Project case study
AI Specification Assistant
A Copilot Studio-based AI assistant designed to support product specification, value analysis, and controlled use of internal knowledge sources.
Overview
The AI Specification Assistant is a Copilot Studio-based product concept designed to help product managers, business analysts, and delivery teams create clearer software specifications.
The idea is not to let AI replace judgement, it is to use AI as a controlled assistant that can help organise thinking, challenge vague requirements, identify missing information, assess value, and produce more consistent specification drafts.
Software specifications often fail because the problem is not understood clearly enough before the solution is described. Requirements can be spread across meetings, discovery notes, tickets, training material, historic specifications, customer feedback, and internal product knowledge.
The AI Specification Assistant is designed to help bring that information together into a more structured workflow.
Because this project is commercially sensitive, this case study describes the design principles, product thinking, and broad Copilot Studio architecture only. It intentionally avoids internal implementation detail, proprietary prompts, customer-specific material, confidential product logic, and sensitive knowledge source content.
Project Context
This project explores how Copilot Studio can be used to support product specification work in a controlled and useful way.
In many software organisations, the quality of a specification depends heavily on the clarity of stakeholder input, the experience of the person writing it, and the amount of time available for analysis.
AI creates an opportunity to improve this process, but only if it is used carefully. The risk is that AI can produce confident-looking text that hides weak thinking.
The product challenge is therefore not:
“Can AI write a specification?”
The more useful question is:
“Can AI help to produce better specifications by challenging assumptions, using controlled knowledge sources, and making gaps visible before delivery starts?”
That is the core idea behind the AI Specification Assistant.
The Problem
Specification writing is often more difficult than it appears.
A good specification needs to explain what problem is being solved, who the users are, what value is expected, what business rules apply etc. In practice, early requirements are often incomplete. Common issues include vague user needs, unclear scope boundaries, assumptions hidden in notes or conversations
The result is avoidable rework. Poor specifications create downstream cost for design, engineering, testing, support, implementation, and customer communication etc
Product Goal
The goal of the AI Specification Assistant is to improve the quality, consistency, and completeness of software specifications. The system should help users move from rough input to a structured specification without losing control of the process.
The target output is a specification that is:
- clear, structured, testable
- value-led, technically useful
- reviewed by a human
- aligned to the actual product problem
The product should help answer:
“Have we understood the problem, value, and delivery implications well enough to specify the solution?”
Product Boundary
The AI Specification Assistant should support the specification process.It should not own the product decision. The system should improve the quality of thinking, not bypass it. This boundary is important because specifications influence real delivery decisions.
Technology Choice: Copilot Studio
The project is based on Microsoft Copilot Studio.
Copilot Studio is a suitable choice because the concept depends on controlled AI workflows, topic design, agent orchestration, and secure use of organisational knowledge sources.
The technology choice supports:
- building controlled AI assistants
- defining specialist agent behaviours
- connecting to approved knowledge sources
- guiding users through structured workflows
- keeping AI interaction within a governed environment
- supporting future integration with Microsoft 365 and enterprise tools
The goal is not to build a general chatbot.
The goal is to design a controlled assistant that helps with specific product management tasks.
Conceptual Architecture
The architecture is intentionally simple.
| Component | Responsibility | Output |
|---|---|---|
| Orchestration Agent | Controls the workflow and routes requests | Structured response plan |
| Specification Agent | Builds requirement and specification content | Draft specification sections |
| Value Agent | Assesses value, outcome, and rationale | Value summary and challenge points |
| Knowledge Sources | Provide controlled reference material | Relevant supporting context |
| Human Reviewer | Reviews, edits, approves, or rejects output | Final specification decision |
This separation keeps the assistant focused. It also prevents one generic AI interaction from trying to do every product management task at once.
Orchestration Flow
sequenceDiagram
participant User
participant Orchestrator as Orchestration Agent
participant Spec as Specification Agent
participant Value as Value Agent
participant Knowledge as Knowledge Sources
participant Reviewer as Human Reviewer
User->>Orchestrator: Submit idea, notes or requirement
Orchestrator->>Knowledge: Retrieve relevant approved context
Knowledge->>Orchestrator: Return supporting information
Orchestrator->>Value: Assess product value and rationale
Value->>Orchestrator: Return value summary and challenge points
Orchestrator->>Spec: Request structured specification draft
Spec->>Orchestrator: Return draft requirements and acceptance criteria
Orchestrator->>Reviewer: Present structured output
Reviewer->>Orchestrator: Approve, reject or revise
The user does not need to manually manage each specialist capability. The Orchestration Agent coordinates the process.
Product Design Principles
1. Human judgement Remains Central
The assistant should never be the final authority.
It can suggest, organise, challenge, and draft.
The product manager, business analyst, technical lead, or stakeholder still owns the final decision.
This is especially important because requirements involve trade-offs.
AI can help surface those trade-offs, but it cannot decide them in isolation.
2. Value Before Detail
The Value Agent exists because a well-written specification can still describe the wrong thing.
Before investing too much effort in detailed requirements, the assistant should help clarify:
- why the work matters
- what outcome is expected
- who benefits
- what problem is being reduced or removed
- whether the value is operational, commercial, technical, strategic, or risk-based
This helps prevent the specification process from becoming a formatting exercise.
3. Challenge Before Drafting
One of the most important product decisions is to challenge the requirement before writing the specification.
If the system drafts too early, it may produce a polished document around a weak idea.
The workflow should therefore ask:
- What problem is being solved?
- Who experiences the problem?
- What outcome is required?
- What value is expected?
- What is in scope?
- What is out of scope?
- What assumptions are being made?
- What constraints apply?
- What decisions are still open?
Only after this should a specification be drafted.
4. Structure Over Prose
A long specification is not a good specification. The assistant should prioritise structure.
Useful sections may include:
- problem and value statement
- User stories, acceptence criteria, gherkin tests
- functional and non functional requirements
- assumptions, dependencies and risks The structure should be consistent enough to support delivery, but flexible enough to adapt to different types of change.
5. Safe Use of Sensitive Information
The design must assume that requirements may include commercially sensitive information and therefore prevent exposure
Key Trade-Offs
Trade-Off 1: Copilot Studio Instead of a Custom AI Application
Using Copilot Studio reduces the need to build a complete AI application from scratch.
This is useful because the product problem is workflow design, knowledge grounding, and controlled assistance rather than low-level AI infrastructure.
The trade-off is that the solution works within the design patterns and constraints of Copilot Studio.
For this project, that is acceptable because the objective is to prove a useful AI-assisted specification workflow.
Trade-Off 2: Controlled Workflow Instead of Free Chat
A free chat interface is flexible, but it can become inconsistent.
A controlled workflow is less flexible, but it gives better repeatability.
For specification work, repeatability matters.
The system should guide the user through a process rather than simply wait for a perfect prompt.
Trade-Off 3: High-Level Portfolio Description Instead of Full Implementation Detail
This project is sensitive therefore this page gives limited information
Trade-Off 4: AI Assistance Instead of AI Automation
Full automation would be risky. Specifications are not just documents. They represent business commitments, technical direction, commercial choices, and delivery scope.
AI can improve speed and consistency, but the final document needs review. The assistant should therefore be positioned as a productivity and quality tool, not an autonomous product owner.
Risk Controls
An AI specification tool needs controls.
Controls include:
| Risk | Control |
|---|---|
| AI invents requirements | Use knowledge sources, mark assumptions, and require human review |
| Sensitive information leaks | Keep public description high-level and avoid exposing internal detail |
| Poor requirement quality | Challenge before drafting |
| Weak business case | Use the Value Agent before detailed specification |
| Over-reliance on AI | Keep approval with product owner or reviewer |
| Inconsistent outputs | Use controlled templates and orchestration |
| Missing edge cases | Include dedicated review and acceptance criteria stages |
| Confusion between evidence and inference | Separate knowledge-source content from AI interpretation |
The product was designed around these risks from the start.
What This Project Demonstrates
This project demonstrates several product and design capabilities:
- applying Copilot Studio to a real product management workflow
- designing AI around human judgement rather than replacing it
- identifying where specification quality breaks down
- using an orchestration agent to control specialist capabilities
- separating specification drafting from value assessment
- grounding answers in knowledge sources
- thinking about confidentiality and commercial sensitivity
- designing for review, traceability, and governance
- balancing speed with control
The project is not just about using AI to write documents.
It is about designing a safer and more useful specification process.
Product Management Reflection
The most important product decision in this concept is the decision to include a Value Agent as well as a Specification Agent.
This matters because a specification can be technically well-written and still describe work that has weak value.
The assistant should therefore help answer two questions:
Should this be specified?
How should this be specified?That distinction is important.
AI can make poor ideas look polished very quickly. The system needs to slow the process down at the right points, challenge the rationale, and then accelerate the parts of the workflow where structure and drafting genuinely help.
This reflects a broader product management principle:
Better specifications come from better thinking, not just better formatting.
The AI Specification Assistant is therefore designed around structured thinking, value challenge, controlled knowledge use, and human review.
Status
The AI Specification Assistant is currently best positioned as in a live test.
Future development areas include:
- improved orchestration flows
- safer specification templates
- clearer value assessment
- controlled knowledge source retrieval
- stronger source traceability
- review and approval workflows
- acceptance criteria generation
- gap analysis
- export into delivery tools