The biggest challenge to any complex change is the lack of detailed knowledge on current state operations. To truly understand and optimize your business operations, you need a clear view of both the big picture and the finer details. That’s where task mining and process mining come in. While task mining zooms in on user-level activities to uncover how tasks are performed, process mining takes a broader perspective, analyzing system-level data to map out end-to-end workflows. In this article, we’ll explore how task mining and process mining work, how they differ, and how the two drive smarter, data-driven decision-making.
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Use cases and examples of task mining
Why is process mining important?
Examples and use cases of process mining
Task mining vs. process mining: Core differences
Task mining vs. process mining: When to use which
How ABBYY Process AI combines process mining and task mining
What is task mining?
Task mining is a method that examines how people interact with systems at the user interface (UI) level—tracking actions like mouse clicks, keystrokes, and data inputs—to gain insights into task execution within business processes. By capturing this detailed activity data, task mining reveals patterns within workflows, shows tasks that are unnecessarily slowing down processes, and identifies areas where automation can streamline operations and improve efficiency.
Why is task mining important?
By analyzing how tasks are performed, task mining provides you with insights to optimize your processes. Here are four key ways that task mining benefits businesses:
- Identifies inefficiencies and bottlenecks: By capturing detailed user interactions, task mining highlights where time and resources are wasted.
- Boosts productivity and employee focus: Automating manual tasks frees employees to focus on higher-value work, improving overall performance and job satisfaction.
- Enables smarter automation decisions: By uncovering repetitive, time-consuming tasks, task mining prioritizes opportunities for automation to maximize efficiency and return on investment.
- Accelerates digital transformation: With clear insights into task-level inefficiencies, organizations can strategically adopt process improvements for long-term growth.
Use cases and examples of task mining
Banking and financial services
Task mining streamlines critical processes in banking and financial services, with a focus on optimizing individual tasks such as loan approvals, account openings, and transaction processing. For example, task mining can analyze how employees process loan application reviews by tracking user actions across spreadsheets, CRM systems, and document uploads. This analysis may reveal redundant manual steps like repetitive data entry, which can then be automated to speed up approvals and improve accuracy.
Transportation and logistics
By analyzing how tasks in day-to-day operations like route planning, shipment tracking, and inventory management are executed, task mining helps improve delivery times for transportation and logistics companies. Task mining can identify repetitive manual steps, such as updating delivery statuses across multiple systems, highlighting opportunities to automate these processes to reduce delays, and ensure faster, more accurate updates for customers and partners.
Insurance
Task mining helps optimize insurance processes like claims handling, policy underwriting, and customer onboarding. By analyzing task-level data, task mining also provides deeper insights into areas like fraud detection and compliance monitoring, so insurers can proactively manage risks and deliver better outcomes for customers.
Legal
To get clear visibility into how tasks are performed in key legal processes, such as contract review, case management, and document preparation, task mining is essential. In addition, task mining supports compliance and risk management by uncovering gaps in processes, ensuring accuracy, and enabling firms to meet regulatory requirements.
What is process mining?
Process mining is a data-driven technique that analyzes event logs generated by business systems to visualize and improve processes, as well as monitor workflows for compliance. These logs—which include timestamps, activities, and case IDs from customer relationship management (CRM) systems, enterprise resource planning (ERP) tools, and other core applications—illustrate case execution, showing how processes unfold across multiple systems. By aggregating these trails, process mining creates clear, detailed maps of how workflows operate, including any variations or unexpected steps. This allows business users to easily understand why processes aren’t working optimally and make changes to improve them.
Why is process mining important?
Businesses depend on well-functioning processes. When workflows break down, risks like revenue loss, customer dissatisfaction, and compliance violations follow. By analyzing system event data, process mining provides insights that help businesses improve processes. Here are four key ways that process mining benefits organizations:
- Visualizes processes in detail: Process mining creates clear, data-driven models of how workflows operate, uncovering the real execution of processes across systems.
- Identifies bottlenecks and inefficiencies: By pinpointing delays and redundant steps, process mining highlights areas for improvement to optimize performance and resource use.
- Supports real-time decision-making: Process mining analyzes performance metrics as processes run, enabling businesses to allocate resources effectively and improve outcomes.
- Enhances compliance: With visibility into process execution, organizations can ensure adherence to internal policies and regulatory standards, reducing compliance risks.
Examples and use cases of process mining
Banking and financial services
Process mining provides a view of end-to-end processes in banking and financial services, such as mortgage approvals, fraud detection, or payment processing. For instance, process mining can analyze event logs across loan origination systems, credit checks, and underwriting platforms to identify bottlenecks, such as delays in document verification. By uncovering where applications stall or deviate from optimal workflows, banks can reduce costs and offer a better customer experience.
Transportation and logistics
By analyzing event logs from transportation and logistics systems like fleet management tools and warehouse management software, process mining can pinpoint bottlenecks, such as frequent delays in warehouse handoffs or inefficient routing decisions. This helps businesses minimize downtime and improve delivery timelines.
Healthcare
Healthcare organizations can use process mining to optimize patient care pathways by analyzing treatment procedures, resource use, and processing times. This deeper visibility helps identify inefficiencies at every stage, from patient admission to discharge. By addressing these gaps, providers can reduce wait times and ultimately improve the quality of care delivered to patients.
Manufacturing
Manufacturing companies are leveraging process mining to gain a comprehensive view of their end-to-end operations, spanning production lines, supply chain systems, and quality control processes. By analyzing event logs from these systems, manufacturers can pinpoint inefficiencies, such as delays in material delivery, production bottlenecks, or deviations from standard workflows. For instance, process mining can reveal where assembly stages are stalling or identify patterns that lead to frequent quality issues. Manufacturers use process mining to streamline their operations, reduce costs, and improve product quality. This not only enhances overall operational efficiency but also positions companies to meet customer demands more effectively and stay competitive in a fast-paced market.
Task mining vs process mining: Core differences
| Task mining | Process mining | |
|---|---|---|
| Focus | Granular, bottom-up analysis of individual tasks and user activities. | Top-down analysis of entire workflows and processes across systems. |
| Scope | Analyzes user activities and interactions at the task level. Provides insights into specific tasks within applications or user desktops. | Analyzes system event logs to understand end-to-end business processes. Provides a holistic view of processes, including variations and bottlenecks. |
| Data source | User interactions, screen recordings, and application-level data. | Event logs generated by enterprise systems (e.g., ERP, CRM, databases). |
| Dependencies | Automates simple tasks within existing systems and connects to different business systems. | Integrates RPA with AI, predictive analytics, and process mining for end-to-end process optimization and deeper insights. |
| Outcome | Highlights inefficiencies in task execution and improves user productivity. | Identifies bottlenecks, process deviations, and areas for system-level improvement. |
| Value | Enhances task-level efficiency and automation. | Provides end-to-end process optimization and strategic insights. |
Task mining vs process mining: When to use which
Understanding task mining and process mining isn’t about choosing one over the other—organizations need both to gain a complete picture of their operations. The two technologies address different challenges but work together to deliver a 360-degree view of workflows:
- Process mining provides top-down insights into end-to-end business processes, revealing what happened across systems and identifying bottlenecks or inefficiencies.
- Task mining delivers bottom-up details by capturing user activities, explaining how tasks are performed and where time is spent.
Traditional systems in your finance, sales, HR, and service departments offer only a partial view of operations and how work is actually executed, leaving businesses with critical blind spots. Combining task and process mining bridges this gap by providing comprehensive insights that enable companies to become fully data-driven, uncover inefficiencies, and make lasting improvements across their operations.
How ABBYY Process AI combines process mining and task mining
To drive real improvements in your operations, you shouldn’t choose between process mining and task mining. Rather, you need both a holistic, end-to-end view of your processes and a close-up look at the details. ABBYY Timeline process intelligence combines task and process mining capabilities to deliver a 360-degree view of your operations, uncovering how processes really work at both the system and task levels.
Powered by AI and machine learning, ABBYY Timeline automatically detects patterns, identifies inefficiencies, and highlights variations in task execution across large teams. With real-time monitoring and over 25 pre-built tools, it pinpoints high-value automation opportunities, predicts outcomes, and measures the impact of changes, so you can optimize workflows and scale improvements effectively.
As a process intelligence platform, ABBYY Timeline provides end-to-end visibility, enabling smarter, data-driven decisions that deliver meaningful results. Ready to unlock the full potential of your processes? Connect with one of our experts today.