Project Stark: Scaling AI-Augmented Business Analysis Across the SDLC
How BT and IBM co-designed an AI-powered Software Development Lifecycle experience — moving from a single proof-of-concept to a production-grade, human-in-the-loop platform for Business Analysts.
From proof-of-concept to production-grade platform
Project Stark is a joint BT and IBM initiative to embed AI agents throughout the Business Analyst SDLC — from initial idea capture through to engineering-ready user stories and acceptance criteria in JIRA. The programme began as a PoC built on IBM's internal tooling platform, delivered on BT's AWS infrastructure.
A long, manual, multi-stage process — with compounding friction at every step
Discovery workshops with BT BAs, PMs and the IBM delivery team surfaced a consistent set of pain points across the full idea-to-JIRA workflow.
“Before the process moves — sometimes shaping documents do not include this detail, that it is very important.”
Quality
Drafts not “engineering ready,” creating slow, repeated back-and-forth between BAs and Solution Architects.
Inconsistent Output
Shaping documents vary widely in completeness, tone, and complexity, making consistent AI support difficult.
Idle Time
Work frequently stalls in review queues, with no proactive nudge to unblock it.
Time-Consuming Groundwork
Manual AWS process mapping, competitor research, and BA drafting consume disproportionate BA time.
Fragmented Context
Business rules, legal/regulatory constraints (Ofcom, data-sensitive setting) and prior context are scattered rather than centrally available.
Design Thinking Workshops — Double Diamond
The team ran a structured series of Design Thinking Workshops (Parts 1–4) following a Double Diamond approach — Discover, Define, Develop, Deliver.
As-Is Process Mapping
Mapped the existing BA workflow end to end — idea intake via JIRA, story creation and SA review. Persona, time taken, tools, pain points, and candidate Key Agent Value Areas.
Context & Gold-Standard Documents
Defined what “good” looks like for AI inputs and outputs; gold-standard requirement documents, user stories and acceptance criteria scored across quality, topic, and complexity.
Value Hypotheses & Prioritisation
Brainstormed and voted on candidate use cases — from AI-checked coverage of story generation to INVEST-aligned drafting, staffing, and QA notification agents.
The AI-Powered SDLC Application
Built around three core views: a personalised dashboard, a human-in-the-loop story review page, and an embedded conversational AI assistant.
Personalised Work List Dashboard
Each BA gets an immediate view of their queue — stories created, pending reviews, and open flagged issues — alongside a prioritised list surfaced from JIRA tickets.
- Low-friction triage via Quick-Preview links
- Priority flags (High/Medium) keep urgent work visible
- Dedicated SME override-panel for auditable governance
User Story Review & Feedback Page
The core human-in-the-loop screen. Each AI-generated story is shown with description, acceptance criteria, and a structured quality evaluation reflecting INVEST compliance.
- Star-rated quality scoring tied to INVEST compliance
- Given-When-Then acceptance criteria structure
- Full audit trail from first draft to approved output
Embedded AI Assistant
A persistent Smart Assistant panel offers conversational support — answering questions about task status, dependencies, and how to perform common actions.
Contextual
Directly aware of the BA's queue and work items.
Conversational
Natural language for status, dependencies, and actions.
Governed
Scoped to authorised actions and BA/PM rules.
Show Cases
Every interaction logged for audit and improvement.
Keeping AI trustworthy — a multi-stage approach
Recognising that AI quality is the biggest adoption risk, the team designed a staged evaluation flow rather than a single pass/fail gate.
Test the Limits
Ran the agent close to real-world shaping documents, not just clean demos.
Balanced Judgement
Neither too tough nor too lenient — checked out LLM-as-judge scoring being too generous.
Full Auditability
Score every intermediate step, for auditability and trend-based improvement over releases.
“Project Stark shows what's possible when AI augmentation is designed around trust, not just speed — a human-in-the-loop platform Business Analysts actually want to use.”