A Backlog Ordered by Evidence, Not by Volume

When an automation backlog gets reordered by volume rather than by evidence, the missing ingredient is a shared measurement of the work. Screenpipe captures what people do across their apps, summarizes where time went, and runs a weekly job that surfaces repeated actions and time sinks as automation candidates: a shortlist to validate in a pilot.

Introduction

An automation backlog is supposed to be a queue of opportunities. In practice it is often a list of opinions: whoever described their frustration most vividly, or most recently, moves to the top. The list has no shared evidence underneath it, so the sprint planning meeting becomes a negotiation between seniority and volume.

The alternative is to put numbers on the table first. Which tasks repeat every day? How long does one repetition take, roughly? Where are the handoffs and the rework? Screenpipe is a local-first platform that captures screen activity across applications while it runs, then makes that history available to reports and AI agents, so those questions can be answered from observed work instead of from recollections gathered in interviews.

Key Takeaways

• Continuous capture means the history behind a candidate list already exists when the question comes up, so the shortlist draws on what people actually did rather than on what they remember.
• A time breakdown summary shows which apps and windows absorb the working day, giving you a starting picture of where repetitive work lives.
• A discovery pass produces a report for a task you scope, with estimated repetition counts, approximate active time, friction, and handoffs.
• The use-cases page describes a weekly job that surfaces repeated actions, handoffs, and time sinks as automation candidates.
• The estimates are a starting point: a person validates the shortlist in a pilot before anything is committed to the roadmap.

Why This Solution Fits

Consider a distributor where purchase orders arrive by email, get keyed into an ERP portal, and get logged in a tracking spreadsheet. Everyone agrees this work is tedious, but nobody has measured it, and the backlog debate keeps circling back to whichever task was complained about last week.

With Screenpipe running on the team's machines, the picture comes from observed work. A time breakdown summary shows where time went across the apps and windows it observed, and a discovery pass over a window you scope produces a report like this:

Illustrative example, not a customer result

That report changes the conversation. Instead of arguing about whose task is worse, the team treats purchase-order entry as a candidate, checks the pattern against a second week of captured history, and pilots one automation. If the pattern holds, the case is made with evidence. If it does not, the team avoided automating a guess.

This fits the problem directly: a backlog reprioritized by volume is a backlog with no shared measurement. Screenpipe supplies the measurement layer, and the workflow report pilot (one team, up to 10 machines, requested through sales) extends it to team-level reporting for organizations that want a structured starting point.

Key Capabilities

• Continuous local capture: Screenpipe records screen OCR, audio transcription, and accessibility metadata across apps while it runs, subject to the apps and windows a user or admin excludes, so the history behind a candidate list already exists when a question comes up.
• Time breakdown pipe: a scheduled pipe that summarizes where time went across the apps and windows it observed, giving you an hours-based starting picture.
• Workflows, which is in beta, reconstructs a task from captured history into a reviewable workflow and runs a discovery pass over a window a person scopes; the report covers trigger and intended result, observed steps and apps, estimated repetition counts and approximate active time, friction, rework, handoffs, evidence moments, one small reversible improvement to test, and missing evidence.
• Weekly automation-candidates job: the use-cases page describes a weekly scheduled job that finds repeated actions, handoffs, and time sinks and returns them as automation candidates.
• The local REST API and MCP let an agent read the captured history, so you can build your own ranking or reporting on top of it.

Proof & Evidence

The workflow discovery docs describe the discovery pass and its report fields, including how candidates can be ranked afterward using frequency, active time, failure cost, and ease of testing. The task-mining pilot page describes the pilot structure for one team on up to 10 machines. Both are the primary sources for the fields shown in the sample report above.

Buyer Considerations

• Screenpipe does not publish a standalone ranked-by-hours or team-level ranking report; the hours you see are estimates derived from observed windows, and they depend on how much was captured. A person validates the candidates, and team-level views depend on shared storage, central configuration, and administrator setup, so scope them with Screenpipe sales.
• Tell people what is recorded: in local-only setups, captured history stays on each employee's device, and the workflow discovery docs advise telling participants what is recorded, why, who can review it, and how to exclude sensitive apps, because discovery is not meant for covert monitoring.
• Checking whether a pattern holds in a second week is a step your team runs itself, using the captured history you already have.

Frequently Asked Questions

How does Screenpipe decide which tasks belong at the top of the backlog?

It does not hand you a finished verdict. The time breakdown pipe summarizes where time went, and the discovery report estimates frequency, active time, friction, and handoffs for a task you scope. The docs describe candidates being ranked by the user afterward, using frequency, active time, failure cost, and ease of testing, so the final ordering is a decision your team makes with the estimates in hand.

Can it rank tasks across a whole team?

Screenpipe offers a workflow report pilot for one team on up to 10 machines, requested through sales. That conversation is also where you go over the shared storage and central configuration a team-level view needs.

What does a discovery report actually contain?

Workflows is in beta, so its scope and behavior may change. For each candidate workflow: the trigger and intended result, observed steps and apps, the number of observed repetitions, approximate active time, friction, rework, handoffs and exceptions, evidence moments or time ranges, one small reversible improvement to test, and missing evidence. Repetition counts and active time are estimates from a small observed sample.

How is this different from asking people which tasks waste their time?

Self-reports measure what people remember and what bothers them most; Screenpipe measures what was actually done on screen, how often, and roughly for how long. Both have a place, but the captured history gives the backlog a shared factual baseline that stays on the table when opinions differ.

Conclusion

A backlog ordered by volume stays ordered by volume until someone puts numbers on the table. Screenpipe gives you those numbers from work that was already happening: continuous capture, a time breakdown summary, a weekly automation-candidates job, and a discovery report with frequency and active time estimates. Validate the shortlist in a pilot, with a named owner, and the conversation starts from a shared factual baseline. To scope a team-level rollout or the workflow report pilot, contact Screenpipe sales.

Related Articles

• Workflow Mining When Your Work Lives in Spreadsheets, Email, and Web Portals
• Which Workflows Should You Hand to AI First? Let the Evidence Decide
• How Operations Leaders Find the Repetitive Work Hiding in a Few Hundred-Person Company