When organizations set out to improve operational efficiency or prepare for transformation, they often turn to tools like process discovery and process mining. These terms are frequently used interchangeably—but they aren’t the same.
In this post, we’ll break down the key differences between process discovery and process mining, explore when to use each, and highlight a major blind spot that organizations should be aware of when relying on traditional process mining tools.
🔗 If you're new to this topic, check out our Ultimate Guide to Process Discovery and Mapping for a full step-by-step breakdown.
Process discovery is the practice of identifying and documenting how work actually happens inside an organization. This includes both formal process steps and informal behaviors, such as workarounds, manual handoffs, and decision-making shortcuts.
It’s especially useful when:
Modern solutions like ClearWork automate the process discovery effort by capturing real-time user activity across applications and enriching it with natural language feedback from employees. This results in rich visual maps, SOPs, and requirements grounded in reality—not assumptions.
Process mining is a data analytics technique used to reconstruct and analyze how digital processes unfold based on event logs from enterprise systems like ERP, CRM, or HR platforms.
Core capabilities include:
It’s particularly effective in highly digitized, structured workflows like procure-to-pay, order-to-cash, or IT ticket resolution—where most of the process takes place within a single system.
While process mining offers powerful insight into how data flows through systems, it leaves out a critical piece of the transformation puzzle: the human experience.
Here are key limitations:
Process mining shows what happened in the system, but not what the user experienced leading up to, during, or after that step. It can’t see:
Most work doesn’t happen in a single system anymore. A sales rep may jump between Salesforce, Gmail, Notion, and Excel. Process mining won’t capture activity that happens outside the core system where logs are generated.
Process mining can show that something took too long—but not why. It doesn’t capture user frustration, missteps, or repeated tasks that don’t leave a digital footprint in system logs.
In many departments (e.g., HR, procurement, customer service), work happens through conversations, spreadsheets, browser apps, or ad-hoc actions. These don’t generate the structured logs process mining tools rely on.
Most process mining tools fail to capture the full scope of human-driven processes in hybrid digital environments.
If you’re relying only on process mining, you’re seeing the “what”—but not the “how” or “why.” You may know where delays occur, but not what’s causing them. To design effective transformation strategies, you need visibility into both system execution and human behavior.
✅ Process discovery fills that gap—providing the ground truth of how work actually gets done across apps, teams, and workflows.
👉 Want to move beyond logs and start understanding what’s really happening in your organization? Contact us to see how ClearWork gives you full visibility into real user workflows—without the guesswork.
Process discovery captures how work actually happens by recording both system interactions and human behavior—including workarounds, manual handoffs, and multi-app workflows. Process mining, on the other hand, analyzes system log data from structured platforms like ERP or CRM to show how digital transactions flow. In short: discovery uncovers the human side, while mining analyzes the system side.
Process discovery is best when processes are undocumented, inconsistent, or span multiple tools and teams. It’s especially useful for identifying inefficiencies, preparing for automation, or planning digital transformation. Modern process discovery software like ClearWork automatically captures user activity across applications and enriches it with employee context, creating accurate workflow maps and SOPs.
Process mining excels in highly digitized, structured workflows that live within one system—for example, procure-to-pay, order-to-cash, or IT ticket resolution. By analyzing system logs, it reconstructs digital process flows, measures performance (cycle time, throughput, deviations), and helps benchmark execution against SOPs. It’s most effective when most of the process takes place inside a single enterprise system.
While powerful, process mining leaves out key elements:
Yes—organizations gain the most value when combining both. Process mining provides structured, system-level insight, while process discovery captures human-driven activities across applications. Together, they deliver a complete picture of “what happened” and “why it happened,” ensuring transformation strategies are grounded in both operational data and real user experience.
Whether you are looking at a highly structured process or a completely custom and variable process, it's clear that traditional approaches of interviewing stakeholders to understand a process doesn't work. You need to look at how work ACTUALLy gets done, in order to have a solid baseline to plan for the future. Let's chat to see what toolset will work best for you.
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