The AI Augmented SDLC

Intro/Ideation/Feasibility/Requirements/Design & Coding/Testing/Deployment/Operations/Maintenance/End of Life/Recap/Epilogue

Chapter 2: AI-Augmented Feasibility - Streamlining Assessment with Predictive Analysis

Once we have a solid concept in place, the next step in the Agile Software Development Lifecycle (SDLC) is feasibility assessment. This stage requires thorough examination of the technical, operational, and financial aspects of the idea to ensure its viability. With AI technologies, particularly GPT-4 and its associated capabilities, this process can be significantly optimized. Here, we’ll look at two key use-cases.

AI-Enhanced Technical Feasibility Analysis

Evaluating the technical feasibility of an idea often involves complex estimations such as predicting the time and resources required for development, or understanding the potential technical limitations that might hinder implementation. GPT-4 can help simplify this process.

GPT-4 can process vast quantities of data and use its understanding to generate insightful predictions. For instance, by feeding GPT-4 with data related to past software development projects, including the team’s capacity, technology stack, scope of the project, and the actual time taken for development, it can predict the resources needed for the new project.

Additionally, GPT-4 can analyze the proposed technology stack against its expansive knowledge base and flag potential challenges, like compatibility issues, maintenance overhead, or security concerns, assisting the team in foreseeing and planning for potential obstacles.

Use Case 2: AI-Powered Cost-Benefit Analysis

The financial feasibility of a project is another critical consideration. A cost-benefit analysis can provide insights into the expected return on investment (ROI), but compiling the necessary data and making accurate projections can be labor-intensive.

Using GPT-4, teams can automate this process. When provided with historical financial data, market trends, and project details, GPT-4 can model different scenarios and project the potential costs and revenues associated with each. This capability allows for more nuanced and detailed cost-benefit analyses, helping teams make informed financial decisions.

The use of AI in feasibility assessment reduces the manual effort involved and brings a level of depth and breadth in analysis that was previously unattainable. In the next chapter, we’ll explore how AI continues to streamline the Agile SDLC, this time in the requirements gathering and prioritization stage.

Chapter 3: Requirements