AI Applications for Task Mining

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Introduction

Task mining serves as a critical complement to process mining, offering a comprehensive view of an organization's workflows. By delivering data-driven insights into user behaviors and tasks, it enhances the process transformation lifecycle, facilitating a deeper understanding, optimization, and continuous improvement of business operations.

SAP UEM by Knoa’s task mining solution plays a pivotal role in AI-driven enterprise strategies. Below are several ways organizations can leverage Knoa task mining as part of their AI strategy:

• AI Insights from Combined Process and Task Mining Data
• Conversational AI for Knoa Analytics
• Measuring AI Adoption
• Task Mining Data as Input for AI Models

 

AI Insights from Combined Process and Task Mining Data

Knoa task mining seamlessly integrates with SAP Signavio process mining, augmenting Signavio process level data with task level information. By capturing task related metrics, such as errors, active and idle time, response time, and task sequences, Knoa enhances process analytics with empirical data on usage patterns and task level information.

With this integration, organizations gain valuable insights into:

• How employees interact with SAP ERP and CRM systems to execute a business process, front to back
• The additional applications users engage with during specific business processes
• Task efficiency, user proficiency, and user compliance

Since Knoa’s task mining data is accessible from within the SAP Signavio Process Intelligence product, business analysts can leverage SAP Signavio AI features, such as the AI-Assisted Process Analyzer “text to insights” and “text to widget” capabilities. The seamless integration of task mining data with Signavio’s AI democratizes process analytics, making insights accessible to business users without requiring deep technical expertise.

The AI-Assisted Process Analyzer’s text to insights capability allows business users to define the scope of their process analysis with a simple question in natural language, delivering relevant automated insights as a result. This AI capability significantly accelerates time to value by providing immediate, out-of-the-box insights from process data, without requiring users to have in-depth knowledge of the data structure or query writing skills. The AI-Assisted Process Analyzer capability also includes additional features such as text to widget, which allows users to create widgets in the dashboards using an AI natural language prompt. Both “text to insights” and “text to widget” are released in open beta.

SAP Signavio Process Analyzer

By combining Knoa task mining with SAP Signavio, organizations can empower users of all skill levels to uncover process improvement opportunities related to user proficiency, adoption, and compliance. This supports business process transformation initiatives, ensuring AI-driven process enhancements deliver measurable value.

Conversational AI for Knoa Analytics

Knoa integrates conversational AI within its own analytics platform, leveraging Tableau Pulse to deliver real-time, actionable insights into enterprise application and user performance. AI-driven interactions empower business and IT users to:

• Identify business process inefficiencies and bottlenecks
• Pinpoint automation opportunities at the task level
• Monitor user productivity and adoption over time and spot trends and other patterns

Tableau Pulse

Tableau Pulse provides contextualized insights on top of the role-based analytics delivered by Knoa, enriching the user experience for the business analysts who interact with Knoa Analytics. It helps drive smarter and faster decision-making, as part of digital transformation initiatives. Conversational AI for Knoa Analytics is available in open beta.

Measuring AI Adoption 

AI co-pilots are transforming enterprise environments by enhancing productivity, streamlining workflows, and automating routine tasks. These AI-powered assistants enable employees to summarize information, generate content, and access real-time insights, allowing them to focus on strategic initiatives.

Organizations are integrating AI co-pilots into enterprise applications such as CRM, ERP, and collaboration tools, ensuring seamless adoption while minimizing workflow disruptions. Additionally, structured training programs and governance frameworks help maximize AI effectiveness while ensuring security and compliance.

Knoa task mining serves as a vital tool in AI adoption strategies, enabling organizations to measure AI co-pilot usage, track adoption rates, and quantify the impact on productivity and business outcomes.

SAP Joule transforms user interactions with SAP systems by enabling direct navigation, conversational search, contextual insights, and more. As employees begin to rely on AI co-pilots as part of their daily work, Knoa task mining empirically measures these behavioral shifts, to further guide AI development and refinement.

Aside from ensuring AI adoption, Knoa Task Mining can also help organizations identify tasks and processes that can benefit from AI-powered chatbots and automation.

SAP AI Co-Pilot Joule

Successful AI transformation goes beyond chatbot implementation - it requires widespread and effective adoption. Knoa task mining provides organizations with visibility into user engagement, ensuring AI technologies deliver real business value. Moreover, as an application-agnostic platform, Knoa Task Mining can be used with various AI co-pilots, including Salesforce Einstein, Google Gemini, and Microsoft Copilot.

Task Mining as Input for AI Models

Knoa task mining provides crucial behavioral data that can enhance AI models. It generates intelligent, context-aware data about user interactions across CRM, ERP, and other enterprise applications. By leveraging these insights, AI co-pilots can deliver highly relevant recommendations and automation to end-users.

SAP AI Application for SAP Sales Order Management

Here are some examples of how Knoa task mining can augment various SAP AI co-pilot applications:

• SAP Sales Order Management for S/4HANA Cloud: AI recommends missing field entries in sales orders based on historical data. Knoa task mining can enhance this by providing more insights into user’s own past experience, helping improve relevancy and accuracy of recommendations.
• Joule for SAP S/4HANA Cloud Public Edition: Joule guides users through optimized workflows and offers contextualized assistance. Knoa task mining can assist in creating dynamic user profiles based on real behavior patterns, further personalizing the user’s experience.
• SAP Business Integrity Screening: This AI-driven solution detects unusual transactions and fraud. Knoa task mining can enhance the compliance capabilities by generating detailed audit trails of system access at both the screen and field level, increasing the resolution of compliance checks.

Conclusion

By leveraging Knoa task mining within their AI strategy, enterprises can unlock deeper insights, drive AI adoption, and optimize operational efficiency. As AI technology continues to evolve, use of task mining technology in conjunction with AI can help companies drive better adoption of AI, deliver more user relevant context through AI applications, and create more intelligent recommendations and automation for end-users.