AI processen MSP
Which MSP processes break when you add AI?
AI automation only strengthens processes that already hold up: garbage in, garbage out applies literally to MSP workflows. Work built on negotiation, trust and complex client conversations stays human, while the back office and first line support are changing fast.
MSP Late Night · 8 October 2026
Where does AI work in an MSP, and where does it not?
AI works in repetitive, data-rich processes such as ticket handling, logging and first line customer service. It does not work in processes that run on personal trust: negotiating the contract for the next project, legal coordination with lawyers and notaries, and escalations where the client relationship is at stake. The first dividing line now showing up among MSPs: AI-native competitors build around AI from day one, while established MSPs have to clean up their processes before automation returns anything at all.
If you automate a rotten process with AI, it is garbage in, garbage out.
Frequently asked questions
Why is garbage in, garbage out extra risky in MSP automation?
AI speeds up execution, not thinking. An MSP that automates a messy ticket process or unclear escalation paths gets the same mistakes, only faster and at a larger scale. The error also becomes harder to spot, because the system produces its output with complete confidence. The practical order is to document and clean up the process first, then bring in AI to make it scalable.
Which MSP processes are the first candidates for AI?
Processes with high volume, clear input and repeatable output: first line support, ticket classification, filtering monitoring alerts, log analysis and standard reporting. Opening up internal knowledge for engineers is a common first step as well. Processes where the context differs sharply per case, or where human judgement about the client relationship matters, belong later in the roadmap or stay out of scope.
What does AI-native mean for an MSP business model?
An AI-native MSP designs delivery, pricing and organisation around AI from day one. That means fewer staff per client, hourly billing disappearing from the model, and margins moving towards outcome-based contracts. Established MSPs have to run that sum in reverse: which services stay viable if a competitor delivers them for 30 to 50 per cent of the price with AI-driven delivery.
How do you tell whether a process is ready for AI automation?
A process is ready when it is documented, has measurable input and output, and a low exception rate. If engineers run it the same way every time and the quality can be checked, AI can take over the execution. If the process depends on tribal knowledge or personal judgement, standardising comes first, not automating.
The full conversation, transcribed
Niemand heeft het antwoord nog. Dus we zijn allemaal zoekende naar waar gaat het naartoe. En we zien nu wel de eerste politiciteit. Ik zie het bij onze eigen klanten en bij onze eigen customer service. Als je de proces namelijk via AI gaat automatiseren en je zit met een uit proces, dan is het garbage in, garbage uit. Precies. En wat je ziet is dat juist door AI processen veranderen. Maar in andere processen, als jij met een klant gaat onderhandelen over jouw volgende project, Er is geen AI die dat gaat doen. Dat zijn veel te persoonlijke, interactieve... En als je dan naar de truststuk gaat kijken, advocaten, notarissen... Ja, en dat is natuurlijk de grap van Salesforce, die ooit native cloud gebouwd was. En wij nu de transformatie moeten maken naar concurrenten die AI native zijn en wij niet.