Section 1: Foundation and Project Vision
Every successful software project begins with clarity. Before diving into actors, use cases, diagrams, or code, you must establish a rock-solid understanding of what the system is intended to achieve, why it matters, and who it serves. This foundational stage—often called defining the project vision and scope—prevents misalignment, reduces costly rework, and ensures that every subsequent modeling step (from use case identification to test case generation) remains anchored to real business value.
In traditional requirements gathering, this phase can feel slow and subjective: teams spend weeks in workshops debating vague ideas, producing lengthy documents that quickly become outdated. Visual Paradigm’s AI-Powered Use Case Modeling Studio transforms this into an accelerated, intelligent process. By leveraging AI-assisted forms and generators, you can move from a rough idea to a clear, structured project foundation in minutes—while still retaining full control to refine and validate the outputs.
Core Elements Established in This Section
The Foundation and Project Vision stage focuses on three interconnected building blocks:
- Clearly defining the system’s scope — including its official name, primary purpose, target audience, and boundaries.
- Articulating the problem or opportunity the system addresses in a concise, contextual paragraph.
- Identifying key stakeholders and user types to ensure the solution delivers meaningful value to the right people.
These elements form the “north star” for the entire modeling effort. They guide AI suggestions for use cases, prevent feature creep, and make it easier to evaluate whether later diagrams, flows, and tests truly solve the intended challenge.
How the AI Modeling Studio Accelerates This Phase
Rather than starting with a blank page, you interact with structured, AI-enhanced tools:
- Scope Description Form You fill in straightforward fields: system name, main purpose (often phrased as a high-level goal), and primary audience. The AI can suggest refinements or generate a polished scope statement. → Example: For a mobile dining app, you enter:
- Name: GourmetReserve
- Purpose: “Allows users to book tables and pre-order meals” → AI generates: “GourmetReserve is a mobile application that enables diners to book restaurant tables and pre-order meals, while helping restaurant managers reduce wait times and optimize kitchen workflow.”
- Problem Description Generator Provide a brief prompt (e.g., “ATM system”, “revamp the UI of our shopping cart website”, “online library for university students”), and the AI instantly produces a well-written, paragraph-long problem description that sets context and highlights pain points. You can edit freely in the integrated editor. → Example prompts and outputs:
- Prompt: “ATM system” → Output: “In today’s fast-paced world, customers frequently need quick, secure access to banking services outside traditional branch hours. However, many existing ATMs suffer from long wait times during peak hours, confusing interfaces for first-time users, unreliable hardware, and limited support for modern features such as contactless transactions or multi-currency withdrawals. A next-generation ATM system must address these issues to improve user satisfaction and operational efficiency for banks.”
- Prompt: “Dining reservation system for busy urban restaurants” → Output: “Urban diners often face frustration when trying to secure a table at popular restaurants—long phone waits, fully booked slots without waitlist options, and no ability to pre-order meals ahead of arrival. Restaurant staff struggle with inefficient reservation management, leading to no-shows, overbooking, and underutilized kitchen capacity. A modern dining reservation platform is needed to streamline bookings, reduce wait times, minimize no-shows through reminders and deposits, and enable pre-ordering to improve both customer experience and restaurant operations.”
- Prompt: “E-learning platform for corporate training” → Output: “Many organizations invest heavily in employee training, yet traditional in-person sessions are costly, hard to schedule, and difficult to scale across global teams. Employees frequently complain about outdated content, poor mobile access, and lack of personalized learning paths. An AI-enhanced e-learning platform must deliver engaging, on-demand courses with progress tracking, assessments, certifications, and analytics to help HR measure real skill improvement and ROI.”
- Stakeholder and User Identification Once scope and problem are defined, you map who interacts with or is affected by the system—end users, administrators, external systems, regulators, etc. The AI can propose initial categories based on the problem description, helping ensure the system delivers sufficient value across the ecosystem. → Example for GourmetReserve dining app:
- Primary users: Diners (casual customers, frequent visitors)
- Secondary users: Restaurant managers / staff
- Supporting roles: Payment gateway providers, notification services
- Indirect stakeholders: Restaurant owners (interested in revenue metrics), health inspectors (menu compliance)
Why This Stage Remains Essential (Even with AI)
AI dramatically speeds up drafting, but it thrives on your direction. A poorly scoped prompt yields generic outputs; a precise understanding of business context yields targeted, high-value models downstream. By mastering how to craft effective scope statements, problem narratives, and stakeholder maps, you become an expert “co-pilot” who can:
- Spot when AI suggestions miss domain-specific constraints (e.g., regulatory needs in banking ATMs)
- Refine outputs to align with organizational priorities
- Use the foundation to evaluate completeness in later phases (e.g., “Does every use case trace back to solving the stated problem?”)
With this strong foundation in place, you’re ready to transition seamlessly into identifying actors and use cases—where the AI truly shines at brainstorming and visualizing system functionality.
By the end of Section 1, you will have transformed a vague idea into a crisp, agreed-upon project vision that everyone—from developers to stakeholders—can rally behind. This clarity is the difference between building the right system and merely building a system.