life cycle
management
If you can write acceptance criteria using these five techniques, you'll write
the tightest user stories your team has ever seen
Test Scenarios — transform requirements into comprehensive test scenarios
covering happy paths and edge cases
Scenario → Test Case → Test Step hierarchy
UAT Planning — coordinate user acceptance testing with stakeholders,
environments, and test data
UAT participant selection — representative sample of real users
UAT environment vs Production parity considerations
Test data refresh and masking for UAT
UAT Scripts — create detailed step-by-step testing instructions with expected
results
Script formats that real business users can follow
Recording actual results for sign-off documentation
Facilitating UAT sessions — guide business users through testing, answer
questions in real-time, document feedback
The BA as UAT moderator
Defect Management — log, prioritise, and track defects through resolution
Participate in triage meetings to assess severity and impact
Severity vs Priority — the BA must master both axes
Defect lifecycle — New → Assigned → Open → Fixed → Retest → Verified → Closed
Acceptance Criteria Validation — verify delivered functionality meets documented
criteria and business expectations before sign-off
Formal vs informal sign-off processes
Maintain test plans, scripts, results, and traceability matrices linking tests to
requirements
UAT sign-off documentation for audit trails
GenAI for Testing — automatically generate test cases from requirements, create
test data sets, produce comprehensive testing documentation
UAT is where the BA's reputation crystallises — a well-run UAT cycle is your
single best career investment
04
Data Analysis with Power BI
The data-driven BA's superpower. Power BI provides a comprehensive platform for
connecting to data sources, creating interactive visualisations, and sharing insights across
organisations through intuitive dashboards and reports. Six modules transforming you from a
documents-only BA into a data-backed BA whose recommendations come with evidence.
Modern analytics approaches that turn data into competitive advantage
Descriptive vs Diagnostic vs Predictive vs Prescriptive analytics
The BA's role in translating data insights into business action
Power BI Desktop — authoring environment
Power BI Service — cloud platform for sharing
Power BI Mobile — consumption on the go
Power BI Gateway — on-premises data connectivity
Master the workspace and create your first report
Desktop vs Service capabilities — what each is best for
File sources — Excel, CSV, JSON
Database connections — SQL Server, Oracle, PostgreSQL, MySQL
Cloud services — Azure, AWS, Google Cloud
Web sources and APIs — REST endpoints, OData feeds
Import mode — data copied into Power BI
DirectQuery — live connection to source
Live Connection — Analysis Services models
Performance considerations — Import mode offers best performance for most
scenarios; use DirectQuery for real-time data or when data volumes exceed Power BI limits
Credential management and security
Refresh scheduling
Gateway configuration for on-premises sources
Power Query Editor — M language fundamentals
Step-by-step transformation patterns
Applied steps and reproducibility
Column profiling, distribution, quality indicators
Identifying outliers and data quality issues
Statistical previews
Removing duplicates
Handling nulls and errors
Data type conversions
Standardising text and dates
Pivoting and unpivoting
Merge queries (joins)
Append queries (unions)
Group By operations
Star Schema Design — fact tables vs dimension tables
Why star schemas outperform flat tables
Conformed dimensions across multiple facts
One-to-many, many-to-many relationships
Active vs inactive relationships
Bi-directional filtering — when to use, when to avoid
Cross-filter direction
Date hierarchies (Year → Quarter → Month → Day)
Geographic hierarchies (Country → Region → City)
Organisational hierarchies (Division → Team → Individual)
Chart selection — bar, line, scatter, pie, treemap, waterfall
Core visualisations — charts, tables, maps, KPIs
Interactive elements — slicers, filters, bookmarks
Drill-through navigation
Dashboard layout and mobile optimisation
Data storytelling techniques
DAX syntax and structure
Calculated columns vs measures
Aggregation functions — SUM, AVERAGE, COUNT, MIN, MAX, DISTINCTCOUNT
Logical functions — IF, SWITCH, AND, OR
Text functions — CONCATENATE, FORMAT, UPPER, LOWER
Date/time functions — TODAY, YEAR, MONTH, DAY
CALCULATE and FILTER functions — the heart of DAX
Creating KPIs aligned with OKRs from Section 2
Conditional formatting for status visualisation
Target lines and benchmark indicators
Year-over-year, quarter-over-quarter comparisons
Year-to-date, quarter-to-date, month-to-date
Period-over-period growth calculations
Custom calendar handling
Iterator functions — SUMX, AVERAGEX, COUNTX
Variables in DAX for performance and readability
Time intelligence patterns
ALL, ALLEXCEPT, REMOVEFILTERS for filter manipulation
AI visuals — Q&A, Key Influencers, Decomposition Tree
Smart Narrative for auto-generated insights
Anomaly detection in time series
Custom visuals from AppSource marketplace
Importing and validating custom visuals
When to use custom vs built-in visuals
Workspaces — types and roles
Apps for distribution
Sharing and access management
Subscriptions and alerts
Row-Level Security (RLS) — securing data at the row level
Dynamic security with USERPRINCIPALNAME()
Sensitivity labels and information protection
Premium capacity considerations
Query optimisation patterns and DAX performance tuning
Composite models and aggregations
Foundation for the Microsoft Certified: Power BI Data Analyst Associate (PL-300)
certification
05
Generative AI & Agentic AI
The production AI engineering destination — and where this programme
distinguishes itself from every other Indian BA course. From the 70-year arc of AI history to
deploying a production BA Coding Agent — this section builds the complete 2026 GenAI engineering
stack tuned for BA work: frontier models, prompt engineering, RAG, agent frameworks, and the Model
Context Protocol. The named BA Coding Agent project lives here.
Narrow AI — image classifiers, speech recognition — the pre-2022 era
Generative AI — LLMs, image/video/audio generation — the post-2022 era unleashed
by ChatGPT
Agentic AI — Plan / Reason / Act / Learn loops, tool use — the post-2024 era of
autonomous systems
2022 inflection point — ChatGPT launch enters mainstream consciousness
2024 inflection point — AI systems begin planning, using tools, completing
multi-step tasks autonomously — including BA tasks
What's coming 2026-2030 — increasingly capable reasoning models, deeper tool
integration with BA systems (Jira, Confluence, Azure DevOps)
Multi-agent collaboration at scale and systems that learn continuously from
production stakeholder feedback
For BAs, the agentic era means agents that can draft BRDs, summarise stakeholder
interviews, and propose acceptance criteria — with you reviewing and refining
GPT-5.5 — The Autonomous Agent. Terminal-Bench 2.0 leader at 82.7%
GPT-5.5 best for autonomous BA agents that execute multi-step research and
analysis
Claude Opus 4.7 — The Precision Writer. SWE-bench Pro leader at 64.3%, lowest
hallucination rate at 36%
Claude Opus 4.7 — deepest native MCP support of any frontier model, critical for
BA tool integration
Claude Opus 4.7 best for long-form BRD drafting and stakeholder analysis
Gemini 3.1 Pro — The Context Giant with 2M+ token context window large enough to
ingest entire requirements repositories and historical project documentation
Open-source frontier — Llama 4 (Meta), DeepSeek, Mistral, Qwen — when stakeholder
data must stay in your own VPC
Intelligent Routing for BAs — Opus 4.7 as daily driver for BRD drafting and
stakeholder analysis
GPT-5.5 for autonomous research and competitive analysis
Gemini 3.1 Pro for ingesting massive RFP documents and legacy specifications
Copilot in Word for BRD and FDD drafting
Copilot in Excel for RTM generation and analysis
Copilot in PowerPoint for stakeholder presentations
Copilot in Teams for meeting summaries and action items
Copilot Studio for building custom BA agents
Perplexity — citation-grounded research for industry analysis
NotebookLM — long-document analysis for RFPs and contracts
ChatGPT Codex — agentic environment for technical BA tasks
Fundamentals — Context + Task + Examples + Format + Constraints
Core Techniques — Zero-shot, few-shot, Chain-of-Thought (CoT), ReAct,
Tree-of-Thought
System Prompts — persistent persona design, guardrails, extended thinking
Multimodal — reading hand-drawn process diagrams and whiteboard photos
Hallucination & Context — grounding for accurate requirement generation
Domain & Library — BA-specific prompt patterns + versioned prompt library
Context Engineering — managing what enters the LLM's context window — for BAs
this means feeding the right user stories, business rules, and historical requirements at the
right moments
Project — ship a 30+ prompt library for BA work on GitHub (BRD generation,
acceptance criteria, stakeholder interview synthesis, etc.)
Using ChatGPT, Claude, and Gemini for daily BA work
AI for BA documentation — BRDs, FDDs, RTMs, meeting notes
Research with Perplexity for industry analysis and benchmarking
Microsoft Copilot integration — Word, Excel, PowerPoint, Outlook, Teams
AI for stakeholder communication — drafting emails, summaries, status reports
Building BA-specific AI workflows that save 15+ hours per week
Reading whiteboard photos and hand-drawn process diagrams
Analysing stakeholder interview videos and extracting structured insights
OCR for legacy requirements documents
Image generation for BA training materials and process diagrams
Audio — automatic transcription of stakeholder interviews with Whisper
The "I'll send you a photo of the whiteboard" workflow is transformed —
multimodal LLMs now generate BPMN diagrams from sketches
Hallucination — when an LLM hallucinates a stakeholder requirement that doesn't
exist
Prompt injection — when an attacker poisons your AI's context through documents
Privacy — keeping confidential business strategy out of public LLMs
Security — secrets management when AI tools have access to requirements repos
Regulatory landscape — EU AI Act, India DPDP Act, sector-specific (BFSI,
healthcare)
Validating AI-generated requirements — the BA's new responsibility
In 2026, BAs increasingly validate AI-generated content — the skill of grading AI
output is becoming as core as the skill of writing requirements
Streamlit — rapid prototyping for internal BA dashboards
FastAPI — production-grade Python API for AI BA services
Building chatbots for requirement repository search
Building diagnostic agents for stakeholder analysis
Build and deploy a Streamlit + FastAPI internal tool that answers BA questions
from your team's documentation
LLM APIs in production — OpenAI, Anthropic, Google GenAI, DeepSeek Python SDKs
API patterns — completions, chat, streaming, function calling, structured outputs
Rate limits, retries, exponential backoff
Cost tracking for AI API spend
Function calling & structured outputs — the 2026 production pattern for reliable
JSON
Pydantic-validated structured outputs — type-safe AI
Use cases — extracting structured requirements from interview transcripts,
generating valid BRD sections
Embeddings — OpenAI text-embedding-3-large, Voyage, Cohere models
Vector databases — ChromaDB (local/dev), Pinecone (managed), Qdrant
(open-source), pgvector (PostgreSQL)
Indexing strategies — HNSW, IVF
RAG pipeline — Chunk → Embed → Index → Retrieve → Augment → Generate
Building RAG over your requirements repositories, stakeholder interviews, BRDs,
and historical projects
Hybrid search (BM25 + embeddings) for technical BA documentation
Re-ranking with cross-encoders
Agentic RAG — self-improving retrieval where the agent decides if it has enough
BA context
Multi-step retrieval — first find similar past projects, then specific BRDs, then
acceptance criteria patterns
Project — Internal BA RAG App: RAG over your team's BRDs, FDDs, and stakeholder
interview library; deployed with hybrid search and re-ranking; ChatOps integration with Microsoft
Teams
LangGraph 1.0 — complex stateful workflows, graph-based state machines,
human-in-the-loop, LangSmith observability — the production default for agentic BA work
Claude Agent SDK — powers Claude Code, deepest MCP integration critical for BA
tool calls into Jira, Confluence, Azure DevOps; extended thinking for complex stakeholder analysis
CrewAI — role-based multi-agent crews, fastest prototyping; use case: a "BA team"
of agents (Discovery Lead, Requirements Writer, RTM Builder, Reviewer)
Semantic Kernel / Microsoft Agent Framework — enterprise .NET stacks
Pydantic AI — type-safe Python, validation-first agent design
ReAct (Reasoning + Acting) — investigate a stakeholder concern, then propose a
requirement update
Plan-and-Execute — generate a multi-step discovery plan and execute it
Reflection loops — agent reviews its own draft BRD before producing the final
version
Multi-agent collaboration — Discovery agent gathers, Writer agent drafts,
Reviewer agent critiques
Human-in-the-loop checkpoints — humans approve every stakeholder-facing artefact
Production BA agents are 90% about state management, observability, and human
approval gates — the LLM is the easy part
MCP — the open standard for connecting agents to tools, data, and systems
Proposed by Anthropic in late 2024, now stewarded by the Linux Foundation
200+ server implementations and 97M+ monthly SDK downloads
Adoption across Anthropic, OpenAI, Google, Microsoft, AWS, and 50+ partners
For BAs — MCP servers exist for Jira, Azure DevOps, Confluence, SharePoint,
Microsoft Teams, GitHub, GitLab, and dozens more
Build an MCP server exposing Jira/Azure Boards for safe ticket creation and
updates
Build an MCP server exposing Confluence for documentation retrieval
Build an MCP server exposing your requirements library with permission-aware
authorisation
Connect LangGraph agents to multiple MCP servers via adapters
Use Claude Agent SDK's deepest native MCP integration
A2A Protocol — Google-led agent-to-agent communication standard with 50+ launch
partners
Linux Foundation governance
Three state management levels — session-level, agent-level, task-level
BA CODING AGENT CAPSTONE — multi-agent BA Coding Agent using LangGraph + Claude
Agent SDK with MCP servers exposing your Jira/Azure Boards, Confluence, and requirements library
Agent drafts BRDs from interview transcripts, generates acceptance criteria from
user stories, builds RTMs automatically, and proposes process improvements
Frontend with React or Streamlit, backend with FastAPI, observability via
LangSmith — human approval gates for all stakeholder-facing artefacts — the named project for the
entire Business Analyst & AI Agents programme
Tools you'll master
32+ BA & AI copilot tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project · weeks 3–12
Enterprise requirements + BPMN suite with AI copilot
Run a full enterprise BA engagement — discover stakeholder needs, model current and future state in BPMN, generate user stories with an AI copilot, and ship the Jira + Confluence package a delivery team will actually use.
01Validated requirements package — vision, scope, business + functional requirements, user stories, acceptance criteria, traceability matrix — signed off by 5 real stakeholders.
02BPMN process suite — current/future state diagrams in Camunda/Signavio with swim lanes, gateways, and event flows; SLA targets per task.
03AI requirements copilot — a Cursor/Claude-driven assistant that drafts user stories from interview notes, flags gaps, and lints stories against INVEST + Gherkin formats.
04Agile delivery scaffold — Jira board with epics/stories, definition-of-ready/done, sprint cadence, and a Confluence runbook the team actually uses.
Outcome: 5 stakeholder interviews
Reviewer: Pragmatic-certified BA
Plan: 3-sprint delivery
BPMNJiraConfluenceBABOKAI Copilot
Enterprise · weeks 6–11
Process improvement & KPI redesign
Reverse-engineer a real partner process — current-state BPMN, pain-point map, future-state BPMN, RICE-prioritised improvements, and a KPI dashboard for stakeholders.
BPMNKPIRICELean
Real-time · weeks 8–12
Agentic-era requirements automation
Build a LangChain / OpenAI-driven assistant that ingests stakeholder transcripts, drafts BRDs/PRDs in Confluence, and auto-creates Jira stories with acceptance criteria.
LangChainOpenAIJira APIConfluence API
Project · weeks 11–12
Your AI BA artifact in a real partner org.
Pick a real partner business problem. Discover, document, and ship the full BA package — requirements, BPMN, user stories, and an AI copilot — to a partner team that's running it for real users.
Download the real world project
Full scope, sample partner orgs, weekly milestones, and grading rubric — PDF, 14 pages.
2026: 220+ shipped76% → placement offers
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Lync · Principal Business Analyst Architect
BPMN · BABOK · Jira · Confluence · Camunda · Signavio · AI Copilots
"Business analysis at enterprise scale is where AI copilots earn their keep — Cursor
and Claude wired into requirements, BPMN modelling that survives the next reorg, and Jira boards a delivery
team will actually execute against. That's the bar I teach to, every cohort."
15 yrs
BA & PLATFORM
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of enterprise platform architecture across analytics, ITSM, and BA practice — leading requirements, BPMN, and Agile delivery for Fortune-500 banks, telcos, and insurers at firms like AT&T, Salesforce, Cox Communications, and Broadcom. Most recently he architected AI-copilot-driven BA workflows that turned interview transcripts into production-grade requirement packages.
His classes get you two things other programs don't give you: a founding architect who's shipped enterprise BA artifacts from inside the Fortune 500, and a curriculum rewritten every quarter — so when hiring managers ask about BPMN gateways, INVEST stories, or AI-augmented requirements, you've already built it. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · Process & Requirements Lead
BPMN · BABOK · Jira · Confluence · Camunda · Signavio · AI Copilots
"Process and requirements is where business analysis stops being slideware and starts
being the operating model of the enterprise — BPMN diagrams a delivery team can execute, BABOK-grade
requirements that survive change windows, and AI copilots that draft user stories without losing rigor.
That's what I teach."
10 yrs
PROCESS & REQS
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the process and requirements practice. After ~10 years of requirements engineering and BPMN modeling for global firms — banks, telcos, and insurance carriers — he stepped into the Chief Technologist seat to wire AI copilots into the BA practice: Cursor- and Claude-driven assistants that draft BRDs, lint user stories against INVEST, and turn interview notes into traceable requirements.
His modules are built from real enterprise rollouts, not slide decks. Expect to leave with working BPMN diagrams in Camunda and Signavio, BABOK-grade requirements packages, a Jira + Confluence delivery scaffold, and an AI requirements copilot you can show in interviews. Hyderabad-based, hands-on, and known for the unglamorous BABOK rigor everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What BA employers say about Digital Lync grads.
Real feedback from delivery leaders at AI-first companies and the firms hiring our Business Analyst + AI graduates.
Digital Lync grads ramp 40% faster on BA deliveries than typical BA hires. Best Business Analyst + AI pipeline in India.
Aakash Mehta, BA Practice Director, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for requirements packages and BPMN suites practices.
Anita Sharma, Senior Manager, Deloitte
The Business Analyst + AI programme is comprehensive — requirements, BPMN, user stories, AI copilots. Grads come pre-trained for enterprise BA practice with AI copilots.
Rahul Bhatt, Solutions Lead, Mphasis
Their BPMN + AI copilot track produces PMs who write production-grade requirements on day one. Rare combination of BABOK rigor and stakeholder craft.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the AI copilot layer baked into the BA track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their CBAP + IIBA-AAC prep is rigorous, and the shipped project — requirements package, BPMN suite, AI copilot — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's BAs ship validated requirements packages twice as fast in the first 90 days. Our internal delivery metrics back this up clearly.
Lakshmi Nair, VP Delivery, Wipro
Best Business Analyst + AI pipeline we've sourced from in India. Their projects are real shipped requirements, not generic templates.
Karthik Subramanian, Engineering Director, Cognizant
Strong BABOK and BPMN engineering foundation. Their BA grads need almost zero ramp time on enterprise BA engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our BA and watsonx delivery teams. Strong fundamentals, sharp on BABOK and BPMN.
Sanjay Verma, Talent Director, IBM
requirements + BPMN suites is exactly the talent gap we've been struggling to close. Digital Lync is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their BA track delivers analysts who navigate requirements, BPMN, and user stories on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our BA practice. Strong on requirements, sharp on BPMN, fluent in AI copilots.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync grads who blend requirements with Azure OpenAI copilots land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Lync · Institute Certificate
Agent‑Ready Business Analyst
Presented to
Spandana Bala
For the successful discovery, modeling, and shipping of an enterprise BA package —
validated requirements, BPMN suite, and AI requirements copilot — evaluated against the IIBA CBAP, IIBA-AAC (Agile Analysis), and Pragmatic Business Analyst credential rubrics.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the BA community and mapped to IIBA CBAP
and IIBA-AAC (Agile Analysis) credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — requirements package, BPMN suite, AI copilot — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: requirements, BPMN, user stories, traceability,
stakeholder management & AI copilots. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
04Job placement support
Your first Business Analyst offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into enterprise delivery teams and AI-first companies. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your requirements package, BPMN suite, Jira board, AI requirements copilot, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the BA artifacts you shipped (requirements packages, BPMN suites, AI copilots), the partner-org project, and the business outcome. Reviewed by BA leaders who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into enterprise delivery teams and AI-first companies — Microsoft, Atlassian, Salesforce, Adobe, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, Postman, plus services that staff BA practices (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
Business Analyst alumni
Hundreds of Business Analyst careers launched — here are eight.
NV
Naveen Vedala
Senior Business Analyst
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Principal Business Analyst
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
BA Lead (AI Copilots)
Seattle · United States
Now at · Microsoft
MM
Mujahed Mohammed
Process Analyst
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
BPMN Modeler
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
Requirements Engineer
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Director of BA Practice
Hyderabad · India
Now at · Atlassian
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online BA Lead (AI Copilots) cohorts running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 90003 29956
US desk
+1 858 666 6719
Hours
Mon–Sat · 9am–9pm
Online class
Global
Weekend and evening BA cohorts running on IST and PST. Every online cohort ships
the same shipped project — requirements package, BPMN suite, AI copilot, Jira board — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Next class
25 May 2026
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the BA stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a tech background or prior BA experience?
No on both counts. Roughly 40% of every class comes from non-tech streams — commerce, finance, engineering, BCom, BBA, and first-time analysts. Weeks 1–2 cover the BABOK fundamentals, BPMN modeling, and requirements craft from scratch. What you do need: consistency and 12–15 hours a week.
Will I actually deliver real BA artifacts, or just templates?
You actually deliver. Every learner ships a validated requirements package with traceability matrix, a BPMN suite in Camunda/Signavio, a Jira board with epics & stories, and an AI requirements copilot wired to OpenAI/Claude. The project is a real partner BA package — not a generic template.
Which tools, frameworks, and AI models will I use?
Requirements & collab: Jira, Confluence, Notion, MS Project, Mural, Miro, Figma, v0. BPMN: Lucidchart, Visio, Bizagi, Camunda, Signavio. Methodology: BABOK, Agile/Scrum, Use Cases, User Stories, Data Modeling, ERD, UML. AI copilots: OpenAI, Claude, ChatGPT, LangChain, Cursor AI. Reporting: SQL, Excel, Power BI, Tableau.
Will I prep for AIPMM Business Analyst and Pragmatic BA Lead (AI Copilots) certs?
Yes. The curriculum is mapped to the AIPMM Business Analyst track and the Pragmatic BA Lead (AI Copilots) credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours modeling BPMN and writing requirements, and ~5 hours of project work (BABOK, Jira, AI copilots). Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire BAs?
Yes — a dedicated placement advisor from week 8, not a helpdesk. AI product hiring partners include Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, and Postman. Resume, LinkedIn, mock interviews, and warm intros are individual.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST cohorts), and a weekend track for working professionals. Every format ships the same shipped project — requirements package, BPMN suite, AI copilot, Jira board — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for anyone more than a week behind, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
Class BAA-026 starts 1 Jun 2026.
40 seats. 12 already claimed.
Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real projects from class 022.
