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AI-Assisted Technical Business Analysis

Description

This hands-on course helps business analysis professionals strengthen their technical analysis practice in a modern, AI-assisted software delivery environment. Participants learn how to use AI to improve the quality, consistency, and delivery-readiness of analysis work across discovery, requirements, backlog shaping, and cross-team collaboration. The course focuses on practical ways of working that help teams reduce ambiguity, expose risks and dependencies earlier, and prepare clearer analysis outputs without losing judgment, traceability, or control.

 

🕒 Duration: 16 hours

👥 Target Audience:

  • Business analysis professionals and related delivery practitioners who want to strengthen their technical analysis practice and use AI in a more consistent, practical, and delivery-focused way.
Webinar Content

 

Module 1: AI-Assisted Discovery and Analysis Framing
 From Raw Input
to
Structured Analysis
  • Turning scattered stakeholder input into clearer analysis material
  • Identifying goals, scope boundaries, assumptions, unknowns, and open questions
  • Using AI to support early analysis framing and improve clarity before delivery work begins
Clarifying Problem, Scope
& Delivery Context
  • Strengthening problem statements, scope definition, and success criteria
  • Identifying stakeholders, dependencies, business rules, and key delivery constraints
  • Using AI to improve clarification and expose weak or incomplete analysis early
Module 2: Requirements and Technical Analysis Quality
 Requirements, Backlog Quality
& Acceptance Readiness
  • Improving requirements, backlog items, and acceptance criteria for clarity and usability
  • Surfacing ambiguity, missing rules, hidden assumptions, and exception paths
  • Using AI to refine analysis outputs so they are more testable and delivery-ready
Process, Data, Integration
& Non-Functional Analysis
  • Structuring process flows, data needs, system interactions, and integration points
  • Introducing non-functional expectations, operational considerations, and edge cases into analysis work
  • Using AI to broaden coverage and improve consistency across technical analysis areas
Module 3: Delivery Readiness and Reusable AI-Assisted Practices Review, Collaboration
& Reusable Workflows
  • Reviewing analysis outputs for completeness, traceability, and delivery readiness
  • Preparing stronger handoffs across product, engineering, QA, and business stakeholders
  • Building repeatable AI-assisted analysis practices for recurring delivery tasks
Learning Objectives:

After attending this webinar participants will be able to:

  • Use AI to improve the quality and clarity of technical business analysis outputs across common delivery scenarios
  • Strengthen requirements, backlog items, and supporting analysis by exposing gaps, assumptions, dependencies, and edge cases earlier
  • Structure process, data, integration, and non-functional analysis in a more consistent and delivery-ready way
  • Prepare analysis outputs that support better collaboration across product, engineering, QA, and business stakeholders
  • Apply repeatable AI-assisted analysis practices that improve review quality, handoffs, and team ways of working
Prerequisite Knowledge
  • Basic familiarity with business analysis or software delivery work
  • Some experience with requirements, backlog items, workflows, or related analysis artifacts
  • Basic understanding of software delivery concepts such as systems, data flows, testing, or agile practices

 

Date

28 May 2026, 16:00-19:00

Cost

Free

Labels

AI-Native
Category

Organizer

Code.Hub
Email
[email protected]

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