Generative AI Prototype Alignment Process and the Deployment Gap

While generative artificial intelligence technologies reduce software development costs, they cause significant obstacles known as the deployment gap to emerge during the stage of putting prototypes into production.

Challenges in Transitioning from Prototype to Production

The proliferation of generative artificial intelligence has made it easier and faster for companies to produce their own solutions in-house rather than purchasing external software.

However, during the process of turning prototypes into reliable, everyday use solutions, a critical stage called the deployment gap is experienced.

European Banking Sector Example

The experience of a European bank clearly demonstrates the potential risks and costs in this process.

Following a successful pilot study, the bank attempted to internally replicate the project using artificial intelligence tools but struggled to meet regulatory requirements.

Importance of Early Expert Involvement

Successful organizations involve software engineers in the processes at a very early stage to accurately evaluate architecture and maintenance costs.

This strategic approach helps prevent technical disruptions and budget overrun issues that may arise in the future.

Reduction of Technical Debt

Minimizing connections between systems ensures that technical debt, which may occur in operational processes, is kept under control.

To prevent scope creep, the boundaries of use cases must be defined with sharp lines.