AI and machine learning systems rely on structured and high-quality data to function effectively, as well as a plethora of functions such as well-defined inputs and outputs, consistent formatting, clear documentation, and reliable access to the underlying logic. The problem here is that spreadsheets very rarely match up to these standards. When proprietary models and sensitive financial data are introduced into external AI tools, the risk that this data could be exposed or shared with third parties is extremely high. “Instead of companies abandoning their spreadsheet models as a whole, a more practical solution to this is by modernising them. By updating their software, finance departments can transform traditional spreadsheet models into structured, controlled and web-accessible applications. Companies can also retain institutional knowledge embedded in their existing tools while getting rid of the restrictions that currently prevent AI integration. This includes involving spreadsheet logic within a web-based interface that carries out consistent input and output formats, automates validation, and manages access through user permissions. More importantly, this strategic approach enables a flawless interaction with AI-based tools. These web-based applications can get rid of the complications that accompany spreadsheet use by applying structured data, standardised formats, and transparent workflows. These factors create a setting where AI can be effectively used without manual intervention. By transforming traditional spreadsheet models into secure and structured web applications, companies will be empowered to modernise their workflows without disruption and will lead to a favourable outcome that includes a secure and scalable finance function that is AI-ready.