Most teams do not need more automation.
They need better judgment about what to automate, when, and in what state.
A bunch of different sources are saying the same thing:
- Gartner warns that when automation is executed poorly it harms data usage, processes, employee morale, and customer satisfaction instead of improving them.
- Multiple automation studies list “automating the wrong processes”, underestimating complexity, and weak governance as core failure modes.
- Process improvement articles consistently call out overcomplication and lack of ownership as reasons initiatives stall or backslide.
So the problem is not “too little automation”.
It is very often this: you are automating chaos.
This post is about how to stop doing that.
1. The uncomfortable question: what are you speeding up?
Picture this:
- An approvals process everyone already hates
- Confusing criteria
- Different managers applying different rules
- Manual workarounds in spreadsheets and side chats
Someone says: “We can fix this with automation.”
Three months later:
- The bad rules are now hard-coded
- Requests are declined faster, but still inconsistently
- People invent new workarounds to escape the bot
You did not optimize.
You just made the pain move faster.
This is the core pattern a lot of teams are living with, whether they are using RPA, low code, or AI agents.
2. The most common automation mistakes
Across Gartner, McKinsey, and a long tail of automation vendors and consultants, the same mistakes show up again and again.
Mistake 1: Automating the wrong processes
Typical signs:
- You picked a process because it was easy to script, not because it really mattered
- It is low volume or edge case work
- The process is already due to be redesigned or replaced
Result: you burn time and credibility on something no one cares about.
Mistake 2: Automating broken processes
Common pattern:
- The process is full of exceptions and workarounds
- Policies conflict, data is messy, roles are unclear
- Instead of fixing that, the team builds automation on top
FlowWright and others call this out directly: automation should follow process optimization, not be a band-aid for a process you know is flawed.
Result: you lock in waste, rework, and bad data at scale.
Mistake 3: Ignoring upstream/downstream impact
You automate “your” step in the flow without looking at:
- What happens before it
- What happens after it
- Which teams depend on your outputs and in what format
Result:
- Faster processing in one team
- More manual cleanup for the next team
- Extra reconciliation for finance or ops
- Overall cycle time does not improve, it just moves around
Mistake 4: Underestimating complexity and maintenance
Gartner and others note that leaders often underestimate:
- The complexity of automation once it touches multiple systems
- The ongoing maintenance cost as systems, fields, and rules change
Result:
- Fragile bots that break whenever IT ships a change
- Shadow scripts no one fully understands
- Ops teams babysitting automations instead of improving processes
Mistake 5: Automating without a clear business outcome
You launch automation with no explicit answer to:
- Which KPI should move
- How much it should move
- In what timeframe
ProcessMaker and others list “failing to tie automation to clear business goals” as a top error.
Result:
- Pretty demos
- No proof of value
- Skepticism the next time you propose an “automation wave”
3. Criteria for a good automation candidate
So what does a good target look like?
You can think in four filters.
Filter 1: High volume, repeatable, rules-based
Ideal candidates tend to be:
- High frequency
- Structured inputs and outputs
- Clear business rules
- Low emotional or relationship risk if something goes slightly wrong
If you cannot explain the rules in plain language, it is probably not ready.
Filter 2: Clear definition of success and failure
Before you build anything, you should be able to answer:
- What does “done right” look like for this process?
- How will we know it is better after automation?
- What counts as an unacceptable failure?
If there is no agreement here, you are not ready for automation. You are still in policy and process design.
Filter 3: Stable enough process and systems
Good candidates share traits like:
- Core steps are not changing every month
- Upstream data sources are mostly reliable
- The systems you touch are not mid-migration
If everything is in flux, you will be rewriting automation constantly.
Filter 4: Direct link to outcomes that matter
You should be able to draw a line from this process to something leaders already care about, for example:
- Time to onboard a new hire
- Time to resolve a customer case
- Invoice accuracy and days sales outstanding
- Regulatory or audit risk
If you cannot map “this process” to “this metric”, you are probably automating trivia.
4. A simple prioritization framework for leaders
Instead of “what can we automate”, shift to “what should we automate first”.
Here is a lightweight scoring model you can actually use:
Step 1: Score processes on three axes
For each candidate process, score 1 to 5 on:
- Value
- How big is the impact on revenue, cost, risk, or experience
- Feasibility
- How stable is the process and data
- How complex are the systems involved
- Risk
- What happens if the automation fails
- What level of oversight is required
High value, medium to high feasibility, and manageable risk go to the top of the list.
Step 2: Attach an owner and a KPI
For every process you greenlight:
- Name a single process owner, not a committee
- Tie the initiative to one primary KPI plus one guardrail KPI
This is exactly the discipline Lucid chart calls out as missing in many failed process efforts: lack of ownership and fuzzy measurement.
Step 3: Require a tiny business case and maintenance plan
Before work starts, ask for:
- A one-page case: current performance, target, rough benefit
- A one-page maintenance view: who updates rules, who checks logs, how changes are approved
If no one wants to own the maintenance, that is a red flag.
5. How Systems & Workflow Optimization helps you stop automating chaos
This is where a structured Business Operations partner like Scale Crew earns the honor of being your partner.
We are not here to install another tool. We are here to change the sequence.
In practice, that looks like:
- Surface reality before solutions
- Map your top value streams end to end
- Use system data and interviews to see where work actually flows, loops, and stalls
- Identify which processes are genuinely hurting speed, quality, or people
- Decide what to fix, what to automate, what to ignore
For each candidate process:
- Separate “fix it first” from “automate now.”
- Kill or postpone ideas that are low value or high volatility
- Focus your limited change capacity where it actually moves the business
- Simplify before automating
- Remove unnecessary steps, approvals, and variants
- Clarify roles, rules, and data ownership
- Only then decide where automation or AI belongs in the flow
That way, you automate clarity, not chaos.
- Build discipline around future automation
- Create a simple intake and review flow for new automation requests
- Set expectations for business cases, runbooks, and owners
- Make it normal to retire or refactor automations when the process changes
The goal is not to slow teams down.
It is to make sure every automation has a job description and a boss.
6. A quick sanity check for your current automations
Pick your top ten automations or bots.
Ask three questions about each:
- Did we simplify the process first, or did we automate what existed?
- Can we point to a specific KPI that improved because of this?
- Is there a named owner who would notice if it started doing the wrong thing?
If you are getting a lot of “no” or “not sure”, you do not have an automation problem.
You have a Systems & Workflow problem.
That is fixable.
You just need to stop automating chaos and start choosing your processes with intent.
