Nexthink's AI layer transforms what the platform can do with the data it collects. Before AI, the volume of endpoint telemetry was as much a problem as an asset. Three dedicated AI agents, Spark, Workspace, and AI Drive,built natively on that telemetry, change that. Here's what each one does, how it works in practice, and what you need in place before the AI features deliver value.
Context
Nexthink has always excelled at collecting endpoint data. The challenge was always what to do with it. A platform sampling 20 million events per day across 50,000 devices produces more signal than any IT team can manually review. Most of it went unread. Analysts worked reactively: responding to alerts, following up on tickets, investigating problems users had already complained about. The AI layer changes the flow from reactive to proactive.
The personal IT agent for every employee: autonomous L1 resolution via Microsoft Teams
Spark is the most visible part of Nexthink's AI strategy and the one with the clearest, most measurable impact. It is a fully autonomous AI agent that employees interact with through Microsoft Teams or Microsoft Copilot. When an employee has an IT problem, slow laptop, application not opening, VPN issues, printer won't connect,they message Spark, and Spark diagnoses and fixes the problem without escalating to the service desk.
What makes Spark different from a chatbot or a knowledge base is that it has real-time access to the employee's device telemetry through the Nexthink Collector. It doesn't just suggest generic troubleshooting steps: it reads the actual state of that specific machine: what processes are running, what's consuming CPU, what the network quality is, whether a specific service has failed. The diagnosis is precise because the data is precise.
After diagnosing the problem, Spark executes a fix using Nexthink Remote Actions: IT-approved scripts that run on the endpoint. "IT-approved" is the critical phrase: Spark only executes actions that have been explicitly pre-authorized by IT through the Infinity platform. IT teams define the remediation library Spark can draw from. Spark cannot take actions outside that approved set, which means IT retains full control of what the AI does on managed devices.
In Microsoft Teams or Copilot, the employee describes the problem in natural language: "My laptop has been really slow since this morning." No ticket required, no hold queue.
Spark queries the Nexthink Collector on the employee's specific device: checking CPU load, memory usage, running processes, recent crashes, network quality, and application performance in real time.
The AI correlates the telemetry against known patterns to identify the most likely root cause. It may ask one clarifying question if the diagnosis is ambiguous, but typically acts directly on what the data shows.
If a pre-approved remediation action exists, Spark runs it on the endpoint. The fix takes effect silently in the background: clearing a stuck process, restarting a service, flushing DNS, reconfiguring a setting. Average resolution time: under 2 minutes.
Spark tells the employee what it found and what it did. If the fix didn't resolve the issue or no approved action covers the problem, Spark hands off to the service desk: with full diagnostic context already documented, reducing the agent's resolution time.
The IT AI cockpit: natural language investigations, proactive DEX insights, and guided actions in one AI-native space
Workspace is Nexthink's AI-native environment for IT teams. It replaces the fragmented workflow of separate dashboards, NQL written in isolation, and manual correlation with a single conversational interface: ask a question in plain English, Workspace translates it to NQL, runs the investigation, and returns results with a visualization already built. It also surfaces proactive insights without being prompted, flagging anomalies, correlating root causes across the full fleet, and generating diagnostic cards that shift the analyst's role from detection to triage.
The distinction between Spark and Workspace is the audience: Spark is the IT agent every employee interacts with. Workspace is the AI cockpit that IT analysts, DEX program managers, and service desk leads use to investigate, act, and stay ahead of problems at the fleet level.
Discover, govern, and measure enterprise AI adoption. Powered by AI Drive.
AI Activation Hub is Nexthink's end-to-end platform for managing AI in the enterprise. Where Spark automates resolution for employees and Workspace gives IT a conversational cockpit, AI Activation Hub answers the organizational question: what AI tools are actually running across the enterprise, who is using them, are they sanctioned, and is the experience good enough to justify the investment?
It operates across five disciplines: Discover (find every AI tool in use, including unsanctioned shadow AI detected via browser extensions and connectors), Assess (measure adoption depth, usage frequency, and experience quality), Govern (enforce AI usage policies and flag compliance risks), Enable (deliver in-the-moment guidance to employees through Nexthink Guides), and Measure (report AI ROI to leadership with data procurement and finance can act on).
Implementation Guidance
The AI layer is compelling, but it sits on top of a data and operational foundation. Organizations that attempt to implement Spark before that foundation exists will get a fraction of the value. These are the prerequisites worth getting right first.
Spark and Workspace require the Collector deployed to a high percentage of managed devices: ideally 95%+ coverage. An AI agent that can't see a third of your fleet will miss a third of the problems and produce skewed analytics. Coverage gaps are the most common reason AI features underdeliver at launch.
Spark's autonomous resolution capability depends entirely on having pre-approved remediation workflows available through Nexthink Flow. If your Flow library is empty or minimal, Spark can diagnose but cannot fix: and its value proposition collapses to an expensive chatbot. Build and test your remediation library before deploying Spark to employees.
Every Remote Action Spark can execute must be reviewed and approved by IT. This is not just a technical step: it requires a governance conversation about what actions are safe to run autonomously, what guardrails are needed, and how to test in a staging environment before production rollout. Budget time for this process.
Spark surfaces to employees through Microsoft Teams or Copilot. If your organization doesn't have Teams deployed or if the Nexthink app hasn't been provisioned in your Teams tenant, Spark has no delivery channel. The Teams app deployment and configuration is a prerequisite, not a post-launch step.
Workspace's proactive diagnostic insights improve significantly with historical data to establish baselines. A fresh deployment with two weeks of data will produce more false positives than a mature deployment with six months of baseline. Plan for a maturation period before relying heavily on Workspace's automated root cause analysis for operational decisions.
Employees need to know Spark exists and trust it enough to use it. Adoption of Spark as a support channel is a change management challenge as much as a technical one. A rollout without employee communication, explaining what Spark is, what it can and can't access, and how to use it,typically produces low initial adoption regardless of technical quality.
Continue
Spark and Flow are tightly connected: Spark's autonomous resolution capability runs on remediation workflows built and governed in Nexthink Flow. Understanding Flow in depth is the next logical step for teams planning a Spark deployment. Amplify is also worth exploring early: it surfaces the same Nexthink data and remediations inside your ITSM tool for escalated tickets Spark can't resolve autonomously.
Statistics and technical details on this page are sourced from Nexthink official documentation, press releases, and independent industry research. View full references →