Arahi AI

Q: What data do I need for process mining?

You need event logs that record activities with at least three pieces of information: a case identifier (which transaction or request), an activity name (what happened), and a timestamp (when it happened). Most business systems generate this data automatically.

Q: How is process mining different from process mapping?

Process mapping is a manual exercise where people describe how they think a process works. Process mining uses actual data to show how a process truly operates. The difference is often surprising and reveals important optimization opportunities.

Q: Can process mining work with any business system?

Process mining works with any system that generates event logs or audit trails. This includes ERP systems, CRM platforms, ticketing systems, workflow tools, and most modern business applications.

Q: How long does process mining take?

Initial analysis can be completed in days once event data is available. Ongoing monitoring is continuous. The challenge is usually accessing and preparing the data, not the analysis itself.

AI Concept

What is Process Mining?

Learn what process mining is, how it works, key benefits, real-world examples, and how it relates to modern AI automation platforms.

Definition

Process mining is a data-driven technique that analyzes event logs from information systems to discover, monitor, and improve real business processes. It creates visual process maps from actual system data, revealing how work truly flows through an organization as opposed to how it is documented or assumed to flow.

Detailed Explanation

Most organizations have a significant gap between how they think processes work and how they actually work. Process documentation, if it exists at all, typically reflects the intended process rather than the reality of daily operations. Process mining closes this gap by reconstructing actual process flows from digital footprints.

The technique works by analyzing event logs that record when activities occur, who performs them, and what case or transaction they belong to. From this data, process mining algorithms construct process maps that show the actual paths work takes, including common variations, bottlenecks, loops, and exceptions.

For automation initiatives, process mining is invaluable because it identifies the best candidates for automation based on actual process behavior. It shows which steps are most time-consuming, where errors occur most frequently, and which process variations could be standardized.

How Arahi AI Makes This Work for You

Arahi AI can analyze your workflow data to identify automation opportunities and optimize existing processes. By examining how tasks flow through your systems, the platform identifies bottlenecks, redundant steps, and high-impact automation candidates. This analysis informs which AI agents to deploy and how to configure workflows for maximum impact.

Key Benefits

Why process mining matters for your business.

Process Transparency

See how work actually flows through your organization, not just how it is supposed to flow according to documentation.

Bottleneck Identification

Pinpoint exactly where work gets stuck, enabling targeted improvements that have the biggest impact.

Automation Prioritization

Identify the highest-ROI automation opportunities based on actual process data rather than guesswork.

Continuous Monitoring

Track process performance over time and detect when processes deviate from optimal patterns.

Real-World Examples

How businesses use process mining in practice.

Order Fulfillment Analysis

Process mining reveals that 40% of orders go through an unexpected rework loop due to address validation failures, identifying a specific automation opportunity that would eliminate the rework.

Support Ticket Flow

Analysis of ticket handling data shows that tickets reassigned more than twice take 3x longer to resolve, highlighting the need for better initial routing automation.

Accounts Payable Optimization

Process mining shows that invoices from certain vendors consistently require manual intervention, enabling targeted automation for those specific vendor formats.