At Boxfusion, we understand that the quality of our Business Requirements Specifications (BRS) is the foundation of successful implementations. Our SMARTGOV solutions support complex governance functions for public sector clients, so it’s essential that our specifications capture each client’s unique needs precisely and clearly.
To achieve this, our Business Analysts are now harnessing Artificial Intelligence to review and refine our requirements documentation. This initiative is transforming the way we work and how we deliver.
Why BRS Quality Matters in Public Sector Solutions
BRS documents are much more than internal paperwork. They are:
- The blueprints for client solutions
- The contract for what will be built
- The bridge between business needs and technology delivery
For SMARTGOV implementations—where compliance, auditability, and usability are critical—our specifications must be:
- Traceable to product features and legislation
- Clear for both technical and non-technical stakeholders
- Structured to support testing, sign-off and change control
The AI Shift: Custom GPTs for Specification Review
To boost the quality of our requirements, our BA team has developed and deployed custom-trained GPT tools that:
- Understand Boxfusion’s SMARTGOV product modules
- Apply a rigorous BRS Quality Criteria Matrix
- Suggest improvements using language, formatting, and validation standards
These GPTs aren’t generic chatbots—they are trained on our:
- Common module templates (such as Performance Management, M&E, SLA Tracking)
- Quality scoring criteria (including requirements traceability, INVEST/SMART principles, terminology consistency)
- Style and documentation standards
A Real-World Example: Performance Management BRS Review
Recently, our team used a custom GPT tool to evaluate a draft Business Requirements Specification for a SMARTGOV Performance Management module. Here’s how the AI helped improve the document:
- Requirements Consistency: The AI detected some overlap between standard and custom fields. Thanks to this, we were able to de-duplicate and clarify those requirements.
- Process Completeness: It identified missing exception paths, specifically for rejected reports, which led us to refine and document the workflow more clearly.
- Validation Criteria: The tool also noted that acceptance criteria were missing for SLA triggers. We added clear acceptance criteria to each relevant requirement description.
This AI-assisted review saved time on manual checks and provided targeted feedback, making the specification stronger and more complete before development started.
Impact on Our Delivery Teams
Since launching this AI-enhanced review process:
- Peer reviews are faster and more focused
- Documentation errors are caught earlier, before development begins
- BAs feel more confident that their outputs meet both internal and client-facing standards
More importantly, this initiative supports our strategic goal of embedding intelligence into every layer of delivery—from discovery to deployment.
AI is not here to replace our Business Analysts – it’s here to amplify their expertise. By combining deep human knowledge with intelligent review tools, we’re setting new standards for quality in public sector digital transformation.
If you’re part of a delivery team looking to boost the clarity, confidence, and quality of your requirements, ask yourself: Who’s reviewing your specs before they go live?
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