- Task Mining 概述
- 设置和配置
- 通知
- Task Mining
- 其他资源
Task Mining, AI-based desktop activity analysis that surfaces evidence of automation opportunities and captures task variations, with automation skeleton export as PDD and XAML files.
Task Mining helps you build a deeper understanding of existing processes happening on your team's desktops. It records variations of a known task, uses AI to cluster and merge the captured traces, and surfaces task-level evidence, such as task graphs, variants, and statistics, that analysts and subject matter experts (SMEs) use to identify automation opportunities and process improvement areas.
Task Mining 通过以下优势使每个人受益，无论是自动化负责人，还是业务分析师、 Automation Developer 和员工：
- 与传统的咨询主导方法相比，节省成本和时间。
- 以客观的机会视角与业务团队互动。
- 加快发现和分析部分职责的速度，并减少流程文档编制的时间。
- 通过自动化框架加快起点。
- 符合隐私和通用数据保护条例 (GDPR) 同意书。
Task Mining (formerly known as Assisted Task Mining) empowers you to capture variations of a known task and review and merge the results in a collaborative manner. With the help of Task Mining, you can:
- collect variations of a specific task by capturing each action you perform (mouse clicks, keystrokes, and hotkeys)
- merge variations into a comprehensive picture of your task for end-to-end understanding and further analysis
- edit the task graph and annotate actions
- accelerate actionability by generating essential assets for automation implementation: a PDD and an XAML file
What Task Mining does and does not do
Task Mining is built around a known task: one that a Business Analyst or Process Subject Matter Expert (SME) has already identified as worth investigating.
Task Mining does:
- Recording and comparing variations (traces) of a known task
- Using AI to cluster screenshots and merge traces into a single task graph with decision points
- Surfacing task-level evidence, such as variants, step statistics, and time-per-action data
- Exporting artifacts, including a Process Definition Document (PDD) and a Studio XAML skeleton, for further use
Task Mining does not:
- Automatically discovering unknown tasks across your organization
- Producing root-cause findings or a ranked list of automation recommendations
- Replacing the review of a Business Analyst or SME
- Analyzing end-to-end system event logs across business systems: see Process Mining for that capability
How Task Mining uses AI
Task Mining's AI clusters screenshots of the same application and screen across traces, then merges matching steps and creates decision points where actions differ. This clustering and merging produces the consolidated task graph, but the AI does not rank findings or recommend which automation candidate to pursue. Determining automation potential and submitting a candidate to Automation Hub remains a manual step performed by a Business Analyst or SME.
Expected workflow
- Defining the known task to analyze.
- Collecting comparable traces of that task from one or more recording users.
- Merging or comparing the traces to consolidate the variations into one task graph. See Merge traces.
- Reviewing the merged trace as a Business Analyst or SME.
- Quantifying the finding, for example by estimating time savings, frequency, or complexity.
- Submitting the candidate to Automation Hub for further assessment. See Automation Hub.