Skip to main content

Planning and Design

Back to SDLC Intro

The Planning and Design phases are the foundational pillars of the SDLC. Mistakes made here are often the most expensive to fix later in the cycle.

Planning Phase​

Planning involves defining the scope, objectives, and of the project. It starts with requirement gathering - understanding exactly what the stakeholders need.

  • : Can we build it? (e.g. technical, economic, legal checks).
  • Requirement Analysis: Detailed breakdown of features (functional and non-functional).
  • Project Scheduling: Estimating timelines, resources, and costs (often using Agile methodologies).

Planning Key Deliverables​

  • Software Requirement Specification (SRS)
  • Project Plan / Roadmap
  • Risk Management Plan
How AI Can Help: Planning

Global AI tools are streamlining the planning process:

  • Meeting Automation: Tools can transcribe and summarize meetings such as the AI feature in Microsoft Teams, extracting action items automatically.
  • Agile Enhancements: In Agile, AI such as Zenhub analyzes historical data to predict sprint velocities and suggest optimal backlogs.
  • Planning Tools: Atlassian Jira uses AI to help teams make data-driven decisions in planning, and can identify potential risks and bottlenecks.

Design Phase​

Once requirements are clear, the Design phase translates them into a blueprint for construction. This includes both high-level architecture and low-level component design.

  • System Architecture: Defining the high-level structure ( vs , Cloud vs On-prem).
  • Data Design: Database schemas and data flow diagrams.
  • UI/UX Design: , mockups, and prototypes for the user interface.

Design Key Deliverables​

  • Design Document (High-Level and Low-Level)
  • Database Schema
  • UI Mockups/Prototypes
How AI Can Help: Design

AI is accelerating the transition from concept to blueprint:

  • Generative UI: AI can generate initial UI/UX mockups from text requirements (e.g., Figma with WireGen or Lovable). Designers can also iterate quickly through design variations using AI-generated designs.
  • Architectural Advice: AI tools can analyze requirements to suggest optimal system architectures and even creates preliminary code structures (e.g. Claude Code).
  • Documentation Analysis: AI can analyze voice transcripts or lengthy discussions on platforms like Harness or Bitbucket to extract key design decisions.
  • Security Design: AI can analyze system designs to identify potential security risks and suggest mitigations.
Case Study: AI in Planning and Design

One startup tooling stack was as follows:

Source: The Pragmatic Engineer