AI Spreadsheet and Data Analysis Tools Compared: Excel Copilot, WPS AI, Teable, NL2SQL, and BI

Hiding under “analyze data with AI” are four completely different products: assistants that write formulas and pivots inside your existing spreadsheet, AI databases that build and manage tables from natural language, converters that turn a sentence into SQL, and BI platforms that answer questions over a governed warehouse. Ranking them on “who analyzes best” is like ranking a calculator, Excel, and a data warehouse on one list.

This guide compares Excel AI, Google Sheets AI, WPS AI, Teable, AI2SQL, SQLAI, Power BI AI, and Qlik Sense AI across four layers. Slide generation and app builders are out of scope.

Quick Verdict

Layer	Tool	Best for	Main tradeoff
Spreadsheet copilot	Excel AI (M365 Copilot)	Office and finance teams in the Microsoft ecosystem	Requires Copilot licensing; numbers need human review
Spreadsheet copilot	Google Sheets AI (Gemini)	Google Workspace teams	Value shrinks outside the ecosystem
Spreadsheet copilot	WPS AI	China-based office work, local file formats	Less AI depth than the two giants; wins on accessibility
AI database	Teable	Teams needing structured collaborative data, open-source self-hosting	A database, not an analytics platform; limited charting
NL2SQL	AI2SQL, SQLAI	Analysts and developers who can review SQL	Generated SQL must be verified before execution
BI	Power BI AI	Self-service analytics in Microsoft-stack enterprises	Governance and semantic models are prerequisite engineering
BI	Qlik Sense AI	Enterprises valuing interactive exploration, hybrid deployment	Steeper learning curve and implementation cost

In one line: turn on the AI assistant inside whichever office ecosystem you already use, move team data into a Teable-class AI database, let SQL-literate people accelerate with NL2SQL, and put enterprise Q&A on a BI AI layer only after data governance is in place.

Scope and Method

This article covers AI products that process and analyze tabular and structured data, compared across four layers; slide tools, app builders, and bare data warehouses are excluded. Three orienting questions: where does the data live (local files, cloud sheets, databases), who uses it (business colleagues or analysts), and what does an error cost (a formula you catch yourself, versus a wrong number entering business decisions)?

The evaluation method is a fixed question set: prepare 10 questions on your own real data (sums and pivots, cross-table joins, trend reading, deliberately ambiguous wording), run the same set through each candidate layer, and verify every number manually. Capabilities and licensing follow official documentation (access verification attempted 2026-07-24); no prices or plans are pinned.

Layer 1: AI Assistants Inside the Spreadsheet

This layer’s logic is “AI comes to your existing sheet,” and value tracks your ecosystem:

Excel AI (Microsoft 365 Copilot in Excel) generates formulas, explains data, suggests pivots and charts, and pairs with Python in Excel for heavier analysis. Two cautions: Copilot is paid licensing stacked on the M365 subscription, and Python in Excel computes in Microsoft’s cloud — sensitive data must clear your company’s data policy first.

Google Sheets AI (Gemini in Sheets) is the same story: in-sheet formula generation, text classification, and summaries, tied into Workspace. Restricted access in China is the hard constraint.

WPS AI is the directly usable option in China: formula generation, data Q&A, and table formatting cover daily needs, with local file-format compatibility and frictionless accounts and payment as real advantages; AI depth trails the two giants — test the scenarios you care about with the fixed question set.

The universal red line at this layer: every number the AI produces must be re-checked. Formula errors, shifted range references, and misread metric definitions (“profit margin”) recur in testing. Treat the AI as an assistant that writes formulas, not an accountant that does the math.

Layer 2: AI Databases

The layer Teable represents sits between spreadsheets and databases: a multi-view table interface with field types, views, permissions, and APIs, plus AI-assisted table building and data processing. When your “spreadsheet” is actually shared structured team data (customer lists, project ledgers, inventory), the freeform cell is the enemy of data quality — field constraints and permissions are the real need.

Teable is open source and self-hostable, friendly to teams whose data cannot leave the network. Its boundary is equally clear: it is where data lives, not an analytics platform — complex charts and large aggregations connect to BI or export. In China, ecosystem options like Feishu Base cover similar ground; teams already on Feishu should use what is at hand first.

Layer 3: NL2SQL

AI2SQL and SQLAI translate natural language into SQL, plus explain, optimize, and fix it. The audience test is simple: can you read the generated SQL? If yes — it is an efficiency tool saving typing time. If no — it is a risk tool, because you cannot detect that it read “last month’s active users” as “last month’s registered users.”

Three conditions for using this layer well: give the tool a complete schema with field comments (schema documentation quality directly determines generation quality); execute with read-only accounts, and keep writes human-only; validate critical queries with known-answer questions first (“last year’s total revenue” — a number you already know). General coding assistants (Copilot, Claude) also write SQL; the dedicated tools’ increment is dialect support and schema management — try both routes.

Layer 4: The BI Platforms’ AI Layer

Power BI AI (Copilot in Power BI) and Qlik Sense AI (Insight Advisor and related) put natural-language Q&A on top of a governed semantic model. This is the right road for “business colleagues ask the data directly” — but the premise is in the qualifier: a governed semantic model. With inconsistent metric definitions and unbuilt dimension tables, AI Q&A merely translates data chaos into fluent wrong answers.

Choose by existing stack: Microsoft estates (Azure, Fabric, Teams) take Power BI; teams valuing interactive exploration, the associative engine, and hybrid deployment take Qlik. Both are implementation projects, and AI-feature licensing tiers need separate confirmation. Before buying the AI layer, self-check: is there a metric dictionary, named data owners, and refresh monitoring? If not, the money belongs in governance first.

Data Security and Compliance

The shared upstream question across all four layers: where does the data go? Spreadsheet-copilot AI processing mostly runs in the cloud (Microsoft’s and Google’s enterprise terms carry data commitments; WPS under domestic terms); NL2SQL tools may upload schemas or even data samples — check; BI-layer data already lives in the platform, so the incremental risk is whether AI features introduce new processors. Before financial data or customer personal information enters any AI feature, confirm training use, retention, and processing-location clauses in your enterprise agreement. For China-based procurement checks, see the China-accessible AI tools guide.

The Combined Workflow

Most teams end up layered rather than single-product: ecosystem copilots for daily sheets, a Teable-class database for team ledgers, NL2SQL for analysts, BI for management. To wire tabular data into automation (scheduled summaries, anomaly alerts), use a workflow automation platform; to try “chat with your spreadsheet” hands-on, this site has a ready build: talk to your Google Sheets with ChatGPT.

FAQ

Excel Copilot or WPS AI?

Follow the ecosystem: company on M365 and willing to pay Copilot licensing — Excel Copilot; China-based office work, WPS files, no international payment — WPS AI. Test both with the fixed question set; never switch an entire office ecosystem for “stronger AI.”

Can AI-generated formulas and SQL be trusted?

Used, yes; trusted blindly, no. Validate formulas on small known-answer data first; read SQL before executing, on read-only accounts. The most insidious error source is the AI’s reading of business definitions (“active,” “retention,” “gross margin”) — write definitions into prompts or schema comments.

What is the essential difference between Teable and Excel?

Excel is freeform cells; Teable is structured data with field types and constraints. Personal analysis and ad-hoc math belong in Excel; business data maintained by many people over time (each row a record, fields consistent) belongs in a Teable-class product — data quality improves by an order of magnitude.

Can business colleagues use NL2SQL directly?

Not recommended. Someone who cannot read the generated SQL cannot catch semantic errors, and a wrong query result is more dangerous than none. The right road for business Q&A is the BI layer’s AI (on a governed semantic model); leave NL2SQL to people who can review SQL.

Can we run BI AI Q&A without data governance?

You can, but the outcome is chaos translated fluently. The minimum prerequisites: unified core metric definitions (one metric dictionary), named data owners, and reliable refresh. Without these three, the answers carry no credibility.

How do these tools perform in Chinese?

WPS AI and Teable are Chinese-native. Excel Copilot and Sheets AI handle Chinese conversation, but test their understanding of Chinese field names and business terms. NL2SQL tools vary widely in how well they use Chinese schema comments. Put a few Chinese-field questions into your fixed set.

Official Sources and Verification

• Microsoft: Copilot in Excel and Python in Excel documentation, access verification attempted 2026-07-24.
• Google: Gemini in Google Sheets notes, access verification attempted 2026-07-24.
• WPS: wps.cn AI feature pages, access verification attempted 2026-07-24.
• Teable: teable.ai and the open-source repository, access verification attempted 2026-07-24.
• AI2SQL: ai2sql.io, access verification attempted 2026-07-24.
• SQLAI: sqlai.ai, access verification attempted 2026-07-24.
• Microsoft Power BI: Copilot in Power BI documentation, access verification attempted 2026-07-24.
• Qlik: qlik.com and Qlik Cloud Analytics docs, access verification attempted 2026-07-24.

AI-feature licensing tiers and data-processing terms change frequently; this article pins no prices. Enterprise procurement follows the official terms of the day.

Bottom Line

Sort by layer first: ecosystem copilots for daily sheets (Microsoft/Google/WPS — follow your stack), a Teable-class AI database for structured team data, NL2SQL for SQL-literate acceleration, and Power BI or Qlik AI layers only on top of governance. Two disciplines run through all four layers: re-check every AI-produced number, and clear the data terms before sensitive data goes in. Test with 10 of your own real questions before buying — the cost of a wrong data-tool choice was never the subscription; it is the wrong decision made on a wrong number.