How do you decide which tasks in your business need automation the most?

With so many moving parts, identifying inefficiencies can feel like finding a needle in a haystack. That’s where task mining steps in. A smart way to analyze processes and pinpoint where automation can create the biggest impact. But here’s the twist: what if the solution didn’t stop at identifying tasks, but also executed them? Enter AI agents.

In this blog, we cover how task mining finds inefficiencies in workflows, while AI agents automate repetitive tasks. We also look at how Beam AI integrates these technologies to help businesses optimize processes and enhance productivity.

Overview of Task Mining

Task mining is a systematic and data-driven approach that captures, analyzes, and understands user interactions with digital systems and applications. By leveraging techniques such as process discovery and analysis, task mining uncovers patterns, bottlenecks, and variations in user workflows, ultimately contributing to enhanced process optimization and automation efforts.

Task Mining vs Process Mining

In understanding an enterprise, there are two key data types: business data and user interaction data.

• Process Mining

Process mining focuses on business data contained in transactional systems as time-stamped event logs. These logs detail every step of a business process, including deviations and when they occur, capturing real-time data like the creation, approval, and fulfillment of purchase orders. This method not only tracks processes but also identifies opportunities to enhance their value.

• Task Mining

Task mining analyzes user interaction data, which includes the actions employees take between process steps, such as filling out purchase orders, verifying figures in Excel, or matching invoices. It monitors activities like clicks, copy/paste actions, and time spent in applications to understand team workflows and discover ways to boost productivity and employee experience.

How Task Mining Works

Task mining involves capturing and analyzing user interaction data within business software applications. This data typically includes mouse clicks, keystrokes, and navigation paths, which are collected through software installed on the employee's computer or screen recordings of business processes in action. The primary goal of task mining is to identify repetitive and rule-based tasks that are candidates for automation.

Task mining tools follow a systematic approach to uncover inefficiencies in workflows. Here's a closer look at the steps involved:

Step 1: Monitor and Collect Users Data

Task mining operates in the background on user desktops, gathering data on their interactions with various applications. It tracks activities such as mouse clicks, application usage, keyboard inputs, copying and pasting, scrolling, and the time spent on different tasks. Some methods may also capture screenshots or recordings to provide visual context to the collected information.

Many task mining tools utilize technologies like vision models to extract text from screenshots or recordings. This information enables the system to link specific user actions with the corresponding tasks, offering a clearer understanding of the workflow.

Step 2: Discover and Group Similar Tasks

Task mining focuses on identifying individual tasks rather than entire processes. Here, a task refers to an action performed by a user as part of a broader process where it makes sense of the collected data all while grouping similar activities.

Step 3: Find Inefficient Business Processes

Advanced algorithms analyze the data to identify recurring tasks, such as common data entry methods or frequent error resolutions. The system also recognizes the unique paths different employees take to complete tasks, providing valuable insights for process enhancement.

Step 4: Auto Agent Setup

Based on the analysis of user tasks and business process inefficiencies, an automation step is introduced. This step involves setting up an AI agent automatically, which can help automate or optimize the identified tasks.

Automatically Setting Up AI Agents with Task Mining

The integration of AI agents with task mining focuses on creating executable agent graphs that agents can follow. The key steps involved are:

1. Task Mining Results

   The outcome of task mining typically results in the creation of a Standard Operating Procedure (SOP) or a detailed business process model. This provides a clear framework for understanding the tasks to be automated.

   
   

2. Create Agent Graph

   Using the SOP or business process model, a structured agent graph is derived. This graph serves as the blueprint for the AI agent's behavior, detailing the precise steps, conditions, and reasoning logic it needs to follow. This structured workflow is essential for ensuring that tasks are automated efficiently and without disruption.

   
   

3. Refine the Agent

   After the initial setup, the AI agent is trained on a relevant dataset that includes example executions. This training phase ensures that the agent can effectively perform the tasks, adapt to variations, and improve its performance over time.

Real World Example

Consider a customer service team overwhelmed with inquiries such as subscription requests or product return queries. The process begins with task mining, which collects detailed user interaction data from across customer service applications and systems without disrupting the humans' workflow. By observing how humans handle emails, categorizing inquiries, drafting responses, and retrieving information, task mining identifies inefficiencies such as excessive time spent on repetitive tasks or frequent errors in data retrieval.

Using the data collected, task mining software analyzes and maps out structured workflows that outline the exact steps required to handle inquiries effectively. This involves creating a process graph that details key actions like identifying the inquiry type, extracting relevant customer details, and drafting a response. The process graph highlights dependencies and decision points, transforming raw data into a clear (SOP). These insights form the foundation for designing an AI agent automation.

After the workflow is defined, the AI agent is automatically configured to perform the tasks. It is trained on a dataset that includes example executions, allowing it to understand various customer inquiries and responses. Once deployed, the agent’s performance is continuously monitored, with feedback gathered from real-world interactions to help it adapt and improve over time.

When a customer sends an email, the AI agent identifies the nature of the inquiry, retrieves relevant customer details like the name and order information, and categorizes the request. Leveraging insights from task mining, the agent uses large language models (LLMs) and internal system integrations to generate a contextually appropriate response. The agent then sends the response back to the customer, ensuring timely and accurate communication.