In our previous article, we explored why AI can accelerate development but cannot replace experience and judgment.
However, even if AI helps create a solution faster, the launch date is only the beginning.
Consider an organization using a mobile app and onsite badge printing solution for annual conferences. The platform may work perfectly when first deployed, but operating system updates, printer firmware changes, and modifications to AMS integrations can introduce new requirements. Maintenance isn’t simply about fixing bugs. It’s about keeping technology aligned with constantly changing environments.
Many organizations underestimate where the majority of technology investment occurs. The initial build often receives the most attention, but maintenance is where the true commitment begins.
Software requires updates. Documentation needs to stay current. Security vulnerabilities must be addressed. New operating systems, browser versions, integrations, and user expectations create a continuous need for adaptation.
Then there’s staff turnover.
What happens when the individual who built the solution leaves? What happens when the original requirements change? Who understands the architecture, the dependencies, and the decisions that were made along the way?
These are not hypothetical concerns. They are realities every organization eventually faces.
Established technology partners spread these responsibilities across dedicated teams, documented processes, support structures, and long-term product strategies. The burden doesn’t rest on a single developer or internal champion.
Maintaining software isn’t glamorous, but it often determines whether a solution continues delivering value years after implementation.
This leads to another frequently overlooked consideration: How much does maintaining and supporting a custom solution actually cost?
Next in the series: The Hidden Costs of DIY and AI-Generated Solutions.
