# Where It Shows Up

### Introduction

AI features are now embedded across PSA, RMM, and specialized tools serving MSPs. Some add measurable value, others are rebranded automation. This section aims to compare what’s available, where it fits, and how to evaluate claims responsibly. 

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### **Example Implementation Categories**

<table><thead><tr><th width="143.48828125">Category</th><th width="180.53515625">Strength</th><th width="192.22265625">Limitation</th><th>Examples</th></tr></thead><tbody><tr><td><strong>General AI Assistants</strong></td><td>Flexible, cheap</td><td>No MSP-specific context, limited business logic</td><td>ChatGPT, Claude, Microsoft Copilot (often approved due to no-training policy)</td></tr><tr><td><strong>PSA-Native Features</strong></td><td>Built into existing workflows, access to client-specific data, bi-directional sync</td><td>Vendor lock-in, limited to single PSA ecosystem</td><td>Atera, ConnectWise, Autotask, Syncro</td></tr><tr><td><strong>Specialized AI Tools</strong></td><td>Fill gaps PSA/RMM don’t cover</td><td>Fragmented ecosystem, requires integration</td><td>Mizo AI, zofiQ, Neo Agent/Cooper Copilot, Rewst, N8N, DialPad, Nextiva, Riscosity, Augmentt, Auvik</td></tr></tbody></table>

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### **Example PSA Platform Comparison**

*Something missing or incorrect? Make an edit! \*Still needs sources added.*

<table><thead><tr><th width="126.27734375">Platform</th><th width="208.09375">AI Features</th><th width="138.66015625">Cost Model</th><th width="140.65234375">ROI Claims</th><th>Data Handling</th></tr></thead><tbody><tr><td><strong>Atera</strong></td><td>Diagnostics, script gen, ticket summarization, alert analysis</td><td>$129+/tech/mo</td><td>Faster troubleshooting</td><td>SOC 2, ISO 27001</td></tr><tr><td><strong>ConnectWise Sidekick</strong></td><td>Triage, email replies, sentiment tracking, scripting</td><td>~$1,042/mo saved per tech</td><td>5 min saved/ticket</td><td>No-training, secure access</td></tr><tr><td><strong>Autotask PSA</strong></td><td>Categorization, summaries, polished comms</td><td>Seat-based</td><td>15–30% more tickets/tech</td><td>DPA, least-privilege</td></tr><tr><td><strong>Syncro</strong></td><td>Categorization, summaries, responses</td><td>Seat-based</td><td>Limited scope</td><td>Review privacy policy</td></tr></tbody></table>

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### Agentic AI Explained

Agentic AI is marketed as the “next step” beyond automation. In practice, it chains multiple probabilistic decisions together. That makes it more flexible, but also more fragile.

**Key Points**

* Works by chaining LLM-driven tasks (interpret, act, summarize)
* Can save hours in triage and resolution
* Compounds risk if unchecked: one bad step can cascade
* Requires explicit rollback and human sign-off policies

*Example failure*: AI suggests a reboot script for all endpoints based on one vague ticket. Without review, this cascades into widespread disruption.

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#### Bottom Line

MSPs now have a broad menu of AI features across PSA, RMM, and specialized tools. Real ROI is possible, but only when features are evaluated against data policies, integration depth, and oversight requirements. Agentic AI should be treated as a junior tech, useful and fast, but prone to confident mistakes without supervision.


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