Introduction

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 profite à tous, des responsables de l’automatisation aux analystes métier, en passant par les Automation Developer et les employés, grâce aux points forts suivants :

• Économisez des coûts et du temps par rapport à l'approche traditionnelle basée sur le conseil.
• Engagez les équipes commerciales avec une vue objective des opportunités.
• Accélérez la découverte et l'analyse des parties de vos tâches et réduisez le temps consacré à la documentation des processus.
• Obtenez un point de départ accéléré avec un squelette d’automatisation.
• Conforme au consentement à la confidentialité et au règlement général sur la protection des données (RGPD).

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

1. Defining the known task to analyze.
2. Collecting comparable traces of that task from one or more recording users.
3. Merging or comparing the traces to consolidate the variations into one task graph. See Merge traces.
4. Reviewing the merged trace as a Business Analyst or SME.
5. Quantifying the finding, for example by estimating time savings, frequency, or complexity.
6. Submitting the candidate to Automation Hub for further assessment. See Automation Hub.