You know that many business processes are ripe for automation. But how do you find the best opportunities? Guessing which workflows are inefficient is slow and often wrong. To truly optimize, you first need to see how work actually gets done, not just how it's supposed to get done on paper.

This is where automated process discovery comes in. It acts as the 'eyes' of your automation strategy, using data to build an objective picture of your operations. Two key techniques provide this vision: Process Mining and Task Mining.

Imagine you want to understand your entire order-to-cash process. It spans multiple departments and systems like Salesforce for sales, SAP for order management, and an Oracle database for finance. Process mining tools connect to these systems and pull their event logs—digital records of every action taken.

The software then analyzes these logs to create a visual process map. This isn't a theoretical flowchart; it's a data-driven diagram showing all the different paths, or variants, a process can take. It highlights the most common routes, the bottlenecks where work piles up, and the costly exceptions that deviate from the standard procedure.

This map gives you a powerful diagnostic tool. You can see precisely where delays occur, which steps are being reworked, and how often non-standard workflows happen. It's like having an x-ray of your business operations.

Process mining is great for understanding system-to-system handoffs, but what about the work that happens on a single user's desktop? An employee might spend hours copying data between an Excel sheet and a legacy application that doesn't generate neat event logs. Process mining would miss this entirely.

Task Mining fills this gap. It uses software installed on employee desktops to capture user interface (UI) interactions: clicks, keystrokes, copy-pastes, and application switches. It then uses AI to analyze this raw data and identify repetitive tasks that make up a larger workflow. For example, it might discover that 20 different employees are all following the same 15-step process to generate a weekly report.

Combining these two discovery methods gives you a complete, multi-layered view of your processes. You can build a digital twin of your operations—a dynamic, virtual model that mirrors how your business actually runs. This model becomes the foundation for making smart automation decisions.

With this data, calculating the potential Return on Investment (ROI) for automating a complex workflow moves from guesswork to data science. You can quantify the exact time spent on each manual step across the organization. You can see the frequency of inefficient process variants and calculate the cost of those deviations. This allows you to prioritize automation projects based on their real, measurable impact.

Sometimes, the data reveals that a process is too chaotic to automate directly. If you have dozens of variants with no clear 'happy path,' simply automating the most common one might not solve the underlying problem. This leads to a critical decision point: automate or re-engineer?

If a process has a few well-defined, high-volume variants, it's a great candidate for automation. But if it's plagued by inconsistency and exceptions, you may need to perform process re-engineering first—simplifying and standardizing the workflow before you apply automation. Process discovery data is what allows you to make that call with confidence.