Software documentation has traditionally been treated as something that happens after the code is written. But as applications become more complex and development cycles accelerate, keeping technical information aligned with constantly changing systems is becoming a challenge of its own.
Artificial intelligence is beginning to change that process by helping development teams generate, update and connect documentation directly to the software they are building.
According to an analysis by GFT Technologies, teams using AI for documentation or code analysis report up to 40% faster onboarding and up to 30% lower maintenance effort when knowledge assets remain aligned with evolving systems. The company also said more than 65% of enterprises already use AI for documentation or code analysis.
From static documentation to living software knowledge
Traditional documentation can quickly become outdated. Developers may update a payment module, API or internal process without updating the technical material that explains how those changes affect the rest of the system.
AI can potentially reduce that gap by analyzing code structures, dependencies and logic and using that information to explain how different components interact. A change to a payment processing module, for example, could trigger updates to related API documentation, sequence diagrams and operational runbooks.
That capability could be particularly relevant for organizations operating large or legacy software environments. In those systems, developers can spend considerable time trying to understand existing code, dependencies and processes before they can make changes.
For financial institutions, the implications also extend beyond developer productivity. Documentation that remains synchronized with applications can help organizations demonstrate how critical systems operate, while providing a clearer record during modernization and regulatory processes.
Andre Gagne, CEO of GFT Technologies Canada, said the change represents a broader shift in the role AI plays across software development.
“AI stops being a productivity experiment and becomes a foundation of efficiency across the software lifecycle. A 30% reduction in maintenance effort is only part of the story. When documentation stays current automatically, financial institutions can demonstrate with confidence to regulators how their critical applications work. It is also a key success factor for any institution starting a modernisation journey; you can’t modernise what you don’t understand,” Gagne said.
The approach does not eliminate the need for human oversight. GFT also highlighted the importance of validating and monitoring AI-generated documentation, alongside version control, audit trails, secure authentication and transparency around how information is produced.
As AI becomes more embedded in software development, documentation may therefore shift from being a final deliverable to becoming part of the development workflow itself. The result is not simply more documentation, but technical knowledge that can evolve alongside the systems it describes.
