Is Your AI Missing Out? The Hidden Logic in Your Spreadsheets
AI's Spreadsheet Blind Spot: Bridging the Logic Gap
In a previous article, I highlighted the need for AI applications to bridge the gap between their advanced capabilities and the real-time data that businesses depend on. However, for AI to truly transform business workflows, it must also tap into one of the most enduring and ubiquitous repositories of organizational knowledge: spreadsheets. Despite the rapid advancements in AI, spreadsheets remain indispensable for knowledge workers and enterprise operations, with a vast majority of businesses relying on them for essential tasks like financial analysis, reporting, and strategic planning. With over 400 million Office 365 users and countless ad-hoc reports generated daily, spreadsheets continue to significantly influence enterprise decisions. They are deeply embedded in organizational workflows, with more than 90% of businesses utilizing spreadsheets as a core tool for data analysis.
While traditional spreadsheet software like Excel and Google Sheets remains dominant, the spreadsheet landscape is rapidly evolving. From domain-specific solutions like Causal and Canvas that streamline financial modeling, to big data offerings like Sigma Computing capable of handling millions of rows, today’s spreadsheet space caters to a wider range of needs than ever before. Beyond these traditional and next-generation tools, a new wave of AI-driven innovations—exemplified by systems such as SpreadsheetBench and Auto-Formula—highlights how emerging technologies are shaping the future of spreadsheets.
For AI teams, integrating spreadsheets into applications and agents presents a unique challenge: spreadsheets are not just data repositories, but complex systems embodying years of refined business logic. Formulas, references, lookups, and intricate conditional logic are woven into these seemingly simple grids, capturing unique business processes and institutional knowledge. While AI excels at processing text and structured databases, it often struggles to interpret the labyrinth of nested formulas, macros, and chained dependencies that span multiple worksheets and workbooks. This disconnect means that the rich business logic embedded within spreadsheets—the very foundation for pricing, forecasting, and operational decisions—remains largely inaccessible to AI.
This inaccessibility forces users into manual workflows, requiring them to manually plug data into formulas and interpret results, hindering automation and real-time analysis. The real friction arises when trying to automate processes like generating quotes or analyzing ROI, where AI systems, lacking a native understanding of spreadsheet logic, cannot reliably reproduce the trusted outputs that organizations depend on. Bridging this gap requires AI to not only access spreadsheet data, but to truly understand and execute the underlying formulas and dependencies. This will allow AI to preserve existing investments in carefully curated spreadsheet models, while unlocking their potential for scalable, automated workflows and smarter decision-making.
Unlocking Spreadsheet Logic for AI
In a recent conversation with Hjalmar Gislason, CEO of GRID, I discovered how this AI-driven spreadsheet integration company is solving a crucial problem: unlocking the logic hidden within spreadsheets for AI consumption. Instead of replacing these familiar tools, their approach is to seamlessly integrate them into the AI ecosystem. This involves preserving the formulas, named ranges, and cell dependencies that knowledge workers have refined over time, and making them available for AI to leverage.
Their solution works by transforming existing Excel and Google Sheets files into web services. By uploading a file or connecting an existing workbook, the system spins up an independent spreadsheet engine in the cloud, exposing every formula and reference through a RESTful API. This engine is capable of handling complex features like cross-workbook references and iterative calculations, unlike other solutions that flatten spreadsheets into simple data tables. Furthermore, the platform assigns natural language labels to key inputs and outputs (e.g., “monthly price per seat” instead of “B12”), allowing AI tools and non-expert users to interact with spreadsheets using semantic labels. AI models, including large language models, can then call this "cloud spreadsheet" in real-time, passing variables and retrieving immediate, accurate outputs.
Hjalmar shared several real-world examples of how this approach eliminates tedious manual steps and reduces errors. For instance, a restaurant chain now automates its catering process: a custom LLM interface extracts parameters from customer emails, plugs them into the spreadsheet via the API, and returns a finalized quote, complete with a grocery list. Other users run thousands of "what-if" scenarios, adjusting variables like interest rates or headcounts and getting results in real-time. This direct alignment with existing spreadsheet logic gives teams confidence in familiar numbers, while also unlocking new possibilities for automation and analysis.
GRID is not without limitations. Spreadsheets laced with VBA macros or Google Apps Scripts do not translate directly, and companies still need to manage security and compliance when exposing internal files as web services. However, the near-term roadmap includes improved self-service onboarding, deeper function-calling integration for AI apps, richer interactive elements in LLM interfaces, and eventual Python support. With these upgrades on the horizon, the vision is to make every business’s legacy spreadsheet an intelligent AI endpoint, bridging the gap between established organizational knowledge and the newest wave of AI applications.
Connecting the Dots: Operational Data, AI Agents, and Spreadsheets
Spreadsheets are more than static grids; they are living blueprints of a company's core processes, embodying years of refined business logic. The same operational data challenge discussed in my earlier post—where AI remains disconnected from real-time sources—also applies to spreadsheets. Whether we're talking about dynamic CRM data or the nested logic buried in Excel formulas, successful AI integration depends on bridging both gaps. Just as real-time data streams are crucial for dynamic decision-making, the embedded logic within spreadsheets represents a wealth of untapped business intelligence. By transforming spreadsheets into intelligent endpoints, developers are not just automating tasks; they’re enabling AI to leverage the carefully curated knowledge that has been built up over years. When spreadsheets can update in real time and their established business logic is directly accessible to AI, agents move from guesswork to grounded decision-making.
Achieving real-time, AI-driven workflows means integrating operational data pipelines and spreadsheet logic into a seamless environment. By breaking down the barriers between these systems, organizations can build agents and applications that make quick, data-informed decisions without sacrificing the rigorous calculations and historical knowledge that spreadsheets embody. This convergence of AI with both real-time data and spreadsheet logic is essential for creating truly intelligent, context-aware applications that can drive meaningful business outcomes.
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Ben Lorica edits the Gradient Flow newsletter. He helps organize the AI Conference, the NLP Summit, Ray Summit, and the Data+AI Summit. He is the host of the Data Exchange podcast. You can follow him on Linkedin, Mastodon, Reddit, Bluesky, YouTube, or TikTok. This newsletter is produced by Gradient Flow.