I Tested the Best AI Tools in Excel—Here’s What Actually Works

AI is finally getting good enough to do serious work inside Excel—from pulling balance sheets out of PDFs to auditing complex financial models. But not all AI add-ins are created equal. Four of the most popular options—TraceLight, ChatGPT, Claude, and Microsoft Copilot—were put through five realistic scenarios to see which one actually helps and which ones get in the way.

The AI Tools Tested in Excel

All four tools work directly in Excel, but they integrate in slightly different ways and target different types of users.

TraceLight and ChatGPT run as Excel add-ins. You install them and then interact with them through a side panel. They can read your workbook, generate formulas, and even build full models.

Claude also works as an add-in with a chat interface plus extra panels for audit and trace, making it more transparent about what it’s doing and why.

Microsoft Copilot is built directly into Excel. It has two modes: a chat mode that only analyzes your data and an edit mode that can change your workbook. In practice, it struggled the most with reliability and responsiveness.

While Claude and ChatGPT are general-purpose AI models that also run in the browser for many other tasks, TraceLight is much more specialized. It’s clearly designed for finance, consulting, and analytics workflows inside spreadsheets. If you’re just getting started with Claude in Excel, you may find this beginner-friendly guide to using Claude for Excel helpful context.

Scenario 1: Extracting a Balance Sheet from a 92-Page PDF

The first test was a classic finance task: pull the consolidated balance sheet from Amazon’s 92-page annual report PDF into Excel, then calculate a set of financial ratios with a clean summary table.

Claude imported the correct data and created formulas for the ratios. Most numbers matched the original 10-K, and the ratios were calculated properly. However, some totals like “Total current assets” were hardcoded instead of formula-driven, which hurts model flexibility. The formatting was also a bit inconsistent, with odd color choices.

TraceLight produced a cleaner, more professional-looking sheet. Key totals were calculated with formulas, ratios were well structured, and the layout followed good financial modeling practices. It clearly prioritized model integrity and readability.

ChatGPT was the fastest. It pulled in the right data, built the ratio summary, and used formulas for calculations. A few small formatting choices (like missing borders and some totals not being formula-based) kept it from being best-in-class, but overall it was solid.

Copilot struggled. It required opening a new workbook to get a result, and even then the extracted balance sheet was fully hardcoded with no formulas and no ratio summary. For a task that should be highly formula-driven, that’s a major drawback.

Verdict for Scenario 1: TraceLight and ChatGPT tied for the top spot, with Claude slightly behind and Copilot last.

Scenario 2: Comparing Two Excel Files for Differences

Next up was a version control task: compare an “old” and “new” Excel file to find structural and data differences—missing rows, changed values, and formula vs. hardcoded changes.

Claude generated a detailed “file comparison report” sheet that listed changes, including deleted rows and value differences. However, it didn’t present the old and new sheets side by side, which makes visual validation harder.

TraceLight shined here thanks to a built-in Compare Spreadsheets feature. You select the old and new sheets, click “Find differences,” and it instantly highlights all changes. A summary panel lists the differences, and you can click into each one to see old vs. new values, including formula vs. static value changes. It even caught subtle issues like dates changing from formulas to hardcoded values.

ChatGPT took a different approach. It uploaded both files, let you view them side by side, and produced a text summary of differences in the chat panel. Useful, but less integrated into Excel than TraceLight’s dedicated comparison tool.

Copilot produced a small, somewhat cryptic summary table and extra tabs with data, but the explanation of what changed and why was unclear. It was the hardest to interpret.

Verdict for Scenario 2: TraceLight clearly led on speed, accuracy, and usability. Claude and ChatGPT were decent but less user-friendly. Copilot came last again.

Scenario 3: Building a Scenario-Based P&L with Dropdowns

The third test moved into full modeling: given assumptions for best, base, and worst cases (monthly recurring revenue, costs, taxes), build a 12‑month profit and loss statement driven by a scenario dropdown.

Claude produced a professional-looking P&L that matched the style of the original sheet. The scenario dropdown worked correctly: switching between best, base, and worst updated net income and other line items as expected. It also added an extra assumptions area, which was somewhat redundant but not harmful.

TraceLight again delivered a clean, well-structured model. It used good practices like EDATE for monthly timelines and highlighted key totals such as EBIT and net income. The scenario dropdown worked smoothly. It did assume a January 1 start date without being told, which may or may not match the user’s intent, but overall the build was strong and easy to read.

ChatGPT was the fastest but stumbled on accuracy. The layout was dense and harder to read, and the scenario dropdown didn’t work due to quoting issues in the formulas. It also introduced an extra “selected case” column whose purpose wasn’t clear, and while it used EDATE, the overall structure needed manual fixing.

Copilot built a 12‑month income statement and wired it to a scenario dropdown that appeared to work correctly. However, it made arbitrary assumptions (like a May start date) and lacked helpful subheaders for key metrics like operating income and net income, making the model less readable.

Verdict for Scenario 3: ChatGPT ranked last due to a broken model despite its speed. Claude, TraceLight, and Copilot all produced usable scenario models, with TraceLight getting a slight edge thanks to speed and polish.

Scenario 4: Auditing a Complex Financial Model for Errors

The fourth test was a real stress test: auditing a multi-tab financial model with intentionally planted issues—hardcoded percentages in the middle of formula ranges, inconsistent plus/minus signs, and mismatched day-count assumptions (365 vs. 360) across working capital calculations.

Claude correctly detected key issues, such as inconsistent formulas in the unlevered free cash flow sheet and the hardcoded 8% and 10% values. However, the way it presented findings was awkward. The layout made it hard to read, even when expanding the panels.

TraceLight has a dedicated Audit panel with live checks that flag potential problems even before you run a full error check. When you run the audit on a sheet, it lists issues clearly and lets you click into each one, tracing precedents and dependents with color-coding. The only limitation is that it audits one sheet at a time, not the entire workbook in a single pass.

ChatGPT was extremely fast and did spot the planted errors. However, it applied fixes directly to the workbook without being asked to, even though the request was only to identify errors. That’s risky behavior in a production model where you may want to review issues before any changes are made.

Copilot simply failed here. It froze and never returned a usable result, even after a long wait.

Verdict for Scenario 4: All tools except Copilot were accurate. ChatGPT won on speed and detection quality, but its habit of auto-fixing formulas without confirmation is a serious concern. TraceLight offered the best balance of clarity and control for auditing.

Scenario 5: Advanced Data Reshaping and World Cup Analysis

The final and hardest test combined data engineering and analysis. The dataset contained FIFA World Cup match data: home and away teams, scores, venue, attendance, and more—around 1,000 rows.

The task had two parts:

1. Create a helper sheet that unpivots the data into one row per team per match (so Argentina vs France becomes two rows: Argentina–France and France–Argentina).

2. Build an analysis sheet with three pivot tables:

– Top 10 teams by win percentage, with gold/silver/bronze formatting for the top three.

– Average attendance.

– Goals conceded per round, plus slicers to filter the views.

Claude correctly built the helper sheet, doubling the row count and ensuring each team had its own record. The analysis sheet used pivot tables and calculated win percentages, but it didn’t actually filter to a top 10 list and didn’t sort by win percentage. It also skipped the slicers requested for interactive filtering.

TraceLight again nailed the helper sheet with the correct row count and structure. On the analysis sheet, it:

– Built a true top 10 pivot table for win percentage.

– Applied gold/silver/bronze formatting to the top performers.

– Added slicers for interactive filtering.

– Created pivot tables for average attendance and total goals per round.

The only minor miss was not sorting the win percentage table from highest to lowest by default, but functionally everything was there and correctly wired.

ChatGPT produced what initially looked like a great dashboard. The rankings were sorted from gold downwards and nicely formatted. But under the hood, the tables weren’t pivot tables—they were hardcoded values. That means no dynamic updates, no real aggregation, and much more manual maintenance. Some rounds were also missing, and slicers were absent.

Copilot built a correct helper sheet and started on the analysis, but the top 10 table showed a #SPILL! error because other data blocked the output range. A simple layout adjustment (moving the blocking range) fixed it, but Copilot didn’t resolve this on its own. It also didn’t finish the requested formatting (percentages, gold/silver/bronze) or fully configure the top 10 logic.

Verdict for Scenario 5: TraceLight was the clear winner, delivering all requested elements with proper pivot tables and interactivity. Claude was decent but incomplete. ChatGPT looked good but failed the pivot-table requirement. Copilot partially completed the task but left key pieces broken.

Overall Results: Which AI Excel Tool Should You Use?

Across all five scenarios—PDF extraction, file comparison, scenario modeling, model auditing, and complex data analysis—the tools separated into clear tiers.

4. Microsoft Copilot (Last Place)

Despite being built into Excel, Copilot was the weakest performer overall. It frequently froze, produced hardcoded outputs instead of formula-driven models, and struggled with more complex tasks like auditing and advanced analysis. It can be useful for quick summaries or simple questions, but it’s not yet reliable for serious financial or analytical work.

3. Claude

Claude handled many tasks reasonably well, especially data extraction and scenario modeling. It also correctly detected some subtle structural changes in spreadsheets. However, its interfaces for audit and comparison were harder to read, and it occasionally skipped requested steps (like top 10 filters and slicers). It’s a capable generalist, but not the most polished Excel specialist.

2. ChatGPT

ChatGPT consistently ranked as the fastest tool. It handled PDF extraction, comparisons, and auditing quickly and usually accurately. But it had two recurring issues: sometimes breaking models (as in the scenario dropdown logic) and sometimes hardcoding what should be dynamic (as in the World Cup analysis). Its tendency to silently change formulas during audits is also risky in production spreadsheets.

That said, if you want a powerful, general-purpose AI that also works well with Excel and other tools, it’s a strong option. For broader model comparisons beyond Excel, you might be interested in this deep test of GPT vs Claude in real-world scenarios.

1. TraceLight (Winner)

TraceLight wasn’t always the fastest, but it was consistently the most accurate and Excel-native in its behavior. It:

– Used formulas and pivot tables correctly instead of hardcoding.

– Followed good financial modeling practices and formatting.

– Offered dedicated tools for comparing spreadsheets and auditing models.

– Successfully completed the most complex data manipulation and analysis task.

It’s also the only tool in this test that offers a free version, and its focus on finance and consulting workflows makes it particularly attractive if you live in Excel for modeling, analysis, or reporting.

When to Use Each Tool

Based on these tests, here’s a simple way to decide which AI to reach for inside Excel:

Use TraceLight if: you build or review financial models, work in FP&A, banking, or consulting, or you care deeply about formulas, auditability, and clean modeling standards.

Use ChatGPT if: you want a fast, general AI assistant that can help with Excel plus many other tasks, and you’re comfortable double-checking formulas and structures it generates.

Use Claude if: you like its reasoning style and already use it for other work, and you’re okay with occasionally refining its Excel outputs manually.

Use Copilot if: you need quick, lightweight help directly inside Microsoft 365 and your tasks are simple. For anything critical or complex, you’ll likely want one of the other tools.

AI is already powerful enough to save hours on tedious Excel work—but only if you pick the right tool and still keep a human eye on the results.