Recently, Feishu Multidimensional Tables AI is reported to have topped two benchmark evaluations for real-world business data analysis—FDABench and SpreadsheetBench—according to the vendor's own announcement, with reported leading results across the FDABench tasks and the SpreadsheetBench V1 Verified leaderboard. Buyers can check the current public leaderboards to confirm the standings.
Feishu has also said its Multidimensional Tables AI ranked among the top performers in TableBench, another evaluation focused on real business scenarios. TableBench primarily assesses AI’s capabilities in data retrieval, statistical computation, analysis, and visualization when handling complex tables; FDABench further tests AI's ability to integrate multi-source information for business analysis; while SpreadsheetBench emphasizes evaluating AI’s robustness and accuracy in performing real-world spreadsheet operations.
The vendor interprets the reported benchmark results as evidence that Feishu Multidimensional Tables AI can work across table reading, calculation, analysis and multiple data sources. Enterprise buyers should test those functions on their own reports, compare the output with verified results and keep human review in the workflow.
FDABench Evaluation: Validating Multi-Source Data Analysis Capabilities
FDABench aims to evaluate whether AI possesses competencies comparable to those of a human business analyst—specifically, its ability to understand and solve complex, multi-source data challenges encountered in real work environments. Per the benchmark's published description, it spans tasks across domains such as finance, healthcare, and retail, with data sources extending beyond spreadsheets to databases, documents, web pages, images, videos, and audio files; teams can verify the current task count on the benchmark's own site. Through multiple-choice, single-choice, and analytical report generation tasks, it comprehensively tests AI’s data retrieval, logical reasoning, and conclusion-formulation abilities in complex settings.
Feishu reports leading results in the FDABench categories for analytical report generation, single-choice and multiple-choice tasks. The vendor presents this as support for workflows that connect data retrieval, calculation and cause analysis; teams should verify each step on their own sales, project or inventory data before relying on the output.
SpreadsheetBench Evaluation: Validating Real Spreadsheet Operation Capabilities
SpreadsheetBench focuses specifically on practical tasks within real electronic spreadsheets. Rather than posing abstract questions, it provides actual Excel files along with user requirements, asking AI to perform common high-frequency operations such as locating, extracting, summing, counting, calculating, modifying, deleting, highlighting, organizing, and presenting results. The test cases are drawn from real Excel forums, covering a large set of practical problems and test instances.
According to the vendor, Feishu Multidimensional Tables' Data Analysis Agent placed first on the V1 Verified sub-leaderboard of SpreadsheetBench—a subset where responses undergo manual verification, which the vendor says yields higher scoring reliability—with reported leading performance. If the reported results are representative, this suggests that when faced with complex workplace spreadsheets, the AI can comprehend content and execute operations—such as locating data, formatting adjustments, performing calculations, and organizing outputs—though teams should test stability after data changes on their own files. For users, this may reduce time spent manually writing formulas, repeatedly filtering, and cross-checking data—something teams should confirm by timing the same tasks with and without the AI on their own spreadsheets.
Feishu Multidimensional Tables: Equipping Teams with a Professional AI Data Analyst
Multidimensional Tables are designed to hold business data such as customer leads, orders, projects, inventory, payments received, and goals—teams can confirm the data types and volume limits relevant to them during a trial. In the past, transforming this data into actionable insights required dedicated personnel to manually filter, calculate, and analyze. Feishu positions Multidimensional Tables AI as assisting users in computing metrics, manipulating tables, generating analytical reports, and providing business recommendations—though each of these capabilities should be confirmed in a team's own trial before being relied on.
Feishu’s roadmap describes further work on business data structures, real-world datasets and analytical functions. Availability of natural-language retrieval, calculation, cause analysis, visualization, report generation and table operations may vary by version, plan, region and tenant, so teams should confirm the current scope and test it before deployment.