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, action capabilities, and learning from resolved incidents, 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:
Employee initiates conversation
The employee submits a request through a configured communication channel.
Spark interprets the employee inquiry
Spark interprets the employee message using natural language processing.
If the inquiry is ambiguous, Spark initiates a conversation planning phase consisting of the following steps:
Acknowledge: Spark acknowledges the request and shows empathy.
Clarify: If the request is vague or ambiguous, Spark enters plan mode and asks concise questions.
Hypothesis: Spark shares a hypothesis and next steps based on the information provided.
Spark evaluates data
Based on the employee message, Spark gathers and evaluates the following data sources:
Nexthink datasets for user or device diagnostics, limited to the employee's own data.
If an employee uses multiple devices (desktop, laptop, or virtual desktop), Spark identifies the relevant device based on context and asks for confirmation. It can list up to 3 most recently used devices.
Service catalog ingested from ServiceNow
Past ticket resolution data from ITSM.
Available actions, including:
built-in agent actions
custom remote actions or workflows for diagnostics or remediation.
Nexthink Adopt application guides
Curated list of trusted websites
Spark provides a response
Spark responds to the employee by providing answers or potential solutions. Depending on the situation, Spark can:
Provide self-help guidance or detailed information
If no automatic remediation or service request is required or no relevant remediation actions have been made available through configuration, Spark provides guidelines to help the employee address the need. This guidance may include the following resources, relevant to the employee’s request:
Links to related knowledge base articles
Links to Adopt guides, displayed directly in the target application as step-by-step overlays.
Shared links redirect the employee to the relevant web application page
The guide launches automatically. Refer to Creating guides to make guides available for Spark.
Spark only shares resources that meet visibility conditions applicable to the employee.
Handle service request
When an employee’s question indicates a need to submit a service request, Spark searches the service request catalog to identify the most relevant option. It then recommends the appropriate request, providing a direct link along with clear submission guidelines to help the employee complete the process quickly and accurately.
Spark follows up on the request
Spark then communicates the outcome, confirms whether the issue is resolved, and asks the employee if further assistance is needed.
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:
Contextual Nexthink data and capabilities: Spark has access to the data available through the standard NQL data model. This includes device health, diagnostics, user metadata, remediations, Adopt guides, and newly released capabilities. Spark also has access to most of the dynamic data model, such as custom fields and data generated by remote action executions. Spark only accesses data relevant to the employee it is interacting with.
Knowledge base articles: Knowledge base articles manually imported from the ITSM or ingested via the ServiceNow Knowledge Base connector.
Service request catalog: Catalog structure, forms, and request metadata ingested from ServiceNow, which Spark can use to identify and recommend service requests and provide associated guidelines. Refer to ServiceNow Request Catalog connector for more information.
Local endpoint data: Real-time data retrieved from the user’s device via the Nexthink Collector to support accurate, context-aware troubleshooting.
Past ticket resolution data: Resolution notes for incidents resolved by support agents. Spark can use them to suggest remediations and continuously reduce manual interventions and escalations. Refer to ServiceNow Tickets connector for more information.
Curated list of trusted websites: If enabled, Spark can search a curated list of trusted web domains when internal knowledge sources do not provide sufficient guidance. This capability helps Spark resolve a broader range of issues autonomously. Refet to Web search for more information.
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
Employees can access Nexthink Spark through Microsoft Teams, Copilot or through existing enterprise chat entry points integrated with Spark. This gives employees flexibility to start IT support conversations from the tools and conversational interfaces they already use.
Spark in Microsoft Teams and Microsoft 365 Copilot
Within Microsoft Teams and Microsoft 365 Copilot, employees can open Spark from the Applications or Agents panel and start a conversation to receive secure, AI-powered assistance.
Log in to Nexthink Community and read more about Spark Microsoft Teams and M365 Copilot app security.
Spark in enterprise AI agents
Seamless integration with AI agents (A2A)
Organizations using enterprise AI agents can integrate them with Spark through the Agent-to-Agent (A2A) protocol. With A2A, employees can start and continue their support journey in their existing chatbot while the agent discovers, invokes, and tracks Spark capabilities in the background. This allows Spark intelligence and Nexthink context to support the employee experience without disrupting the conversation flow.
Refer to Spark Agent2Agent integration for more information.
Chatbot-to-Spark handoff
Employees can also initiate their IT requests through a supported enterprise chatbot. If the issue requires advanced support, the chatbot transfers the conversation to Spark using the configured Handoff API. The full context of the interaction is preserved, allowing employees to continue the resolution seamlessly in Microsoft Teams with Spark.
Log in to Nexthink Community and read more about Spark Handoff API.
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 actions for more information.
Supported languages
Spark detects the employee’s language from message content and delivers responses in that language, enabling employees to interact in their preferred language across most LLM-supported languages.
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.
Last updated
Was this helpful?