Excel: 5 New Functions You Need Now

Key insights

• REGEX: Use Excel’s regular expression functions to extract or validate patterns like emails, dates, and IDs from messy text.
  They replace many legacy text tricks (LEFT, MID, SEARCH) and speed up cleaning without VBA or Power Query.
• GROUPBY: Group and aggregate data with formulas instead of building PivotTables or many SUMIFS/COUNTIFS.
  It returns live, spillable results that update automatically when source data changes.
• XLOOKUP: Replace VLOOKUP and INDEX/MATCH with a single, simpler lookup that works left-to-right or right-to-left.
  It defaults to exact matches, returns entire rows or arrays, and reduces nested formulas.
• FILTER: Create dynamic, live views of rows that meet conditions instead of using the manual Filter tool.
  Combine FILTER with UNIQUE and SORT for flexible, auto-updating extracts and dashboards.
• COPILOT: Use the built-in AI assistant to generate formulas, summarize sheets, and prototype workflows quickly.
  It speeds work but always verify outputs and watch for privacy or accuracy limits.
• BEST PRACTICES: Keep Excel updated, test new functions on copies, and document named formulas like LAMBDA for reuse.
  Rely on dynamic arrays and verification steps to avoid errors when replacing older workflows.

Video Overview

In a recent YouTube video, Kenji Farré (Kenji Explains) [MVP] highlights five new Excel functions that promise to simplify many common tasks. He frames the update as a shift away from older patterns like VLOOKUP, INDEX/MATCH and manual text parsing, and he demonstrates practical examples to make the case. Consequently, the video aims to show how these functions can reduce formula complexity and speed up workflows for analysts and everyday users. Overall, Kenji emphasizes real-world use cases while noting limitations that viewers should consider.

Text and Pattern Extraction with REGEX

Kenji starts with the REGEX family of functions, noting that they replace a host of older text functions such as LEFT, RIGHT, MID, SEARCH and FIND. He demonstrates how pattern matching can extract emails, dates, or specific tokens from messy inputs, which brings greater precision and less manual cleanup. However, regular expressions introduce a learning curve and can be less readable for colleagues who are unfamiliar with regex syntax.

Therefore, the tradeoff is clear: you gain power and flexibility at the cost of immediate readability and portability. Moreover, compatibility is a concern because not all Excel installations—especially older or non-subscription versions—support the new REGEX functions. Teams should document patterns and provide examples so that results remain maintainable and auditable.

Aggregation and Grouping with GROUPBY

Next, Kenji showcases GROUPBY, which he positions as a formula-based alternative to PivotTables and multiple SUMIFS or COUNTIFS formulas. He highlights how GROUPBY spills aggregated results that update in real time, reducing the need to refresh or rebuild PivotTables for dynamic data. This approach can simplify small-to-medium datasets and embed aggregations directly in reports.

Yet, the benefits come with tradeoffs: complex pivot-style layouts and interactive slicers still favor PivotTables for exploration and presentation. In addition, GROUPBY can produce arrays that are harder to format and combine with legacy macros or external tools. As a result, analysts must balance the convenience of live aggregates against the polishing and interactivity that PivotTables provide.

Finding and Filtering: XLOOKUP and FILTER

Kenji also revisits two functions that have already improved lookups and subsetting: XLOOKUP and FILTER. He demonstrates how XLOOKUP replaces VLOOKUP and INDEX/MATCH with clearer syntax and fewer errors from column shifts, while FILTER creates dynamic subsets without the manual filter UI. Together they simplify common lookup and extraction tasks and integrate smoothly with dynamic arrays.

Nevertheless, these functions can change spreadsheet design patterns because they encourage building live formulas rather than static summaries. Consequently, performance can suffer on very large datasets compared with database queries or Power Query. Therefore, teams should weigh convenience against scalability, and consider hybrid approaches that use Power Query or backend processing for heavier loads.

The Copilot Era: Promise and Caveats

Finally, Kenji discusses the emergence of the Copilot experience inside Excel, which uses AI to suggest formulas and automate tasks. He shows how Copilot can draft formulas, explain outputs, or propose transformations that previously required in-depth formula knowledge. This capability accelerates onboarding and can close skill gaps for non-expert users.

However, dependence on AI introduces several challenges. First, suggestions can be incorrect or suboptimal, so users must validate results carefully. Second, Copilot raises questions about reproducibility and transparency because automatic steps may not be as well-documented as hand-crafted formulas. For these reasons, teams should treat Copilot as an aid rather than a replacement for understanding the logic that drives analyses.

Balancing Adoption, Performance and Governance

Throughout the video, Kenji balances enthusiasm with practical advice, urging viewers to test functions in controlled settings before wide adoption. He recommends documenting new formulas, training team members, and maintaining fallback solutions for users on older Excel versions. This mixed approach mitigates compatibility risks while enabling gradual modernization.

In summary, the functions Kenji presents—REGEX, GROUPBY, XLOOKUP, FILTER and Copilot—offer powerful new patterns that reduce repetitive work and clarify many workflows. Yet, teams must consider readability, performance, governance and skill gaps when integrating them into production spreadsheets. Ultimately, Kenji’s video is a useful guide for practitioners who want to modernize Excel usage while remaining aware of the tradeoffs and challenges involved.

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