Google’s Gemini integration within Google Sheets now allows users to offload repetitive spreadsheet tasks. In March 2026, Gemini achieved a 70.48% score on SpreadsheetBench V1 - Full, a benchmark that evaluates performance across 912 real-world spreadsheet tasks. While this performance approaches the level of human experts, users should note that the model may encounter errors in approximately three out of every ten tasks.

Gemini can automate the initial creation of a project tracker by generating a fully formatted table from a single prompt, including specified headers and placeholder data. Users should remain cautious of the model overcomplicating structures by adding unrequested columns. When dealing with messy raw exports, the AI can perform data cleaning, such as standardizing text to Title Case or removing duplicate entries. Users are cautioned to verify dates, as regional format variations between MM/DD and DD/MM are occasionally misread.

Formatting tasks, including the creation of conditional dropdowns and color-coded status rules, can also be handled through natural language prompts. Similarly, the model assists with complex formulas by translating logic into working syntax, such as INDEX/MATCH lookups, though users may need to manually lock cell ranges to prevent errors when dragging formulas. For existing spreadsheets, a dedicated formula troubleshooter can automatically identify and suggest fixes for common #REF or #N/A errors.

Finally, Gemini can generate interactive dashboards and summarize trends based on workbook data. Despite these capabilities, the author emphasizes treating the model as an eager intern rather than an infallible oracle. Because the AI may still struggle with specific nuances like data blank cells or missing cell references, human oversight remains necessary to ensure accuracy for stakeholder-facing reports.

Google’s Gemini integration within Google Sheets now allows users to offload repetitive spreadsheet tasks. In March 2026, Gemini achieved a 70.48% score on SpreadsheetBench V1 - Full, a benchmark that evaluates performance across 912 real-world spreadsheet tasks. While this performance approaches the level of human experts, users should note that the model may encounter errors in approximately three out of every ten tasks.

Gemini can automate the initial creation of a project tracker by generating a fully formatted table from a single prompt, including specified headers and placeholder data. Users should remain cautious of the model overcomplicating structures by adding unrequested columns. When dealing with messy raw exports, the AI can perform data cleaning, such as standardizing text to Title Case or removing duplicate entries. Users are cautioned to verify dates, as regional format variations between MM/DD and DD/MM are occasionally misread.

Formatting tasks, including the creation of conditional dropdowns and color-coded status rules, can also be handled through natural language prompts. Similarly, the model assists with complex formulas by translating logic into working syntax, such as INDEX/MATCH lookups, though users may need to manually lock cell ranges to prevent errors when dragging formulas. For existing spreadsheets, a dedicated formula troubleshooter can automatically identify and suggest fixes for common #REF or #N/A errors.

Finally, Gemini can generate interactive dashboards and summarize trends based on workbook data. Despite these capabilities, the author emphasizes treating the model as an eager intern rather than an infallible oracle. Because the AI may still struggle with specific nuances like data blank cells or missing cell references, human oversight remains necessary to ensure accuracy for stakeholder-facing reports.