Why Process Automation Starts With Understanding User Problems

The Danger of Automating "As-Is" Workflows

Automating an "as-is" workflow means taking a broken process and making it run faster without fixing what's broken first. The result is that mistakes happen more often and become harder to undo.

Most managers have a clean picture of how their processes work. The problem is that employees can see it differently. Take invoice approval as an example.

The official version looks simple: receive the document, verify the data, route for approval, and post it to the ledger. What nobody documented is the personal spreadsheet someone built to double-check the ERP system they don't trust. Or the colleague they message to confirm a vendor code.

This is what's known as shadow IT – the informal tools and habits employees build around official systems because those systems don't quite work for them.

The consequences are quite predictable:

• A workaround that caused occasional mistakes gets baked into the process and now causes them all the time.
• Every system connected downstream inherits the same flaws, and each new integration makes it worse.
• Employees who find the tool doesn't match how they work will route around it.
• Every edge case nobody thought to document becomes a manual task someone has to handle by hand. And this is exactly what automation was supposed to eliminate.

The only way to avoid this is to understand how work genuinely gets done before any technology enters the conversation.

Recommended reading: Discover How Process Automation Is Transforming Business Operations

Human-in-the-Loop (HITL): Why Automation Fails at the User Level

Human-in-the-loop (HITL) automation is a design approach where human judgment is intentionally integrated into automated workflows at critical decision points. When this integration is poorly designed, it creates cognitive overload and drives employees away from the system entirely.

There's a common assumption in enterprise automation: if the backend works, the project is a success. But if the people responsible for overseeing the workflow find it exhausting or confusing to use, the automation will eventually fail.

This is where cognitive load becomes a critical metric. Cognitive load is a mental effort required to interact with a system.

For example, when a tool asks employees to switch between multiple interfaces, re-enter data that should already be available, or interpret unclear outputs, it adds friction instead of removing it.

Over time, that friction becomes system fatigue – the point where the effort of using the tool outweighs the benefit. At that stage, employees don’t even feel the need to complain, they just stop using the system.

This is why HITL design matters as much as the automation logic itself. Building it well means making human judgment as easy to apply as possible. We mean genuinely usable by the people whose job depends on it.

And that requires testing the interface with real users BEFORE deployment. Because, oftentimes, a workflow that looks perfect in a demo can fall apart completely when a real team uses it under real pressure.

Bridging the Gap: The Role of a Structured Discovery Phase

A discovery phase is the work you do before building anything. At this stage, you need to map how processes currently run and find where they break down.

A proper discovery phase does several things that a kickoff meeting or a requirements document simply cannot:

• Separates the stated problem from the real one. What a department head describes as "our approval process is too slow" might actually be three separate issues: a data quality problem upstream, an unclear ownership structure in the middle, and an interface bottleneck at the end.
• Surfaces what never made it into any document. The real process lives in the habits of the people doing the work, not in the SOP manual.
• Filters out the wrong solutions. Some friction points need a process redesign. Some need a policy change. Discovery work stops companies from spending engineering resources solving the wrong thing.
• Forces the success conversation early. Having that conversation before deployment is significantly cheaper than having it after. A discovery phase forces that conversation early, when it's still cheap to have it.

Mapping out enterprise workflows and identifying true bottlenecks requires more than just internal interviews. It demands a structured approach to validate the problem before writing a single line of code or deploying an RPA tool. This is why enterprise leaders often rely on an engineering partner like SpdLoad to conduct a comprehensive product discovery phase. By running this preliminary analysis, companies ensure that the technology they build or integrate actually resolves the users' pain points rather than simply digitizing an inefficient process.

Think of the discovery phase not as a delay, but as the work that makes every subsequent step faster and more precise.

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How to Identify the Right Processes for Automation

Once the discovery phase has mapped your actual workflows, the next challenge is prioritization.

A useful lens for this is bottleneck analysis, which is identifying where errors concentrate and where human effort is being spent on tasks that add no judgment value. Those intersections are where automation earns its keep.

What Makes a Strong Automation Candidate

Strong candidates typically share most or all of the following characteristics:

• High volume – the process runs frequently enough that gains compound with repetition.
• Rule-based logic – decisions follow consistent, documentable rules rather than contextual judgment.
• Standardized inputs – the data or documents entering the process have a predictable format.
• Clear success criteria – it is easy to define what "done correctly" looks like.
• High error cost – mistakes are expensive financially, operationally, or from a compliance standpoint.
• Low exception rate – the majority of cases follow the standard path.

Recommended reading: Learn the Best Practices for Successful Intelligent Process Automation

High ROI vs. Poor Automation Candidates

Measuring the Success of User-Centric Automation

Automation success means the system works and the people using it feel the difference in their daily work. One without the other is not a success.

Going live is just the beginning. The real question is whether the automation is delivering what was promised. The metrics below are the ones that show that honestly.

Conclusion: Why Workflow Optimization Should Start With a Discovery

Technology amplifies whatever you point it at. If the process is broken, automation makes it break faster and at greater scale. But if the process is well understood and built around how people work in real conditions, automation makes it run better than you expected.

The difference between those two outcomes is rarely the tool. In most cases, it is the discovery work you do before touching the tool. That is what turns an automation investment into actual ROI.