Why AI Projects Are Never "Done": Speed running SDLC vs. ADLC
Have you ever actually "finished" a traditional software engineering project? You write requirements, design it, build it, test it against a spec, deploy it, and maintain uptime. It's deterministic—same input, same output. "Done" is a real, achievable state. The AI Development Lifecycle (ADLC) turns this upside down. It's probabilistic, it features 7 distinct phases instead of 5, and it operates…
Why do AI projects never truly reach completion? Traditional software engineering projects follow a straightforward path: first, define requirements, then design and build the solution. Next, test against a specified set of criteria, deploy it into production, and maintain its performance. This process is predictable - the same input should always produce the same output.
However, the AI Development Lifecycle (ADLC) operates quite differently. It is probabilistic, consisting of seven distinct phases instead of the traditional five, and it operates in a continuous loop. To better understand this shift, I have created a visual breakdown that shows how each stage has had to be reinvented rather than simply renamed.
One of the most significant changes in the ADLC is the massive amount of time and resources dedicated to data preparation. Roughly 80% of the project's total effort is spent on cleaning, labeling, and structuring data. This can be compared to multiplying legacy migration scripts by ten. In traditional software testing, a test either passes or fails.
However, in ADLC evaluation, the focus is on precision, recall, bias, and adversarial robustness. For example, when developing a cancer screening model, the ethical trade-off is to prioritize recall over precision, which is not just a checklist item but a genuine ethical consideration.
Another crucial aspect of the ADLC is the use of shadow deployments. While traditional software engineering may rely on blue/green rollbacks, AI teams depend heavily on running new models alongside production traffic without affecting the current system. This allows for testing the new model in a real-world environment before fully deploying it.
In addition to shadow deployments, ADLC introduces continuous Governance and Ethics councils that oversee compliance with regulations and ethical standards across every stage of the development process. This is a crucial difference from traditional software engineering, where these concerns were not a primary focus.
As engineers, our roles in AI development have evolved from executing a static plan to overseeing a probabilistic system. This shift in responsibilities requires a change in mindset and approach. The question remains: How is your team balancing traditional software engineering infrastructure with these shifting AI deployment cycles? Are you utilizing shadow deployments or sticking to rigorous local evaluation pipelines?
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
