COMMENTARY: When you ask an MSP what their biggest pain point is, they’ll tell you it’s dispatching on the service desk. If dispatching is working well, everything runs smoothly. But when it’s not, the whole operation grinds to a halt. It’s a make-or-break issue. That’s why AI-driven automation sounds so appealing – it promises to streamline dispatch, reduce overhead, and free up technicians for more valuable work.
But there’s one big problem: Confidence. MSPs hesitate to trust AI because they know the risks of getting it wrong. When a ticket is misclassified or dispatched incorrectly, it has a ripple effect that disrupts workflows, frustrates technicians, and ultimately impacts customer service. The concern isn’t just that AI can be wrong. It’s that it can be wrong with confidence. And that’s a serious problem if not managed correctly.
So how can MSPs build confidence in AI? How do they separate marketing hype from real, reliable automation? The key is control, visibility, and a measured, data-driven approach.
Understanding AI Confidence: What it Really Means
Before an MSP can automate triage and dispatch, they need to understand the ticket itself. This is what a human dispatcher does, reading the request, analyzing it, and making decisions based on experience. Is there enough information? Does the request need approval? Can it be assigned to a technician right away? These are all questions that need to be answered before any action is taken.
AI does the same thing but with an added layer of complexity. Instead of relying on intuition, it classifies and categorizes tickets based on training data. The system calculates a confidence score – essentially, a rating that indicates how certain it is that a ticket belongs to a particular category.
Confidence in AI classification comes down to two factors:
The biggest risk? AI getting it wrong confidently. If a system is 90% sure that a request is a password reset when it’s actually a software installation issue, that confidence can lead to a misrouted ticket, unnecessary delays, and technician frustration.
AI Without Guardrails Is a Recipe for Disaster
Some MSPs use large language models (LLMs) like ChatGPT to classify tickets. The problem? These models weren’t designed specifically for MSP workflows. If an MSP simply plugs an LLM into their service desk without adding extra controls, they have no way to manage confidence levels.
A good AI system for MSPs is about more than classification. It’s about control. MSPs need to be able to:
Without these controls, an MSP is essentially rolling the dice on ticket accuracy.
Zero-Touch Automation
Zero-touch automation is when an AI system classifies a ticket and initiates an action without human intervention. This is where MSPs need the highest level of trust. While zero-touch automation can be highly efficient, MSPs must retain control over when and how it is deployed. Ideally, the AI system must be able to classify tickets with a high level of confidence before the automation is triggered.
The Role of Confidence in Automation
Confidence levels help determine how and when automation should be executed. MSPs should be able to adjust automation rules based on their risk tolerance and operational needs.
When confidence in a classification is low, an MSP may choose to automate only actions that carry minimal risk. For example, if someone logs an issue saying their computer is running slow, the AI can automatically connect to that person’s machine, run a diagnostics report, and bring that information back to the engineer. This is a non-disruptive action – nothing pops up on the user’s screen and nothing interrupts their work. The AI is simply gathering information in the background to assist the technician. Because no direct changes are being made, there’s no real risk in running this action, even when the confidence level in the classification isn’t high.
When the AI is highly confident in its classification, an MSP may choose to allow more impactful automation to proceed. For example, if someone can’t print because their print spooler service is off, the AI can automatically turn that service back on. This will disrupt the user, but only because the system was already broken – except now, they’ll be able to print again. MSPs have control over which actions run automatically and can decide the level of confidence required for different types of automation.
The key takeaway is that MSPs should always be in control of what’s running and when. They must be able to fine-tune these controls and have full visibility into how automation is working within their service desk.
Trust Is Built in Small Steps
MSPs don’t need to – and often shouldn’t – turn on all the AI automation options all at once. Confidence is built through small trials. The best approach is to:
MSPs who take this measured approach gain trust in the system over time, rather than expecting AI to be perfect from day one. And that’s the reality – no AI system is perfect. But with the right controls, visibility, and gradual adoption, MSPs can confidently use AI to improve efficiency without losing control.
Final Thought: The MSP Owns the Experience
At the end of the day, the AI tool isn’t responsible for customer satisfaction – the MSP is. If a customer is unhappy, they’re going to call the service desk and talk to a person, not an AI. That’s why MSPs need to drive the automation process, not the other way around.
The best AI solutions give MSPs complete control over automation, visibility into how decisions are made, and the ability to fine-tune settings based on real-world results. Confidence in AI doesn’t mean trusting the technology blindly. It’s about proving that it works and knowing that when things go wrong, the MSP has the power to fix it.
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