When the pressure to digitise and optimise intensifies, many organisations reach for automation first – process mining, AI, robotic process automation (RPA) and machine learning are all seen as quick wins. But while these tools offer speed and scalability, they also amplify whatever processes already exist, whether functional or flawed.

Automation is not a shortcut to effectiveness. 

Without foundational simplification, it often leads to embedded inefficiencies at scale. This is why experienced business analysts and process designers increasingly advocate a critical principle: simplify before you automate.

The Kitchen Nightmares Analogy

To understand the consequences of skipping simplification, look no further than a Gordon Ramsay episode of Kitchen Nightmares. In nearly every case, Ramsay enters a struggling restaurant with an overstuffed menu, confused staff and inconsistent quality. The solution is rarely to upgrade equipment or invest in a new online ordering system. It’s to cut the menu by half, clarify responsibilities and standardise the basics.

This principle transfers easily into the business world. Over time, organisations accrue process bloat – layers of approvals, redundant steps, legacy tools and workarounds that may once have had purpose but now only create drag. Automating these tangled processes without interrogating their necessity is like digitising a messy kitchen i.e., you only speed up dysfunction.

Why Simplification Comes First

1. Complexity is the hidden cost in automation

Automation is often sold as a cost-saving solution. But what’s rarely mentioned is how expensive it is to automate complexity. Custom workflows, exception paths and multiple handoffs drive up development time, testing overheads and ongoing maintenance.

By contrast, when processes are simplified first, automation becomes leaner, more reliable and easier to scale. Simplification strips away the non-value-adding steps and surfaces what truly matters.

2. Automation doesn’t solve broken processes

Automating a broken process doesn’t fix it, it cements it. And once automated, a flawed process is harder to change. You’re now working within the constraints of the technology you built and even minor adjustments require reconfiguration, testing or redevelopment.

Simplification brings clarity to how workflows, who owns what and where breakdowns happen. Only with this understanding can automation truly enhance performance.

3. People adapt faster to simpler systems

Successful automation depends on adoption. And people are far more likely to embrace systems they understand. When processes are intuitive, handovers are clean and outcomes are visible, employees feel more confident and engaged.

We often speak to the human side of simplification. The goal isn’t just leaner workflows, it’s clarity for the people doing the work. If they can’t see how the process fits together, no amount of automation will create alignment.

The Business Analyst’s Role in Simplification

Simplification isn’t about gut instinct or hasty removal of steps. It’s a discipline. Business analysts play a crucial role in uncovering where complexity hides and how it can be removed without compromising compliance, quality or customer experience.

Here’s how analysts should approach it:

• Observe the process in action: Not just what’s written in manuals, but how the work actually gets done. Shadow teams, map variations and identify the workarounds that have become default behaviour.
• Identify root causes, not symptoms: Are multiple approvals in place because of one historical incident? Has reporting duplicated because two departments don’t share systems? Simplification requires you to trace decisions back to origin.
• Distinguish complexity from sophistication: Not all detailed processes are bad, some are necessary for regulation, safety or customisation. The analyst’s task is to separate what’s complex for good reason from what’s just accumulated friction.
• Prioritise based on value and effort: Use heat mapping or effort-impact matrices to rank where simplification will have the biggest return. Don’t aim to simplify everything at once.

When Simplification Meets Automation

Once processes are simplified, automation doesn’t just become easier, it becomes smarter.

• RPA becomes more resilient: RPA thrives on consistency. A simplified process with fewer variations reduces exceptions and failure points.
• AI delivers better insights: Clean, streamlined processes yield cleaner data. This improves the quality of training data sets and enhances predictive accuracy.
• Process mining becomes more meaningful: Instead of producing overwhelming spaghetti diagrams, process mining on simplified systems highlights clear flows, deviations and improvement opportunities.

The Risk of Skipping Simplification

There’s often urgency around automation: executive mandates, end-of-life tech systems or budget cycles tied to transformation milestones. The temptation is to move quickly. But skipping simplification carries risks:

• High rework costs post-automation
• Frustrated end users due to clunky digital experiences
• Shadow systems re-emerging as staff find ways around “digital red tape”
• Technical debt, as automation is layered on top of inefficient foundations

Taking the time to simplify isn’t a delay, it’s an accelerant. It makes transformation stick because the improvements are embedded in how people think and work, not just the systems they use.

In the rush to digitise, organisations must resist the lure of automating for automation’s sake. Process complexity rarely stems from technology alone, it accumulates from decisions, habits and exceptions that go unchallenged.

Before investing in tools, invest in clarity. Strip processes back to what’s essential. Redesign them with the people who run them. Then, and only then, should you automate.

Digital transformation shouldn’t be about moving faster with the same problems. It should be about moving better and that starts by simplifying what’s already there.

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