Tyler Von Harz, Community Partner
Dec 18, 2025
Search for Excel AI and you’ll see a familiar promise everywhere: smarter formulas, faster analysis, and a chatbot that helps you “talk to your data.” On the surface, it sounds like Excel has become an AI-powered tool overnight. And in fairness, these features do make everyday spreadsheet work easier.
But underneath those improvements, Excel remains the same tool it has always been: a file-based spreadsheet designed around static cells and formulas. Adding AI on top doesn’t change that foundation. It simply makes the existing workflow easier to operate, not more capable.
This is where a new category is emerging: AI-native spreadsheets. These tools don’t bolt AI onto a legacy model. They embed AI directly into the core experience, alongside native Python, SQL, real database connections, and persistent analytical context. Instead of acting as a helper for formulas, AI becomes an active participant in data work, writing code, transforming datasets, generating visualizations, and supporting repeatable analysis.
In this article, we’ll break down why Excel with AI still falls short, where it works well, and why AI-native spreadsheets like Quadratic offer a more powerful—and more scalable—approach to data analysis.
What “Excel AI” actually means, and why it hits a ceiling
When people talk about AI in Excel, they’re usually referring to a growing set of helpers layered onto the existing spreadsheet experience. These include AI-powered formula generators, natural-language prompts that explain charts or suggest calculations, and third-party plugins that promise faster analysis.
Microsoft’s Copilot-style features fall into the same category: they make Excel easier to talk to, but they don’t change how Excel fundamentally works.
Under the hood, Excel AI tools still operate within the same constraints that have defined Excel for decades. Data lives in files. Logic lives in cells. Automation happens through formulas and macros. AI can assist with those pieces, but it can’t reorganize the system they depend on. As a result, most AI for Excel features feel impressive in isolation yet brittle when applied to larger, more connected datasets.
Why Excel AI struggles to scale beyond small tasks
The core limitation of Excel AI is architectural. Excel assumes a file-based, offline-first workflow where data is copied, shared, and versioned manually. That model made sense when spreadsheets were personal productivity tools. It breaks down when analysis becomes collaborative, continuous, or tied to live systems.
This is where AI should help most, and where Excel AI falls short. AI agents thrive on persistent context and stable inputs. Excel, by contrast, constantly resets context as files move, formulas change, and datasets are reshaped. The result is an assistant that can help with individual actions but can’t reliably support multi-step reasoning or automation.
At its weakest points, Excel AI is constrained by:
- file-based data ownership
- cell-bound logic
- manual versioning and sharing
These are certainly inconveniences, but beyond that, they prevent Excel AI from behaving like a true analytical system rather than a reactive helper.
What are AI-native spreadsheets? and why are they a different category?
AI-native spreadsheets aren’t trying to improve Excel’s interface or make formulas easier to write (though that is something they can do). They start from a different assumption entirely: that modern data work is code-driven, connected, and iterative. Instead of treating AI as an assistant that reacts to cell-level prompts, these tools treat AI as part of the computational fabric itself.
This shift creates a new abstraction layer. Data, logic, and interpretation live in the same environment, and AI operates across all three simultaneously. The spreadsheets can be a live analytical surface where code executes, data flows in from external systems, and AI can reason across the full context of the work.
At a structural level, AI-native spreadsheets are defined by:
- AI embedded into the computation model
- code, data, and chat in a single workspace
- always-online, database-connected execution
These traits allow AI to act less like a helper and more like an agent that can generate logic, apply transformations, and adapt as your analysis evolves.
Quadratic illustrates how this works in practice without trying to replicate Excel’s model. Instead of formulas as the primary unit of logic, Quadratic uses native Python and SQL directly inside the grid. Analysts can clean data, build models, or run statistical analysis using real code that executes in place, not as an export step.
SQL queries run directly against live databases, with results flowing into the spreadsheet as first-class data. There’s no copy-paste pipeline, no intermediate CSVs, and no need to re-import results after every change. The spreadsheet becomes a live interface to the underlying data system rather than a static snapshot.
AI in Quadratic is also not limited to generating suggestions. It functions as a contextual interface layered across the entire workspace. Through chat-based analysis, the AI can generate SQL queries, write Python transformations, build visualizations, and explain results while retaining awareness of prior steps and data relationships. The experience resembles a notebook and a spreadsheet merged into a single, continuously aware environment.
Where Excel AI breaks, but AI-native spreadsheets excel (pun intended)
These architectural differences show up most clearly in real workflows. When cleaning large datasets, Excel AI can suggest formulas but still relies on manual validation and fragile cell logic. In an AI-native spreadsheet, Python-based transformations are explicit, reusable, and auditable, making large-scale cleanup both faster and safer.
The gap widens further with SQL analysis. Excel AI has no native query engine, which forces users into external tools or awkward workarounds. In Quadratic, SQL executes directly, and query results remain live inside the sheet. Statistical modeling follows the same pattern: Excel AI produces static formulas, while AI-native spreadsheets support re-runnable Python models that evolve as data changes.
Most importantly, repeatability becomes possible. Excel AI tools assist with one-off actions. AI-native spreadsheets support agent-driven workflows where analysis can be rerun, extended, and shared without rebuilding logic from scratch. That’s the difference between AI helping you finish a task and AI helping you build a system.
When Excel AI still makes sense
Despite its limitations, Excel AI is not useless. And it isn’t going away anytime soon. In fact, there are several situations where using AI in Excel is still the most reasonable choice, especially when the work is lightweight and the overhead of switching tools would outweigh the benefits.
For small, self-contained datasets, Excel AI can be genuinely effective. Tasks like generating formulas, checking logic, or summarizing simple tables are well within the comfort zone of an AI Excel assistant. When the data lives entirely inside a single file and the analysis is short-lived, the formula-first model doesn’t become a liability.
Excel AI also makes sense for teams that are already deeply embedded in Excel-centric workflows. If stakeholders expect .xlsx files, rely on familiar formatting, or need quick answers rather than durable analytical systems, AI for Excel can speed things up without forcing a process change. In those environments, AI acts as a productivity boost, not a structural upgrade, and that’s often enough.
Finally, Excel AI remains useful for one-off exploration. When the goal is to test an idea, sanity-check numbers, or answer a narrow question, using AI tools for Excel can be faster than standing up a more robust analytical environment. The tradeoff is that those results are rarely reusable or scalable, but that may not matter for short-term work.
Excel AI is a step forward. AI-native spreadsheets are the leap
Excel AI represents real progress. It makes spreadsheets easier to use, lowers friction for common tasks, and helps more people get value out of their data. For quick analysis and familiar workflows, it’s still a perfectly reasonable tool. But it was never designed to be the foundation for modern, connected, AI-driven data work.
As your data becomes more dynamic, and more tightly linked to live systems, the cracks in the Excel AI model become impossible to ignore. File-based workflows, formula-bound logic, and shallow AI context create friction exactly where teams need clarity and speed. AI can help around the edges, but it can’t overcome those structural limits.
AI-native spreadsheets change the equation by collapsing analysis, code, and reasoning into a single environment. When AI is embedded into the computation model itself, insights don’t have to survive handoffs or translations. They move directly from data to decision, with context intact and logic fully traceable.
That’s the promise behind tools like Quadratic. By combining native Python and SQL with an AI-first spreadsheet interface, Quadratic makes complex analysis easier to build, easier to understand, and easier to reuse. If Excel AI feels like an upgrade, AI-native spreadsheets feel like a different era, and Quadratic is a practical place to experience what that future looks like.
