

If you run sales, marketing, or client projects, you already live inside spreadsheets. Google Sheets is often the quiet “single source of truth” where sprint scope, story points, and campaign tasks finally meet. Turning that raw, messy data into clear Scrum reports—burndown, velocity, defect trends—makes the difference between reacting to problems late and steering the sprint in real time.
Scrum reports show you when you’ve overcommitted, when scope is creeping, and when technical or process debt is silently growing. A good burndown keeps the team honest about daily progress. Velocity trends tell you whether your promises to stakeholders are realistic. Burn-up and defect charts explain why a sprint felt chaotic even if the board says “Done.”
Now imagine an AI computer agent quietly doing the reporting work for you. Instead of spending Friday night copy-pasting Jira exports into Google Sheets, it opens your tools, pulls the latest data, refreshes pivot tables, and updates charts. In a few hundred keystrokes of instruction, you’ve delegated the entire reporting ritual, so you can focus on the conversations the numbers are begging you to have.
Scrum reports are the heartbeat of your sprints. They tell you if you’re on track, overcommitted, or quietly building up technical and process debt. For business owners, agencies, and marketing or sales teams, the challenge isn’t understanding what to measure—it’s generating reliable reports every single sprint without drowning in manual work.
Below are three layers of approaches, from simple hands-on methods in Google Sheets to fully automated workflows using an AI computer agent like Simular Pro.
These approaches are perfect if you’re early-stage or validating your process.
Day, Total Story Points, Remaining Story Points.Remaining Story Points.Day and series to Remaining Story Points plus an “ideal line” calculated with a simple linear formula.Pros:
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Velocity tab with columns: Sprint #, Committed Points, Completed Points.Pros:
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Burnup with columns Sprint #, Completed Points, Total Scope.Completed Points (rising line) and Total Scope (scope line).Pros:
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Defects tab with Sprint #, Total Defects, Open, Fixed.Pros:
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Once you trust your metrics, the next bottleneck is updating them. Here’s how to remove most of the grunt work without writing code.
=IMPORTRANGE("source_sheet_url","Sheet1!A1:D100")=QUERY(A:D,"select C,sum(D) where B='Done' group by C",1)Pros:
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Current Sprint tab in Google Sheets.Pros:
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Manual and no-code automations solve parts of the problem. But if you’re an agency running ten client teams, or a sales org running multiple enablement sprints, you quickly hit a ceiling: too many tools, edge cases, and layout changes.
This is where a desktop‑grade AI computer agent like Simular Pro becomes a leverage multiplier.
A Simular agent can:
All of this happens by mimicking a power user on your desktop—clicking, typing, navigating—without you building brittle API scripts.
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Imagine a marketing agency owner juggling six client squads. Every second Friday used to end the same way: exporting CSVs from different tools, cleaning them in Google Sheets, rebuilding burndown charts, pasting them into decks. Three hours gone, and by Monday half the charts were already stale.
With Simular Pro, that owner records one “golden path” workflow:
The agent can then repeat this for Clients B–F, adjusting inputs as needed. On reporting day, the owner simply reviews the agent’s run log, skims the updated Sheets and PDFs, and walks into reviews with fresh, trustworthy numbers.
The agent produces consistent, neutral data; you bring the context. Use your freed-up time to:
That’s the real payoff: not just “automated reports,” but better decisions, made earlier, with less friction.
Start by thinking in layers: raw data, calculated metrics, and charts. In Google Sheets, create separate tabs for each. On a Data tab, store one row per work item (user story, task, bug) with columns like Sprint, Status, Story Points, Assignee, Completed Date. This tab should be treated as an append-only log; you can sync it manually, via imports, or automation tools.
On a Metrics tab, use formulas and QUERY to compute sprint-level summaries: total committed points, completed points, remaining points per day, defect counts, and so on. For example, use =QUERY(Data!A:F, "select B,sum(C) where A='Sprint 5' and E<=TODAY() group by B",1) to aggregate points.
Finally, on a Reports tab, place your burndown, velocity, burn-up, and defect charts. Point each chart’s data range at the Metrics tab, not directly at raw data; this keeps your visual layer stable even as the underlying data grows.
First, decide how you’ll track remaining work: story points, tasks, or hours. In a Burndown tab, add columns Day, Ideal Remaining, and Actual Remaining. Fill Day with each working day of the sprint. For Ideal Remaining, start with total planned points in row 1 and use a formula to linearly decrease it to zero by the last day.
For Actual Remaining, either log the remaining story points after each Daily Scrum or use formulas that sum incomplete work from your Data tab using SUMIF or QUERY. Once you have at least a few days’ data, select the table and go to Insert → Chart. Choose a line chart and set the x-axis to Day and both remaining columns as series.
Use chart customization to color-code ideal vs actual lines. Encourage the team to review the burndown briefly in every standup. If the actual line consistently sits above the ideal, you’ve likely overcommitted or are blocked—perfect topics for immediate adjustment rather than end-of-sprint surprises.
Create a Velocity tab with columns Sprint #, Committed Points, and Completed Points. At the end of each sprint, record how many points were planned and how many reached “Done” based on your board definition. After you’ve captured at least three sprints, select the table and insert a column chart.
Use two series—one for committed, one for completed—to quickly spot over- or under-commitment patterns. Optionally, add an average line by calculating =AVERAGE(Completed Points Range) in a helper row and plotting it as an additional series.
During Sprint Planning, reference this chart instead of guessing. If your last five sprints averaged 35 completed points with a relatively stable team, treat that as your planning ceiling. If velocity fluctuates wildly, use it as a signal to inspect sources of instability—frequent scope changes, interruptions, or unclear acceptance criteria—and document actions in your retrospective. For deeper guidance, compare your approach with Atlassian’s velocity documentation.
Start by identifying your system of record: Jira, Trello, ClickUp, or another tool. In Zapier or Make, create an automation triggered when an issue is created, updated, or moved to Done. Map the key fields (sprint, status, story points, type) to columns in a `Data` tab in Google Sheets.For example, in Zapier: Trigger: “New Issue Updated in Jira” → Action: “Create or Update Spreadsheet Row in Google Sheets.” Use “Issue Key” as a unique identifier so rows get updated instead of duplicated. Once this pipeline is in place, your Sheets Data tab updates automatically whenever the team moves work.From there, build Metrics and Reports tabs that reference the synced data. Set your automations to run frequently (e.g., every 5–15 minutes) or in near real-time depending on your plan. This approach removes tedious manual exports while still giving you full control over how Scrum metrics are calculated and visualized in Sheets.
An AI computer agent like Simular Pro can operate your tools the way a human would—just faster and more consistently. Start by designing a “golden” reporting workflow: which board to open, which sprint to export, where your Google Sheets template lives, and how charts should be refreshed and shared.Next, install Simular Pro and record this workflow: the agent logs into your project tool, exports sprint data, opens your Sheets file, pastes or cleans the data, recalculates metrics, refreshes charts, saves an updated version (or PDF), and emails or posts it to the right stakeholders. Because every action in Simular is transparent and modifiable, you can inspect each step and refine it.Once the run looks correct for one team, parameterize it—let the agent accept inputs like “client name” or “project URL”—so you can reuse it across multiple teams or customers. Finally, schedule the agent ahead of sprint reviews so fresh reports are ready when you walk into the room, turning reporting from a manual chore into a background service.