Ask MSPs how their automation efforts are going and you’ll likely hear a familiar arc: it worked at first, then it started breaking, and now someone is spending hours keeping it alive.
The build is the part everyone plans for. You scope it, test it, ship it, and for a month or two it earns its keep. Then a new client onboards with its own quirks, a PSA update moves a field, and the workflow that was closing those tickets starts failing. No one owns that moment, so fixing it falls to the person who built it. And that person is the one you can least afford to pull off other work, so the queue fills back up while they dig in.
Keeping automation alive as the environment shifts is a full-time job, and someone has to own it. That can either be a role you hire and staff, or a service you buy.
Why automation rollouts stall after the initial build
A rollout optimizes for the build and assumes the environment will hold still, which it doesn’t. Setup succeeds under focused attention in a stable environment, and momentum dies when the environment moves: a new client onboards with its own approval chain, or the PSA ships a release the same week the person who built the workflow is back in the queue.
No one measures how often this happens, so it shows up in war stories more than in benchmarks. The one hard number nearby is satisfaction: a Datto survey of 1,262 MSPs found only 44% are satisfied with the automation in their tools.
The workflows break when a PSA or M365 update ships
Brittle scripts meet moving targets. Your PSA, Microsoft 365, Entra ID, and your RMM all ship changes on their own schedules, and any one of them can move a field your workflow depends on.
Most of these failures are silent. A Rewst fix last spring revealed that ConnectWise board queries had been returning nothing past page 20, and nobody had noticed. Neither HaloPSA nor NinjaOne promises a notice period before a breaking change, so the next one can land without warning. Who on your desk would catch a query that stopped returning results at page 20?
The client-specific exceptions nobody documented
Per-client exceptions block automation because they live in a manager’s head, not in IT Glue or Hudu. The quirkiest clients are the ones whose steps never got written down: a nonstandard MFA approval chain, say, or a one-off Entra ID setup.
Rewst’s pre-built Crates make this worse. A Crate updates automatically while it stays synced, but editing one to address a one-off exception for a specific client unsyncs it, and an unsynced Crate stops getting updates and becomes yours to maintain. Build-once-deploy-everywhere turns into a separate variant for every client with a quirk, with each one owned by whoever last touched it.
For any category you want to automate, ask whether a new tech could run it for your three quirkiest clients from documentation alone. If the answer depends on asking you, the automation depends on you too.
Maintaining automations became a second job no one signed up for
No one put “maintaining the automations” in your job description. You do it anyway. Rewst’s own setup assumes a dedicated Automation Engineer who knows Jinja templating and API troubleshooting, but on most service desks that role lands in the lap of one already-busy person.
The work keeps coming because the vendors keep shipping changes. Maintaining automations across dozens of client environments is like clearing a walk during a storm: you finish, and when you look over your shoulder there’s already a new fluffy layer of snow coating the path. When an automation stops firing at one client and no one notices for a week you can’t just hand it to a junior tech without the Jinja skills, and you can’t finish it while you’re the escalation point for every lockout the docs miss.
Do you need a dedicated RPA developer to sustain automation?
Only if you can fund a permanent build-and-maintain engineer and you accept that person as a second single point of failure. Automation engineers are scarce (one count puts it at 1,000 of them for 80,000 MSPs worldwide). Plus, they’re expensive. A fully loaded senior hire runs well into six figures. All that money only buys you maintenance capacity. The tickets still need someone to work them.
The economics point the other way. When a tech closes a ticket by hand it costs you labor. An escalation to L2 costs more. So, it pays to push volume down a level or out of the queue entirely. Hire the engineer when automation is a product you’re building and offering to your own clients. When the goal is to get L1 volume off the queue, solutions like Fixify make more sense since they price based on the number of tickets they resolve end to tend rather than on seats, endpoints, or client count. That means your cost tracks directly with the value delivered. Spend the hiring budget on L2 and L3.
Software you run vs managed automation that owns maintenance
Rewst and Pia are platforms you staff and operate. Managed automation is an outcome you receive. Rewst’s synced workflows update when Rewst changes them, and Pia maintains and versions its PiaPacks, but you still wire both into your PSA and client environments, and neither vendor employs the analyst who covers the exceptions that pop out of your queue at 2 a.m. or rewrites the exception you unsynced.
| Responsibility | Platform you run (Rewst, Pia) | Managed automation (Fixify) |
|---|---|---|
| Build the workflow | Your automation engineer or a paid partner | Fixify builds a set of playbooks for each client |
| Fix a break after a PSA or M365 update | Automation vendor updates connectors; you update automation logic | Fixify |
| Cover the queue 24/7 | You staff it | Fixify-employed IT experts |
| Handle an ambiguous request | Your technician | Fixify 24×7 team reviews, then executes or escalates |
How Fixify keeps playbooks current without MSP effort
During onboarding, Fixify turns the knowledge you already have into maintained playbooks:
- It ingests your historical tickets, documentation, and recorded SME calls to build natural-language playbooks for each client, with no YAML or PowerShell for you to write.
- It captures the approval chain that lives in your tech’s head on a recorded call during onboarding.
- It watches the integrations, and when a change breaks a connection, Fixify owns the fix.
- It starts every integration read-only and turns on write actions only after you approve them.
Fixify-employed analysts supervise around the clock. Routine L1 requests like password resets run end to end. Anything ambiguous gets context, an identity and policy check, and an analyst’s review before it executes, with every action logged back to your PSA. Coverage starts with the most repetitive, lowest-risk requests and expands as the playbooks mature.
How to tell whether your rollout can recover
You can tell from two things: whether you can name what broke, and whether more than one person can fix it. Name the cause and keep fixes inside your release cycle, and the rollout is recoverable. If the fix lives with one person, or no one can say why it broke, it isn’t. Run these four checks on your last 90 days of L1 tickets:
- What breaks. List the automations that failed and the vendor change behind each. If you can’t name the cause, the rollout is running blind, and monitoring is your first gap.
- Who fixes it today. If the answer is you, or “we haven’t,” it has already stalled. A whole market of firms now sells paid Rewst maintenance, which tells you how much work this is.
- How long fixes take. Compare repair time to how often your vendors ship. If fixes land slower than releases, you fall further behind with every cycle.
- Share of L1 still escalating. Count closed tickets automated end to end; triage counts separately. Fixify’s own 2026 IT Help Desk Benchmark Report found that help desks automating most of their work resolve tickets in a median of 4.4 hours, against 71 hours, roughly three days, without it.
If the causes are nameable and your fixes keep pace, keep it in-house. If they don’t, hand the maintenance to someone who owns it. Either way, upload 90 days of ticket data to Fixify’s free Ticket Snapshot to see which categories drive volume and repeat work, then start with the high-volume one where your automation rate has gone flat.
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