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Why AI-driven business process automation fails without analytics
AI transforms the way businesses operate by streamlining their business processes. According to the 2025 McKinsey report, “The State of AI”, 78% of organizations implement AI in at least one business function, which is 10% more than last year.
AI can help companies make workflows more productive, allowing employees to focus on value-driven activities. It’s already widely used for various purposes: from content creation to research and development. However, one of the most profitable application of AI lies in business process automation (BPA), as it allows companies to get repetitive manual tasks performed automatically, driving tangible outcomes.
Despite its potential, AI-driven automation can fail if businesses don't measure its impact accurately. To crack the code of a successful AI automation and maximize economic benefits from it, businesses need data-driven insights not assumptions. Detailed analytics offers clear visibility into workflows and lets you make informed efficiency-driven decisions.
Let’s dive into the intricacies of the AI role in business process automation and how Task Mining analytics can make AI-driven automation more efficient.
The problem with AI in business process automation (BPA)
The key to a successful BPA is having the right tools to assess processes both before and after automation. Without these tools, automation leads to inefficiencies and missed opportunities.
What can turn things around? Detailed analytics.
One of the most effective ways to maximize the value of business process automation is through Task Mining.
Task Mining – a key to smart AI-driven business process automation (BPA)
Task Mining is a technology that bridges the gap between unstructured business data and automation potential. Intelligent algorithms wired in the technology automatically extract business tasks from extensive user interaction data, group them to calculate labor costs and rank them by priority of automation. Data-driven automation insights allow you not only to see ‘as-is’ business tasks but also understand which of them can be automated and finally start informed automation initiatives.
Operavix Task Mining adoption: step-by-step guide
With Operavix you can calculate automation potential and maximize its benefits. In our previous article, we highlighted the importance of task efficiency with a case study on how a leading pharmaceutical company accelerated procurement process by 17% by precisely defining what to measure.
Now, let’s take a deep dive into how Operavix Task Mining helps companies achieve maximum task-level transparency and economic benefits step-by-step.
How Operavix Task Mining works
1. User interaction data collection
Task Mining Clients get installed on employees’ PCs to capture user interaction data.
2. Task extraction and grouping
Intelligent algorithms extract tasks and group the most repetitive and time-consuming tasks with the highest automation potential.
3. Actionable task-level insights
All the extracted tasks and task groups displayed in an interactive dashboard, so you base your automation decisions on key task efficiency metrics which show:
- number of tasks executed
- exact number of hours spent on task execution
- potential working hours saved
- FTEs optimized through task automation
- top-priority tasks for automation
Key benefits of Operavix Task Mining
- Automated discovery of employee user interaction data
- Accurate and data-driven automation insights into task execution
- AI-powered ranking of the most repetitive manual tasks
Ready to streamline business processes with task-level precision?
Visit our Customers page and learn how Operavix Task Mining helps businesses drive operational efficiency by delivering actionable task-level analytics.