Why your software development lifecycle is slow

Traditional software development lifecycles (SDLC) were built around the speed of typing and coordination. However, AI has transformed the speed at which developers can write, refactor, and document code. Despite this, organizations are still slow at shipping. Gartner research suggests that more than half of developers report only a 10% or less improvement in their team's output due to AI, with some seeing no improvement at all.

The issue is that AI is only applied to writing code, leaving other parts of the process unchanged. This leads to more code waiting for manual reviews, slowing production.

To address this, a more comprehensive AI SDLC should be implemented. For smaller projects, this means writing clear requirements for each feature, including what it should do, what it must not do, and how the team will know it works. These requirements should be stored and used as rules for every task, ensuring they stay up-to-date.

AI agents should have a narrow, well-defined scope when writing and testing code, reducing the chances of errors. A second agent or engineer should then review each change against the same rules before it is signed off, with the reviewer not having seen the code being written to avoid bias.

At an enterprise scale, the AI SDLC needs to be more robust. Business intent should be broken down into three steps: a high-level goal, a feature spec, and individual execution tasks. Every piece of code and test case should be traceable back to the requirement that justified it, providing clarity for audits and compliance. The same discipline should be applied to existing systems, with AI workflows connecting to ticketing tools, code repositories, and approval chains to eliminate the need for manual task lists and release packages. The person approving a change should not be the same person who generated it.

Projects often fail when AI tools are introduced, causing engineers to rush code reviews rather than slow down at the start of the process, where the real risk lies. Requirements and specification documents are crucial, as everything downstream inherits their mistakes. Ambiguous requirements result in fast, confident, yet wrong outcomes, causing issues to accumulate. Spending time reviewing and refining specifications pays off in the long run, as rushing can lead to weeks of meetings to align the team.

To truly benefit from AI, the process itself must be rebuilt around the current bottleneck, which is no longer just the code. Getting the requirements and review discipline right is essential, and the speed AI offers will ultimately reach the business. Failing to do so will simply result in faster accumulation of technical debt. Denis Danov, CTO of Dreamix Ltd., emphasizes the importance of this approach.