Is Excel still worth learning as a skill in 2026, as Google Sheets can now be automated with AI?

Question

Is Excel still worth learning as a skill in 2026, as Google Sheets can now be automated with AI?

Adeel Khan 250

With Google Sheets now offering AI features that can automate formulas, summaries and data cleanup, and Excel also adding Copilot and automation, I am wondering whether learning Excel deeply is still a worthwhile skill in 2026, for jobs, freelance work and data-related tasks.

Do employers and clients still expect strong Excel skills, or is spreadsheet work shifting to AI-assisted tools where deep Excel knowledge matters less? I would appreciate views from people who hire for, or work in, data and office roles.

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2 answers

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Answer 1

Yes—Excel is still worth learning deeply in 2026, but the strongest career skill is Excel plus AI and automation, not Excel instead of AI.

Current job-posting data indicates that Excel remains a common requirement in analyst, business, operations, and office roles; one 2026 tracker reports it in 46% of data-analytics postings, although such third-party figures should be treated as directional rather than a universal measure of employer demand.^1^

Why foundational Excel knowledge still matters

AI can generate formulas, clean data, create PivotTables, and build charts, but somebody must still:

Structure source data correctly.
Define the intended business logic.
Recognize incorrect formulas, joins, totals, or assumptions.
Validate results and explain the workbook to other people.
Maintain the process when the source data changes.

Microsoft explicitly warns that Copilot can make mistakes, misinterpret information, and produce inaccurate results. Its formulas, tables, and insights must be reviewed and verified before use.^2^

Copilot also works through Excel’s underlying features—tables, formulas, charts, PivotTables, formatting, and workbook structures. It does not make those concepts irrelevant. Microsoft describes Copilot as using those built-in tools while leaving the resulting workbook editable.^3^

What “strong Excel skills” should mean in 2026

Prioritize these areas:

Core workbook skills
- Tables and structured references
- Relative and absolute references
- Sorting, filtering, validation, and conditional formatting
- Error handling and workbook auditing
Modern formulas
- XLOOKUP
- SUMIFS and COUNTIFS
- Dynamic-array functions such as FILTER, SORT, and UNIQUE
- LET and, for advanced reusable logic, LAMBDA
Analysis and reporting
- PivotTables and PivotCharts
- Clear chart selection
- Reconciliation and variance analysis
- Dashboard design without fragile manual steps
Data preparation
- Learn Power Query before spending large amounts of time on repetitive copy-and-paste cleanup.
- Power Query connects to data, transforms it without modifying the source, combines sources, and loads the result into a worksheet or Data Model.^4^
Data modeling
- Learn relationships, Data Models, measures, and basic DAX when targeting analyst or BI roles.
- Power Pivot supports multi-table models and analysis over large volumes of data.^5^
Automation and AI
- Use Copilot to draft formulas, summarize data, identify trends, and build an initial report.
- Use Office Scripts for repeatable, deterministic processes. The action recorder can turn manual Excel operations into reusable scripts, which can also be integrated with Power Automate.^6^
- Prefer Power Query for importing and transforming external data; prefer Office Scripts for workbook formatting, visualizations, procedural logic, and Power Automate integration.^7^

How to use AI without becoming dependent on it

For each AI-generated workbook:

Ask Copilot to explain every generated formula.
Test blank values, duplicates, errors, and boundary cases.
Compare totals against a known control result.
Inspect references before filling formulas down.
Save repeatable transformations in Power Query or Office Scripts rather than relying on the same prompt every month.
Document assumptions and refresh instructions.

Also avoid building a skill plan around a single experimental AI feature. For example, Microsoft discontinued the preview COPILOT() worksheet function on September 14, 2026; Copilot remains available through the Excel pane.^8^

Jobs and freelance work

For general office work, accounting support, operations, finance, business analysis, and data analysis, Excel remains a valuable baseline. For freelance work, clients generally care less about formula memorization than whether the delivered workbook is:

Accurate and auditable
Easy to refresh
Clearly documented
Compatible with their environment
Maintainable after handover

Learning Google Sheets is useful too, especially for collaboration-heavy clients. The transferable skill is spreadsheet modeling and data reasoning; Excel then adds a broader Microsoft analytics and automation path through Power Query, Power Pivot, Power BI, Office Scripts, and Power Automate.

A practical sequence is: core Excel → formulas and tables → PivotTables → Power Query → Copilot-assisted verification → Office Scripts or Power Pivot, depending on whether the target is process automation or data analysis.

References

Answer 2

In short - yep - Excel remains a highly worthwhile skill in 2026. While AI tools like Microsoft Copilot and Google Gemini can generate formulas, clean data, and summarize trends, they have not replaced the need for human expertise. Instead, employer expectations have shifted. They no longer just want someone who can enter a formula. They want someone who can validate, design, and audit what AI produces.

For starters, AI models can produce incorrect results or misinterpret complex business logic. If Copilot generates a nested XLOOKUP or LAMBDA function, an advanced user should be able to verify its logic and ensure that business decisions are based on accurate results. Designing scalable data models, structuring relational tables, and planning how data flows across workbooks also require sound judgment and an understanding of the business requirements.

Security and data privacy are additional considerations. Some enterprises, financial institutions, and healthcare organizations restrict the use of AI tools with sensitive data because of regulatory and compliance requirements, including GDPR and HIPAA. In these environments, Excel expertise remains essential, particularly when data cannot be shared with external AI services. Freelance clients also value accuracy and reliability. They need spreadsheets that continue to work when inputs change, not just files that look correct at first glance. Advanced Excel skills make it possible to build robust, reusable templates for clients with limited technical expertise.

Note that the definition of advanced Excel has evolved. Knowing how to write a basic VLOOKUP formula is no longer a significant differentiator because AI can generate one quickly. Instead, focus on Power Query and Power Pivot for automated data preparation and modeling. Learn dynamic array functions such as FILTER and UNIQUE, along with LET, to understand, debug, and optimize complex formulas. Develop data modeling and dashboard design skills to create interactive reports that communicate meaningful business insights. Also, explore Office Scripts and Python in Excel to understand how automation and programming can extend Excel's capabilities.

Generally, it's not a choice between Excel and AI - it's about combining them. AI can draft complex formulas, suggest data transformations, and generate initial automation scripts, reducing the time spent on repetitive tasks and syntax. However, human expertise is still needed to verify the results, correct errors, and ensure that the solution meets the actual business requirements.

Ultimately, AI has made basic spreadsheet tasks easier to automate, but it has increased the value of professionals who can handle complex analysis, reliable automation, data modeling, and quality assurance. Deep Excel knowledge remains valuable, particularly when combined with AI literacy and a strong understanding of business data.

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hth

Marcin