The promise of automated data visualization is tantalizing. Simply upload a CSV file, describe what you want, and a polished, interactive chart will magically appear. Chinese startup Manus.im is living up to this promise with its latest data visualization feature, launched this month.

However, my hands-on testing with corrupted datasets reveals a fundamental enterprise problem: the lack of transparency about data transformations. While Manus excels at handling messy data, it falls short in providing a clear audit trail. The result is a tool that’s more flash than substance, not quite ready for boardroom-ready slides.

The Spreadsheet Problem

It’s no secret that the spreadsheet problem plagues enterprise analytics. A Rossums survey of 470 finance leaders found that 58% still rely on Excel for monthly KPIs, despite owning BI licenses. Another study estimates that 90% of organizations are still stuck in this cycle, creating a “last-mile data problem” between governed warehouses and hasty CSV exports that land in analysts’ inboxes hours before critical meetings.

Manus aims to address this exact gap. With a simple upload of your CSV, a natural language prompt, and a wait of just two minutes, Manus generates a clean and interactive chart ready for export – no pivot tables required.

Comparing Manus and ChatGPT

• Testing both Manus and ChatGPT’s Advanced Data Analysis using three datasets (113k-row ecommerce orders, 200k-row marketing funnel 10k-row SaaS MRR), first clean, then corrupted with 5% error injection including nulls, mixed-format dates and duplicates.
• Results showed that Manus was more accurate with messy data, handling nulls, mixed-format dates, and duplicates without explicit instructions.
• ChatGPT, on the other hand, operated like a speed coder, prioritizing fast output over data hygiene, resulting in misleading visualizations.

Here’s an example of the outputs from the same revenue trend prompt on messy e-commerce data. Manus (bottom) produces a coherent trend despite data corruption, while ChatGPT (top) shows distorted patterns from unclean date formatting.

The Transparency Crisis

The lack of transparency about data transformations is a critical issue for enterprise adoption. Manus never surfaces the cleaning steps it applies, leaving an auditor unable to confirm whether outliers were dropped, imputed, or transformed.

When a CFO presents quarterly results based on a Manus-generated chart, what happens when someone asks, “How did you handle the duplicate transactions from the Q2 system integration?” The answer is silence.

Warehouse-Native AI

Major platforms are building chart generation directly into enterprise data infrastructure. Google’s Gemini in BigQuery, Microsoft’s Copilot in Fabric, and GoodData’s AI Assistant are examples of warehouse-native solutions that eliminate CSV exports entirely.

These solutions preserve complete data lineage and leverage existing security models, making them more scalable and trustworthy for enterprise adoption.

Critical Gaps

• Live data connectivity remains absent – Manus supports file uploads only, with no Snowflake, BigQuery, or S3 connectors.
• Audit trail transparency is completely missing – enterprise data teams need transformation logs showing exactly how AI cleaned their data and whether its interpretation of the fields are correct.
• Export flexibility is limited to PNG outputs – enterprises need customizable, interactive export options.

The verdict is clear: impressive tech, but premature for enterprise use cases.