Software documentation is no longer simply a record of what developers have already built. As artificial intelligence becomes part of the software development lifecycle, companies are beginning to use it to keep technical knowledge connected to the systems it describes.
The change addresses a longstanding problem in enterprise software. Documentation is often created once development is completed, but applications continue to change. Over time, technical information can become incomplete or outdated, leaving developers to reconstruct how systems work when they need to maintain or modernize them.
Andre Gagne, CEO of GFT Technologies Canada, sees the shift as part of a broader change in the role of AI in software engineering.
“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.
GFT said more than 65% of enterprises already use AI for documentation or code analysis. According to the company, teams report up to 40% faster onboarding and up to 30% lower maintenance effort when knowledge assets remain aligned with changing systems.
The technology can work by analyzing code structures, dependencies and logic and using that information to generate or update technical explanations. A change to a payment processing module, for example, could be reflected in associated API documentation, sequence diagrams and runbooks.
That capability could be especially useful in large organizations with complex or legacy software environments. Developers often have to navigate multiple systems and incomplete documentation before they can understand the impact of a change, adding time to maintenance and modernization projects.
For financial institutions, keeping that information current can also have implications beyond engineering. Documentation can help explain how critical applications operate and provide a more consistent record for organizations dealing with regulatory requirements.
The shift also changes the role of documentation itself. Instead of being a final deliverable produced after coding, it can become an evolving source of knowledge that follows the software as it changes.
GFT cautions that AI-generated documentation still requires validation and monitoring. Version control, audit trails, secure authentication and transparency around how information is generated remain important as companies incorporate these systems into development workflows.
The broader trend points to a different way of thinking about enterprise AI. Rather than using AI only to help developers write code faster, companies are increasingly exploring how it can maintain the knowledge surrounding that code as well. For organizations with years of accumulated software, that could become an important part of making modernization more manageable.