Data Insights

Trend Identification

Applicable Channels: Web, MS Teams

Enterprise IT's copilot identifies trends of historical ticket data by leveraging machine learning algorithms and natural language processing (NLP). Copilot uses the AI system aggregating large volumes of past ticket information, which includes details such as issue types, resolution times, affected systems, and customer feedback. By employing machine learning algorithms, Copilot can sift through this data to detect recurring patterns that might be indicative of broader trends.

Additionally, natural language processing (NLP) capabilities enable Copilot to understand the nuances in ticket descriptions and categorize them accurately. This further enhances trend analysis.

The insights gained from this trend identification process can inform decision-making for support teams, allowing them to prioritize and address systemic issues more effectively. This not only improves the efficiency of service management operations but also enhances overall user satisfaction by reducing downtime and improving response times.

Below sample screenshots showcase the Trend Identification capability of Copilot on MS Teams:

User input: Do you see any trend based on the last week Incidents? 

Copilot response:

Figure: Trend Identification sample

User input: Any open tickets with negative sentiment in its symptom or description?

Copilot response:

Figure: Trend Identification

Root Cause Analysis

Applicable Channels: Web, MS Teams

Enterprise AI's Copilot enhances Root Cause Analysis (RCA) by systematically identifying and resolving the underlying issues of Service Requests or Incidents. Leveraging machine learning algorithms and natural language processing (NLP), Copilot analyzes vast amounts of historical data, logs, and previous incident reports to detect patterns and correlations. It filters through this information to pinpoint commonalities and anomalies associated with the reported problem. Once potential root causes are identified, Copilot evaluates them against known solutions and best practices.

By doing so, it not only narrows down the probable cause but also suggests effective remedies. Throughout this process, Copilot continuously learns from new data, refining its analytical capabilities and improving its accuracy in future RCA tasks. This data-driven approach accelerates the resolution process, minimizes downtime, and enhances the overall efficiency of service management operations.

Below sample screenshots showcase the Root Cause Analysis capability of Copilot:

User input: 'Any priority 1 incidents last week?'

Copilot response:

Figure: RCA Analysis - Web

User input: 'Can you summarize the incident INC00**6 with reported problem and solution?'

Copilot response:

Figure: Copilot RCA Analysis - Web

Recommendation System

Applicable Channels: Web, MS Teams

Enterprise IT's Copilot enhances Service Management by providing resolution recommendations based on the analysis of similar tickets and their resolutions. Utilizing machine learning algorithms, Copilot scans a vast database of previously logged tickets. It identifies patterns and commonalities in the issues reported. By examining the context and outcomes of these past tickets, Copilot suggests the most effective solutions for current problems.

This process not only speeds up the resolution time by presenting relevant information and potential fixes to the support agent but also ensures consistency and accuracy in handling recurring issues. Copilot acts as an intelligent assistant, streamlining the support workflow and improving overall service efficiency.

Below sample screenshots showcase the Recommendation System capability of Copilot:

User input: 'Any tickets related to 'data loss due to system crash' historically? If yes, can you state the resolution provided?'

Copilot response:

Figure: Copilot Recommendation System - Web

User input: 'Any tickets related to 'Application login issue' historically? If yes, can you state the resolution provided?'

Copilot response:

Figure: Copilot recommendation system - MS Teams

Predictive Analysis

Applicable Channels: Web, MS Teams

Enterprise IT's Copilot provides predictive analysis by leveraging advanced algorithms and vast datasets to forecast future trends and outcomes. By analyzing historical data, Copilot identifies patterns and correlations that may not be immediately evident to Analysts. This involves processing large volumes of information in real-time, using machine learning models to predict potential issues, opportunities, or changes in various scenarios.

Below sample screenshots showcase the Predictive Analysis capability of Copilot:

User input: 'What are the average daily usage of incidents in the last 1 week and use this to predict the approximate counts for next week?'

Copilot response:

Figure: Copilot Predictive Analysis

Generate Charts

Applicable Channels: Web, MS Teams

Copilot's users have the ability to create dynamic charts - such as pie charts, bar charts, and line charts - based on response data. These charts are automatically determined by the system, but users also have the option to specify the type of chart they want, such as explicitly requesting a bar chart or a pie chart. Importantly, these dynamically generated charts are tailored specifically to the data at hand and are not part of the standard, pre-built ITSM reports. This functionality allows for more flexible and customized data visualization, providing insights that are directly relevant to the user's specific needs and queries.

Below examples and screenshots showcase how you can generate charts through Copilot:

User input:

1^st input - 'Show me top 5 P3 running incidents with its logged date in last 3 week'

Copilot response:

Figure: Copilot response

2^nd input: 'Using this data can you generate a chart and group it by status?'

Copilot response:

Figure: Generated charts

Anomaly Detection

Applicable Channels: Web, MS Teams

Anomaly Detection capability of Copilot spots unusual patterns in data or behavior. It automatically finds odd activities or issues that might suggest mistakes, fraud, or new problems, helping organizations fix them early. By keeping an eye on data all the time, this capability of Copilot helps keep systems running smoothly and assists in decision-making by pointing out important anomalies that need to be looked into.

Below examples and screenshots demonstrate Anomaly Detection capability of Copilot:

Figure: Anomaly detection

Workload Management

Applicable Channels: Web, MS Teams

Copilot allows Workload management by providing insights into how tasks and resources are allocated. It provides real-time data on task distribution, employee productivity, and resource use, helping teams balance their workload effectively. By highlighting overburdened areas and underutilized resources, Copilot enables better planning and prioritization of workload.

Below examples and screenshots demonstrate how Copilot helps in Workload management:

User input: What is the average tickets resolved by analyst abc.xyz@email.com last 3 days and using this tell me how much he can resolve it in next 2 days?

Copilot response:

Figure: Workload management