Understanding employee experience with Spark
Spark is a conversational IT agent that serves as the first point of contact for employees when they need IT assistance.
Unlike traditional chatbots that rely primarily on predefined scripts or static knowledge bases, Spark follows an agentic approach. It can reason over employee context and execute IT-approved actions to diagnose and remediate issues.
Spark leverages extensive device context and Digital Employee Experience (DEX) data, enabling more accurate analysis and targeted resolution. By combining contextual awareness with action capabilities, Spark accelerates issue resolution and reduces the need for manual IT intervention.
Spark is accessible through a supported workplace communication interface, providing support within a familiar environment.
How Spark works
Spark connects with employee requests through the communication channel, runs a diagnosis, and attempts issue resolution.
The Spark workflow consists of the following steps:
The employee submits a request through a configured communication channel, currently only available for MS Teams.
Spark interprets the employee request in natural language. Depending on the employee request, Spark gathers and evaluates:
Nexthink datasets for the specific user/device diagnosis—limited to the user's own device.
Available actions—built-in agent actions and custom remote actions—for diagnostics or remediation.
Spark responds to the employee by sharing answers to employee questions or potential solutions to resolve their issues. Spark can either:
Provide self-help guidance or detailed information, including links to related knowledge base articles.
Request employee authorization for automated resolutions of device issues.
If unresolved, Spark escalates the support request to the service desk with full context. Spark only escalates requests in the following cases:
After exhausting relevant automatic actions and user troubleshooting
Receiving an explicit escalation request from the employee
Running into issues that require administrative access that the employee does not have
Encountering technical limitations that prevent Spark from providing an effective solution
Spark may suggest and initiate resolution measures, but all device remediation actions require user approval.
Context and data inputs
To provide relevant responses, Spark uses a combination of static and dynamic data sources:
Knowledge base articles: Manually imported knowledge base articles.
Contextual Nexthink data: Device health, diagnostics, remediations and user metadata from Nexthink Infinity.
Consequently, Spark relies on specific NQL data model tables to query Spark-user interaction data.
Personal data handling is covered under the Nexthink Data Processing Agreement (DPA). Spark processing is user-specific and restricted to the customer region.
Spark never provides data from other organizations.
Spark communication channels
Currently, Nexthink Spark integrates with Microsoft Teams to deliver secure, AI-powered assistance for IT troubleshooting and employee requests.
Log in to Nexthink Community to read more.
Spark Actions
Spark can perform actions to diagnose and remediate employee issues. These actions are enabled by Nexthink administrators after IT approval. Before executing a remediation action, Spark requests confirmation from the employee. Built-in diagnostic actions are excluded from this requirement and may run in the background without interrupting employee work.
Refer to Managing Spark actionsfor more information.
Supported languages
Spark detects the employee’s language from message content and delivers responses in that language, allowing employees to interact with Spark in their native language.
If the language cannot be detected or is not supported, Spark falls back to the tenant language, currently English or Japanese, and informs the employee accordingly.
Escalated tickets are generated in the tenant language, English or Japanese. They include a short description, actions taken, and the conversation transcript. Additional fields may contain the same content in the original employee language.
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