Mistakes when automating processes in an MSP and how to avoid them

In the immortal lines of Public Enemy: “Don’t believe the hype”. Good advice in general and excellent advice in particular when we talk about automation in MSPs. It is easy to assume that the most efficient MSPs are those that automate the most. However, in many cases, that simply means running faster—yes—but straight toward a cliff.
Relax, this is not a Luddite manifesto, nor the sign you were waiting for to start the rebellion against the machines. When automation is properly planned, it is a sign of operational maturity, but when it is not… the backlash is ruthless because it accelerates errors, multiplies exceptions, and generates technical debt that someone will have to pay back with interest.
Poor automation replaces visible manual work with invisible operational risk, and, of course, it rarely saves as much money as expected.
And in an MSP, invisible risk is what ruins Fridays, something we cannot allow under any circumstances. So, let’s look at the main mistakes in MSP automation and how to avoid them.

Why automation does not always mean improvement

Automation only delivers value if:

• It reduces repetitive work.
• It minimizes errors.
• It maintains operational control.

But all three conditions must be met simultaneously.
If just one is missing, automation becomes a trap that looks good in meeting PowerPoints, but places a bomb under the chair of daily operations.
When there is no prior standardisation, sufficient validation, or clear operational context, automation can:

• Propagate incorrect changes at scale.
• Make diagnosis harder when something fails.
• Create a false sense of control.
• Increase dependence on logic that nobody fully understands or can maintain properly.

Boosting the speed of an inefficient system is like accelerating a car directly into a wall.
And the worst part is that its greatest danger is not so much the failure it causes, but the fact that it is not seen coming and its scale is usually greater than that of human error.
A technician who makes a manual mistake does so in a specific system, but poorly designed automation can easily make that mistake in one hundred systems at once, and sometimes at three in the morning because that is Murphy’s Law.

Signs that an automation is already generating more cost than savings

When people are up to their necks in the mud, whether automation-related or any other kind, we tend to normalise symptoms that clearly show something is not going well, even if we do not want to see it.
We tell ourselves that it comes with the territory, just normal bumps in the road, but no. We must recognise the clear signs that an automation is failing, such as:

• A frequent need for manual intervention: if the automation often requires someone to “push” it, it has stopped being automation.
• It fails with too many exceptions: if 30% of cases need special handling, then the process was not ready to be automated.
• Only the person who created it understands it: an automation that lives in a single person’s head is a single point of failure.
• It forces continuous review of its output: if automation has to be supervised as if it were a junior you do not trust, because you rightly suspect they use ChatGPT for everything, the savings are zero.
• It introduces incidents that did not exist before.
• It requires too much customer-specific logic: a sign that the process was not standardised and that unresolved variability was automated, multiplying the chaos.
• It consumes more maintenance time than the original process: if we reach that point, the loop has closed in the wrong direction.

Which types of automations are usually more problematic in an MSP

Not all mistakes are born under the same star. Some categories concentrate much higher risk when automated and appear with worrying regularity in the postmortems of many MSPs.

1. Changes in production without sufficient validation

This is the cardinal sin par excellence.
Automatically applying configurations to production systems, without a prior test environment or a clear rollback procedure, is like rolling out the red carpet for disaster and bowing as it walks past.

2. Automations applied to highly heterogeneous customers

If every customer is a snowflake with their own architecture, exceptions, and habits, automating without an abstraction layer that normalises that variability produces faster anarchy.

3. Automatic remediation on critical systems without reliable thresholds

If the alerts that trigger those corrections are not properly calibrated, the system will act on false positives, with real consequences for the infrastructure.
This is compounded by other issues such as:

• Flows with poorly controlled external dependencies.
• Actions triggered by poorly tuned alerts.
• Automations operating with excessive privileges.
• Processes with many exceptions and low repeatability.

Any of these factors on its own should already put automation on hold, but together they are the recipe for the incident nobody will want to explain in the postmortem meeting.

What not to automate yet in an MSP

In the episode The Ultimate Computer from the original Star Trek series, Dr Richard Daystrom installs the M-5 computer on the Enterprise. It quickly takes control and demonstrates capabilities superior to those of humans, but shortly afterwards, disaster begins. With the same operational efficiency with which it had previously done things right, it starts attacking its own ships, causing deaths.
In the end, the moral is that there are decisions that cannot be delegated to any system, however sophisticated it may be, if it lacks the context to make them properly.
In a modern MSP, decisions are more prosaic than on a starship, but just as important. That is why it is not advisable to automate yet:

• Mass changes in production: without prior validation in a limited environment. The risk is disproportionate to any supposed theoretical savings.
• Decisions that frequently require human context: if making the right choice means reading between the lines of the customer’s specific situation and taking subtle nuances into account, no machine can do that for now. And frankly, I don’t care what the latest marketing pitch tells you.
• Automations on poorly documented environments: without a reliable inventory, any automation fires at targets it does not know properly, just like M-5, with unpredictable results.
• Processes without prior standardisation: automation and standardisation are not synonyms, and standardisation must always come before automation.
• Automatic responses to ambiguous events: if we do not know for sure what a specific alert means, the automated response is a shot in the dark, not a solution.
• Highly customer-specific flows: when logic has more exceptions than rules, no policy can stand firm and no automation will avoid causing issues.
• Critical tasks without rollback or traceability: if something goes wrong and we cannot undo it, or know exactly what happened, we have placed an unpredictable black box in the middle of the process, left only to pray that it does not have irreversible consequences.

Now, the aim of this list is not fear, nor to become the battle cry of Dune’s Butlerian Jihad. What we want is to introduce a maturity criterion, a safeguard to avoid filming the sequel to The Ultimate Computer.
That is why which processes to automate first is a valid and necessary question, and its answer also defines what we still cannot leave in the hands of a machine.

Why some automations fail even when the tool is good

Those most interested in automation often say that what is happening is that we are not using the right tool, which is, of course, the one they sell, but the problem usually lies more in the operational design surrounding the platform than in the platform itself.
Because an excellent tool applied to non-standardised processes will continue to produce inconsistent results.
The same happens when:

• There is no reliable inventory.
• Rollback is not designed from the start.
• Segmentation between customers is insufficient.
• Parameters are hardcoded instead of being configured by typology.
• Logic designed for a specific case is reused without criteria in different contexts.

In all these cases, the tool will perform its function, as Spock rightly concluded when analysing what M-5 did. The problem is that this function is poorly defined from the beginning.

How to reduce risk when automation is worthwhile

When the process is mature enough for automation, there are principles that significantly reduce risk and should be internalised beforehand:

• Automate only previously standardised processes. If the expected behaviour is not clearly defined, automation has nothing solid to build on.
• Validate in limited environments or small groups before deploying to the entire customer base. Because if something fails on ten machines, it is manageable, but if it fails on two hundred one minute after deployment…
• Design rollback from the start, not as a later add-on. If we cannot undo it, we should not do it, and that should be engraved in every IT department of an MSP.
• Record execution and results centrally. Without traceability, diagnosing something when it fails becomes an archaeological expedition worthy of Indiana Jones among scattered logs.
• Parameterise by customer or typology, so that the same logic works in different environments without needing parallel versions that nobody controls.
• Monitor the automation itself, because if nobody supervises it, it can fail silently for weeks.
• Review exceptions and the real maintenance cost periodically. Automation that was useful may stop being so if the environment changes, and nobody will warn us if we do not check.

What changes when an MSP automates with criteria rather than on impulse

FOMO is powerful. A fire that makes us sweat nervously, fuelled by a huge amount of marketing and information, much of which is nothing more than additional marketing disguised as knowledge behind one of those glasses with a nose and moustache. We must not fall under external pressure, and automation must be the result of internal criteria.
When that is the case, propagated errors decrease, dependence on fragile scripts is reduced, and the team’s confidence in the operation increases. In addition, the need for reactive supervision and support also falls, because things work as they should, not because someone is watching.
In addition to the above, reuse across customers becomes real rather than aspirational, making it possible to operate hundreds of customers with the same technical team without costs growing at the same pace as the portfolio.
MTTR also improves in that case, because our technicians act on what matters, not on what careless automation set on fire.
Finally, the difference between detecting incidents before they impact the customer and putting out fires that should never have existed stops being rhetoric with good automation and becomes something measurable in SLAs and the income statement.

How Pandora FMS helps automate with control in MSP environments

Pandora FMS is a tool that automates with control and criteria, not only because it facilitates this process but, more importantly, because it facilitates the necessary prior actions, such as:

• Infrastructure analysis to identify what to automate with criteria and not with FOMO.
• The necessary standardisation, which I will never tire of repeating, and which is the foundation of automation.

That is why Pandora FMS’s approach in MSP environments starts from the real operational problem, not from automation as an end in itself.
Reusable templates and policies encode the expected behaviour once and apply it to all customers with the corresponding typology. Without this layer, every automation is handmade and fragile, dependent on someone remembering how it works.
Customer segmentation with granular permissions ensures that automation in customer A’s environment cannot affect customer B’s environment, no matter how much they share the same console. This isolation is critical in the multi-customer environments of an MSP.
Action traceability, in turn, makes diagnosing any failure manageable. Every execution is logged with its exact result, context, and timestamp..
Without this, the postmortem of an incident is a multi-hour nightmare that nobody has time for..
Infrastructure monitoring and IT system monitoring, integrated under the same view, allow automatic decisions to be made with real data rather than blindly, or driven by a sales presentation that made us fear “falling behind”.
Controlled response automation (self-healing, automatic escalations, corrective actions under known conditions) eliminates human intervention where it adds no value. But, of course, always with well-calibrated thresholds, with a record of what happens, and with the possibility to intervene when the context requires it, unlike in The Ultimate Computer.
Finally, there is the inevitable matter of security, without which nothing else matters. In this area, Pandora SIEM aligns security events with the rest of the operation, providing a global view and reducing the sea of noise in which relevant alerts often drown.
Ultimately, useful automation in an MSP is the kind that reduces repetitive work without increasing risk, opacity, or unnecessary maintenance.
And that, contrary to what is sold in any webinar, does not magically happen by adopting a new tool.
Good automation demands criteria before technology. It requires knowing just as clearly what not to automate as what to automate, because premature or poorly designed automation can cause the same disaster as M-5 in Star Trek.