The AI Augmented SDLC

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

Chapter 3: Harnessing AI in Requirements Gathering and Prioritization

Agile teams understand the criticality of getting requirements right and their constant evolution over the development life-cycle. Prioritizing these requirements for each sprint can be a daunting task, especially for large, complex projects. Enter AI.

Requirements Gathering

Let’s consider the first use case: continuous requirements gathering. Imagine a situation where stakeholders express their needs via various mediums - emails, chat messages, voice notes, or even hand-written notes. Organizing and making sense of this scattered information is a mammoth task. But with the help of AI, specifically GPT-4’s powerful language understanding and generation capabilities, we can automate this process.

GPT-4 can parse unstructured data from diverse inputs, understanding and categorizing requirements expressed in various forms. It can decipher the context, associate it with the corresponding functional area, and even convert the requirements into a structured format for the development team to act upon.


Our second use case focuses on prioritizing these requirements. This is where GPT-4’s ability to handle nuanced instructions shines. Developers can feed the model a list of requirements along with constraints such as time, resource availability, and strategic business goals. GPT-4 can then output a prioritized list of requirements, balancing the various factors. It can even provide reasoning behind the prioritization, offering insights into potential trade-offs and consequences of prioritizing one requirement over another.

Both of these use cases not only improve efficiency but also ensure a more objective and data-driven approach to requirements gathering and prioritization. However, AI is not a silver bullet, and the results should still undergo human review for validation. But the time saved and the objectivity added make AI a powerful tool in this stage of the Agile SDLC.

Stay tuned for the next post where we will dive into AI’s role in design and coding. From iterative design to writing unit tests, we will explore how AI is revolutionizing these processes.

Chapter 4: Design & Coding