The Requirements Crisis
Modern software development faces a critical bottleneck: the "Garbage In, Garbage Out" problem. Development teams are overwhelmed by vague requests, while users feel their feedback disappears into a "Black Hole." The AI-BA (AI Business Analyst) solves this by using Microsoft Copilot to interview users, refine requirements, and leverage a decision board for structured prioritization before any code is written.
The Noise
Unfiltered, vague requests cluttering backlogs.
The Black Hole
Users have zero visibility into request status.
Feasibility Gap
Approvals made without cost/effort data.
Language Barrier
Duplicate requests across English & French.
The "Smart Intake" Funnel
The AI-BA acts as a rigorous filter. Instead of allowing every thought to become a ticket, the system uses Tier-0 Deflection (Smart Search) and AI Interviews to aggregate demand.
The chart illustrates the dramatic reduction in "Noise." From thousands of raw user interactions, only high-value, pre-approved, and feasible stories reach the Development Team in Azure DevOps.
Request Volume Filtration Pipeline
Data-Driven Decision Making (R.I.C.E.)
The Board and Apps Manager do not guess; they calculate. Using the RICE Score (Reach × Impact × Confidence ÷ Effort), the system objectively ranks features. Architects adjust "Effort" and "Confidence" during the feasibility review, dynamically altering the priority.
X-Axis: Effort (Lower is better) | Y-Axis: Impact (Higher is better) | Bubble Size: Final Priority Score
1. Reach & Impact
Defined by the Business/Board based on user value.
2. Effort & Confidence
Refined by Architects during Feasibility Review.
3. The Result
High Impact + Low Effort = Quick Wins (Top Right).
Microsoft Tech Stack Integration
Robust Technical Architecture
The solution leverages the existing Microsoft ecosystem to ensure security, scalability, and compliance.
- ● Copilot Studio: The "Brain" handling intake, search, and translation.
- ● Power Apps: The "Face" for the Board, Apps Manager, and Users.
- ● Dataverse: The "Memory" storing Requests, Votes, and App Registry.
- ● Azure DevOps: The "Engine" where actual development happens.
Process Workflow: From Idea to Release
This "Happy Path" demonstrates the governance model, including the new Multi-App selection and Feasibility Checks.
1. Intake & Deflection
User Search
Tier-0 Deflection checks Knowledge Base & Existing Requests.
App Selection
User selects "HR Portal" or "Finance App".
AI Interview
GenAI refines "Wants" into "User Stories". Auto-translation (EN/FR).
2. Governance Gates
Owner Review
Business App Owner endorses or rejects.
Board Pre-Approval
Apps Manager checks RICE score desirability.
Tech Feasibility
Architect estimates Cost & Effort. Security check.
3. Execution
Final Approval
Board gives the "Go" signal.
DevOps Sync
Automated creation of User Story in Azure DevOps.
Release & Notify
Status syncs back to Power App. User notified in Teams.
Operational Efficiency Impact
Transforming the Process
Moving from a manual email/ticket based system to the AI-BA dramatically improves key operational metrics.
Requirement Clarity
AI interviews ensure developers get complete User Stories, not vague one-liners.
Cost Control
Feasibility checks prevents expensive features from being approved blindly.
Bilingual Compliance
Automated translation ensures 100% adherence to OLA standards.