Finance meets AI: Lessons from the front lines

From the 2025 Technology Finance Symposium - East

Nigel Glenday, the CFO and COO of Masterworks.io, shared his extensive experience using AI to streamline finance and operational workflows within a highly complex art investment business. His career in investment banking, corporate strategy and specialty finance has informed his approach to using AI not just as a tool, but as a way to scale operational efficiency and knowledge across the organization. Glenday emphasizes that AI’s true value lies in its ability to serve as a thought partner, upskill teams and integrate context-specific knowledge into workflows, rather than simply generating large volumes of generic text.

A central theme of his discussion was the importance of embedding contextual knowledge into AI models to make them genuinely useful for business tasks. Off-the-shelf models often lack the nuanced understanding of a company’s internal processes, workflows and tribal knowledge, which can lead to over- or underwhelming results. To bridge this gap, Glenday has developed strategies to inject detailed instructions, structured data and persona-based prompts into AI systems. For example, he demonstrates creating “projects” that act as context-rich agents capable of providing tailored advice or performing specific tasks, such as reviewing acquisition memos or generating strategic insights for finance teams.

Glenday also highlighted the convergence of finance and engineering tools, noting that finance professionals can greatly benefit from coding literacy and workflow automation. Learning programming languages like Python allows analysts to handle data extraction, transformation and loading (ETL) tasks more efficiently than traditional spreadsheets. He emphasized that AI is particularly effective when paired with structured inputs, such as code-based diagrams or knowledge graphs, which allow the model to understand complex organizational relationships and workflows. Tools like Mermaid for diagramming and custom-built knowledge graph platforms have enabled his team to visualize processes, asset structures and performance metrics in a way that is both intuitive and actionable.

Practical applications of these approaches include automating the first-line review of acquisition documents, creating multi-step analytical workflows and generating visual representations of processes that were previously done manually in spreadsheets or presentation software. Glenday’s work demonstrates how AI can act as an operational lever, allowing teams to do more with less while ensuring accuracy and context-specific decision-making. He underscores that the combination of structured data, contextual prompts and integration into existing file systems or tools is key to unlocking meaningful ROI from AI initiatives.