Doubao Work Agent Chains PPT Editing, Excel Analysis, and Industry Research in One Workflow

The author tests Doubao Work Agent on a quarterly business review: modifying an existing PPT template, analyzing Excel data for anomalies like sales-up-margin-down stores, fetching 2026 Q3 retail industry data, and updating the PPT with charts and recommendations — all in a single session while preserving formatting and tracking changes.

Baobao Algorithm Notes
Baobao Algorithm Notes
Baobao Algorithm Notes
Doubao Work Agent Chains PPT Editing, Excel Analysis, and Industry Research in One Workflow

Using AI for office files often involves tedious upload-download cycles. The author evaluates Doubao Work Agent by running a complete quarterly business review workflow — starting from a local PPT, moving through Excel analysis and external research, and ending with an updated PPT — within one continuous session.

Preserving the corporate PPT template

The author opens an existing quarterly review PPT and instructs the agent to modify it in place: keep the enterprise template (fonts, colors, headers/footers), adjust only specific titles, structure, and content, and add a new "business issue analysis" slide. The agent retains the original layout without misalignment, and the edited file remains manually adjustable — practical for reports that undergo repeated revisions.

Excel analysis: from metrics to anomaly detection

The author seeds the Excel with intentional data issues. The agent calculates Q3 sales, gross margin, year-over-year and month-over-month changes, then segments by store, region, and product category to surface anomalies — specifically stores where sales rose but gross margin fell. It also generates corresponding charts. A follow-up prompt asks for possible causes and further verification steps for a flagged store, demonstrating that the agent can reason around a business problem rather than just execute formulas.

Supplementing with external industry data

Internal data alone cannot distinguish company-specific issues from market trends. In the same session, the agent retrieves 2026 Q3 retail industry figures — total retail sales, category performance, market shifts — and compares them with the Excel findings. The author notes that model-provided data is only referential, but it helps surface overlooked angles and narrows the investigation scope.

Updating the PPT with integrated results

Without starting a new task, the agent updates the original PPT: reuses the corporate template, inserts latest KPIs, anomaly details, industry comparison, charts, and a new "business recommendations" slide. A change log records every modification, and a final cross-check against the Excel ensures numerical consistency and template integrity.

Human judgment, background execution, and mode switching

The agent can further distill the top three issues with data evidence, causes, and suggested actions, but the author emphasizes that final business decisions remain human. The tool supports background "small window" execution so users can multitask, and it allows switching between local-computer mode (accessing local files) and cloud-computer mode (viewable via mobile/web) mid-task.

Overall, the agent reduces repetitive file-handling by chaining heterogeneous steps around a single goal. Data accuracy, analytical conclusions, and business judgment still require human verification.

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business intelligenceworkflow automationAI agentquarterly reviewoffice automationDoubao WorkExcel analysisPPT editing
Baobao Algorithm Notes
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Baobao Algorithm Notes

Author of the BaiMian large model, offering technology and industry insights.

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