AI Spreadsheet Copilots: Claude in Excel vs Gemini in Sheets vs Rows AI

For most of my career the spreadsheet has been the place where AI hype goes to die. Plenty of tools promised to “talk to your data,” and almost all of them meant a separate web app you pasted a CSV into, got a chart back, and never opened again. The interesting shift over the last year is that the assistants finally moved into the spreadsheet — the same grid where the work already lives. So I spent two weeks running the same analyst tasks through three of them: Claude in Excel, Gemini in Google Sheets, and Rows AI. Same messy revenue export, same cohort question, same “build me a formula I half-understand” requests I get from teammates every week.

I am not an engineer, and I deliberately tested as the non-technical person these tools are sold to. I wanted to know which one actually reduces the number of times I have to interrupt a data analyst, and — more importantly — which one I can trust without becoming the bottleneck myself. Those turn out to be two different questions, and the honest answer is that none of these tools fully solves the second one yet.

What “AI in the spreadsheet” actually means now

The three tools take noticeably different shapes, and the shape matters more than the model behind it.

Claude in Excel is Anthropic’s integration that lives inside Microsoft 365 Excel as a side panel. You chat with it, and it can read the cells and ranges in your open workbook and write formulas and values back into the grid. The thing that stood out immediately is how much it narrates. Ask it to build a year-over-year growth column and it will tell you which cells it is reading, what assumption it made about your date column, and what it wrote where. For someone who is going to be asked “where did this number come from,” that running commentary is genuinely useful.

Gemini in Google Sheets shows up as the “Ask Gemini” side panel inside Sheets. It generates formulas, can create starter tables and templates, and will summarize or analyze a selected range. It feels the most like a helpful coworker who is fast but a little terse — it gives you the formula, drops it in, and moves on. Because it is baked into Workspace, there is no extra login, no extra bill line item if your org already pays for the AI tier, and nothing to install.

Rows AI is the odd one out: it is not an add-on to an existing spreadsheet you already use, it is the spreadsheet. Rows is a modern, browser-native grid with AI functions you can call inline (think of an AI()-style function you drop into a cell) plus an AI analyst that can reason over a sheet. Its real differentiator is data connectors — you can pull live data from sources like analytics platforms, databases, and various SaaS APIs directly into the grid, then point AI at it. That is a different value proposition from “help me with my existing Excel file.”

So before you compare accuracy or price, recognize you are choosing between two philosophies: meet me where my files already are (Claude, Gemini) versus come to a better spreadsheet built for connected data (Rows).

Formula generation: who gets it right, and who explains it

For the bread-and-butter task — “write me the formula” — all three are good enough that the bottleneck is no longer typing syntax. Where they diverge is on ambiguous, real-world requests.

I gave each the same fuzzy ask: “give me the average deal size per rep, but only for closed-won deals in Q1, and ignore the blank rows.” On a clean range, all three produced working formulas. The differences showed up at the edges. Claude was the most likely to ask back or at least flag its assumptions — it noted that two rows had a deal stage of “Closed Won ” with a trailing space and adjusted, which is exactly the kind of silent data dirt that breaks a SUMIFS. Gemini produced a correct, compact formula quickly but assumed my ranges and headers were tidy; when they were not, it produced something that ran without error and returned a subtly wrong number, which is the worst failure mode. Rows AI, because it leans on its own functions and a cleaner data model, was strong when the data came through a connector and slightly more awkward when I pasted in a raw mess.

The pattern held across a dozen tasks: Claude trades a little speed for a lot of explanation, Gemini optimizes for speed and assumes competence, and Rows is happiest when the data arrived through its own pipes rather than a clipboard.

Working over real data, and pulling in live data

This is where the standalone-versus-add-on split really bites.

Claude and Gemini both operate on whatever is already in your workbook. That is a feature — your real data is already there, with all its quirks — and a limit, because neither is meant to be your live data pipeline. If your numbers come from a BI tool or a database, you are still exporting and pasting, and then asking the AI to reason over a static snapshot. Within that snapshot, both are competent at the multi-step stuff: pivot-style summaries, cohort tables, “explain what changed between these two months.” Claude’s habit of stating which ranges it touched made multi-step analysis feel auditable in a way Gemini’s quieter approach did not.

Rows AI is built for the opposite world. Its connectors mean the spreadsheet can hold live data, and the AI analyst reasons over data that refreshes rather than a frozen export. For recurring reporting — a weekly marketing dashboard, a sales snapshot that should not require a human to re-paste a CSV every Monday — this is a real advantage. The trade-off is obvious: you have to rebuild the work in Rows, your colleagues have to open Rows, and anything that lives in a locked-down corporate Excel template stays in Excel.

Trust, verification, and the “where did this number come from” problem

Every honest conversation about AI in spreadsheets ends here. A spreadsheet’s whole job is to be a source of truth, and an assistant that is confidently wrong some of the time is a liability in exactly the place you can least afford one.

Three habits made the difference for me, and they are tool-agnostic. First, never let the AI write directly into a range you have not duplicated — work on a copy, diff the results, then promote. Second, ask the model to restate the formula in plain language and to name a failure mode; this catches the off-by-one-column class of bug remarkably often. Third, spot-check at least one row by hand for any formula that feeds a decision or a number someone else will repeat.

On the verification axis specifically, Claude has a structural edge because of how much it explains by default — it is closer to a colleague who shows their work. Gemini is perfectly capable of explaining when asked, but its default is to hand you the answer, which subtly encourages you to trust and move on. Rows sits in between: its AI functions are visible in the cell, which is auditable, but inline AI calls scattered across a sheet can become their own kind of opacity if you are not disciplined about it.

Lock-in and price: the part nobody enjoys

There is no neutral choice here; each tool ties you to a platform.

Claude in Excel is bound to Microsoft 365 and to having access to Claude through the appropriate plan. If your org is already an M365 shop, the marginal friction is low and the data stays inside the Microsoft tenant your security team already approved — often the deciding factor in regulated environments. Gemini in Sheets is bound to Google Workspace; if you are already paying for the Workspace tier that includes Gemini, it is effectively “free” at the margin and requires zero new vendor onboarding. Rows AI is the standalone bet — its own subscription, its own data residency questions, its own seat for every colleague who needs to open the file.

On price, I will stay qualitative on purpose. As of mid-2026 the embedded assistants are generally bundled into the AI-tier subscriptions of their respective suites rather than billed as a clean standalone line, so the “cost” is really the cost of the Microsoft or Google AI tier your company chooses. Rows offers its own tiers, with a usable free level and paid plans that scale with usage and connectors — figure on roughly the low tens of dollars per user per month for serious use, but check current pricing because connector limits and AI usage caps are exactly the kind of thing that gets revised. Do not make a multi-team decision on a price I quoted in a blog post; confirm the live numbers.

How it stacks up against the wider landscape

It is worth naming what these three are not competing with. Dedicated BI tools still win for governed dashboards consumed by dozens of people; a spreadsheet copilot is for the analyst-in-the-loop, not the executive dashboard. Pure chat assistants that take a file upload (the “paste a CSV into a chatbot” pattern) are fine for one-off questions but lose the round-trip into the grid that makes these three useful. And there are smaller spreadsheet-AI add-ons and Airtable-style tools with AI fields; they overlap with Rows more than with Claude or Gemini, but Rows’ connector breadth and analyst mode are stronger than most. The real choice for most knowledge-work teams is not “which AI is smartest” but “which platform do I already live in, and is my pain analysis or plumbing.”

Who should use which

If your company runs on Microsoft 365 and your work involves explaining numbers to other people, Claude in Excel is my default recommendation. The narration and verification-friendliness genuinely lowered my anxiety about shipping AI-assisted analysis, and the data never leaves your Microsoft tenant.

If your company runs on Google Workspace and you mostly need formulas and quick summaries on data you have already pulled, Gemini in Sheets is the pragmatic pick — it is right there, it is fast, and it adds no new vendor. Just build the habit of asking it to explain, because it will not volunteer.

If your weekly pain is re-exporting and re-pasting the same report, and you are willing to move that workflow into a new tool, Rows AI is worth a serious trial. Its connectors plus AI analyst can turn a manual Monday-morning ritual into something that refreshes itself. The cost is migration and getting colleagues to open one more app.

And if you are a solo PM or analyst who just wants the least friction today: use whichever suite you are already paying for, and revisit Rows the moment your bottleneck shifts from “writing the formula” to “getting the data in at all.”

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