{"id":1877,"date":"2026-04-04T09:34:49","date_gmt":"2026-04-04T08:34:49","guid":{"rendered":"https:\/\/leansigma.ie\/?p=1877"},"modified":"2026-04-04T09:34:49","modified_gmt":"2026-04-04T08:34:49","slug":"fix-your-processes-before-you-digitise-them","status":"publish","type":"post","link":"https:\/\/leansigma.ie\/?p=1877","title":{"rendered":"Fix Your Processes Before You Digitise Them"},"content":{"rendered":"\n

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Digital tools can do a lot. They can speed up work, reduce manual effort, improve visibility, and make data easier to track. That is why so many organisations invest in automation, workflow systems, dashboards, AI tools, and large transformation programmes.<\/p>\n\n\n\n

But there is a common mistake at the centre of many of these efforts.<\/p>\n\n\n\n

They digitise broken processes.<\/p>\n\n\n\n

That usually leads to one disappointing result: the same waste, confusion, and inconsistency still exist, but now they move faster and cost more.<\/p>\n\n\n\n

Technology can improve a process. It can support a process. It can strengthen control and efficiency. But it cannot fix poor logic, unclear ownership, unnecessary complexity, or bad habits on its own.<\/p>\n\n\n\n

That is why one principle matters more than most in transformation work: fix your processes before you digitise them.<\/p>\n\n\n\n

Technology does not automatically create good process design<\/h2>\n\n\n\n

There is often an assumption that once a new system is installed, old problems will disappear. People expect the software to create discipline, remove duplication, and force consistency.<\/p>\n\n\n\n

Sometimes it helps. Often it does not.<\/p>\n\n\n\n

If the underlying process is badly designed, the technology usually ends up doing one of three things.<\/p>\n\n\n\n

First, it locks the bad process into the system.<\/p>\n\n\n\n

Second, it makes the complexity harder to see because the workflow looks cleaner on screen.<\/p>\n\n\n\n

Third, it increases frustration because users now have to follow a poor process through a more rigid tool.<\/p>\n\n\n\n

In other words, digitisation can make dysfunction more efficient.<\/p>\n\n\n\n

A messy approval chain does not become smart because it sits inside a digital workflow. A weak handoff does not improve just because notifications are automated. A poor decision process does not become better because it now produces dashboards.<\/p>\n\n\n\n

The real issue is still the process.<\/p>\n\n\n\n

Bad processes become expensive when they are automated<\/h2>\n\n\n\n

Manual workarounds are often annoying, but they are at least visible. People know they are compensating for something that does not work well. They create side conversations, extra checks, spreadsheets, or informal steps to get things done.<\/p>\n\n\n\n

Once that same process is digitised, the weaknesses can become harder and more expensive to fix.<\/p>\n\n\n\n

Now there is system configuration, integration logic, user training, support overhead, reporting structures, and change controls all built around the flawed design. What used to be a local inefficiency can become an enterprise problem.<\/p>\n\n\n\n

This is one reason some digital transformation programmes struggle. The organisation focuses on the platform, the timeline, and the go-live date, but not enough on whether the process itself is sensible, simplified, and ready to scale.<\/p>\n\n\n\n

If the process is poor, technology often magnifies the problem rather than removing it.<\/p>\n\n\n\n

Process waste should be removed before automation begins<\/h2>\n\n\n\n

A good process should be worth digitising.<\/p>\n\n\n\n

That sounds obvious, but it is often skipped because teams are eager to move quickly. They want the tool in place. They want visible progress. They want to modernise. So they automate what already exists instead of stepping back and asking a more useful question:<\/p>\n\n\n\n

Should this process work this way at all?<\/p>\n\n\n\n

Before digitising, teams should examine the process closely.<\/p>\n\n\n\n

What steps add value?<\/p>\n\n\n\n

What steps exist only because of old policy, old structure, or old technology?<\/p>\n\n\n\n

Where are the delays?<\/p>\n\n\n\n

Where do errors repeat?<\/p>\n\n\n\n

Where are approvals unnecessary?<\/p>\n\n\n\n

Where are handoffs weak?<\/p>\n\n\n\n

Where is ownership unclear?<\/p>\n\n\n\n

Where does rework happen?<\/p>\n\n\n\n

Until those questions are answered, digitisation is often premature.<\/p>\n\n\n\n

It is far better to simplify first, remove waste first, and standardise first. Then digitise what remains.<\/p>\n\n\n\n

A flawed process with good software is still a flawed process<\/h2>\n\n\n\n

Organisations sometimes confuse software quality with process quality.<\/p>\n\n\n\n

A platform may be modern, well designed, and technically capable. The interface may be clean. The dashboards may look impressive. The workflow may be automated end to end.<\/p>\n\n\n\n

But if the logic underneath is poor, the organisation still has a process problem.<\/p>\n\n\n\n

For example, a purchasing workflow may be fully digitised but still contain too many approval levels, vague decision rules, and repeated review of low-risk requests. The process is digital, but it is not efficient.<\/p>\n\n\n\n

A customer service tool may route cases automatically, but if categories are unclear and escalation rules are weak, customers will still experience delays.<\/p>\n\n\n\n

A production reporting system may collect data in real time, but if the response process for recurring issues is inconsistent, the visibility does not translate into improvement.<\/p>\n\n\n\n

The software may work exactly as intended. The process may still not.<\/p>\n\n\n\n

Digitisation should support better ways of working<\/h2>\n\n\n\n

The real goal is not to turn paper into screens. It is not to move forms online. It is not to automate old bureaucracy.<\/p>\n\n\n\n

The real goal is to improve how work gets done.<\/p>\n\n\n\n

That means digitisation should follow process redesign, not replace it.<\/p>\n\n\n\n

A better sequence is this:<\/p>\n\n\n\n

Understand the process.<\/p>\n\n\n\n

Challenge the logic.<\/p>\n\n\n\n

Remove unnecessary steps.<\/p>\n\n\n\n

Clarify roles.<\/p>\n\n\n\n

Simplify decisions.<\/p>\n\n\n\n

Standardise where it makes sense.<\/p>\n\n\n\n

Build controls where they matter.<\/p>\n\n\n\n

Then use digital tools to support the improved design.<\/p>\n\n\n\n

This approach takes more discipline at the start, but it usually creates far better results. It reduces the risk of automating waste and increases the chance that the technology will actually deliver value.<\/p>\n\n\n\n

People often digitise around problems instead of solving them<\/h2>\n\n\n\n

Another common mistake is using technology to work around a process issue rather than fix it.<\/p>\n\n\n\n

If communication is poor, a team adds more alerts.<\/p>\n\n\n\n

If approvals are slow, they add escalation rules.<\/p>\n\n\n\n

If data is inconsistent, they add more fields.<\/p>\n\n\n\n

If people miss steps, they add more mandatory checks.<\/p>\n\n\n\n

Sometimes these changes help. But sometimes they just pile complexity on top of weak design.<\/p>\n\n\n\n

More technology is not always the answer. Sometimes the real answer is simpler accountability, fewer decision points, clearer ownership, or better process rules.<\/p>\n\n\n\n

It is worth remembering that complexity can feel like progress. A bigger system, more automation, and more configuration can look sophisticated. But sophistication is not the same as effectiveness.<\/p>\n\n\n\n

Often, the best improvement is a simpler process that people can actually follow.<\/p>\n\n\n\n

Good process design makes digitisation more effective<\/h2>\n\n\n\n

When a process has already been improved, digitisation becomes much more powerful.<\/p>\n\n\n\n

The organisation knows what the process is trying to achieve.<\/p>\n\n\n\n

Roles are clearer.<\/p>\n\n\n\n

Decision points are more rational.<\/p>\n\n\n\n

Exceptions are better understood.<\/p>\n\n\n\n

Waste has already been reduced.<\/p>\n\n\n\n

That means the technology can be configured around a cleaner design. Automation becomes more useful because it supports a process that already makes sense.<\/p>\n\n\n\n

Training becomes easier too. Users are not just learning a system. They are learning a better way of working.<\/p>\n\n\n\n

Metrics become more meaningful because they reflect a stronger process.<\/p>\n\n\n\n

Support demands usually fall because the workflow is more logical.<\/p>\n\n\n\n

In short, good process design gives digital tools something solid to build on.<\/p>\n\n\n\n

This matters in both large and small transformations<\/h2>\n\n\n\n

The idea applies whether the organisation is investing in a major enterprise system or a simple internal tool.<\/p>\n\n\n\n

It applies to ERP rollouts, CRM changes, finance workflows, service processes, manufacturing systems, HR platforms, procurement tools, and AI-enabled automation.<\/p>\n\n\n\n

The scale may change, but the principle stays the same.<\/p>\n\n\n\n

If the process is badly designed, digitising it will rarely produce the results people hope for.<\/p>\n\n\n\n

The same is true for smaller improvements. Even a modest automation project can disappoint if it is built on unclear steps, duplicated tasks, or poor data definitions.<\/p>\n\n\n\n

Fixing the process first is not only for large transformation programmes. It is basic operational discipline.<\/p>\n\n\n\n

What fixing the process actually means<\/h2>\n\n\n\n

Fixing a process does not mean making it perfect. It means making it fit for purpose.<\/p>\n\n\n\n

That usually includes a few practical things:<\/p>\n\n\n\n