Process automation that lasts over time

What is process automation?

Process automation is about making an entire work process “run by itself” as far as is reasonable. From the starting trigger to the finished result, with the right data, the right rules and the right traceability. This can mean everything from a case being automatically created, supplemented and distributed, to a decision being made according to policy, and only the exceptions ending up with a human.

In practice, process automation is a combination of three things:

1. Process logic : The steps, rules and decisions, i.e. “how the job should be done”.
2. Information flow : Data that is included, updated and reused, without anyone having to enter the same thing multiple times.
3. Orchestration : That people, systems and integrations interact in the right order, with clear responsibility and traceability.

It sounds obvious, but there is a difference between automating individual tasks and actually automating a process. The point of process automation is not to automate for the sake of automation, but to streamline by reducing wait times, errors and duplication of work, and ideally also making the process easier to change.

Why is everyone talking about process automation right now?

Two shifts are driving the interest.

One is that the amount of administration is growing, often because more systems, more rules, and more integrations create more “small steps” that someone or something has to keep together.

The second is that the opportunities are increasing rapidly. McKinsey estimates that activities equivalent to up to about 30% of hours worked could be automated by 2030 , driven by genAI, among other things.

This doesn't mean that everything should be automated. It means that it pays to get good at choosing the right processes, and building them in a way that's manageable.

See process automation as a way to shift time from administration to decision-making and value creation. The OECD highlights that a significant proportion of jobs are affected by automation and technological change, and this change often requires dialogue and change management .

Different types of process automation

There is of course a world of terms that are meant to simplify and explain process automation. To some extent, the terms make it more difficult to navigate, but you can see it as different levels of complexity based on the need you are facing: from RPA to business-oriented process automation .

Robotic process automation

Robotic Process Automation (RPA) is great when you want to automate repetitive, rule-based tasks that are otherwise done manually in an interface, such as copying data between two systems. RPA is a form of process automation technology that uses software robots to automate tasks performed by humans.

Process automation is broader. It’s about the flow from start to finish, including rules, exceptions, logging, integrations, and often involves multiple teams. A simple way to think about it is: RPA can be a component of a process, but the process still needs a “conductor.”

Process automation and workflow automation

Workflow automation often focuses on distributing work between people and steps in a flow, such as approval, follow-up, and notifications. Process automation usually takes a broader approach and also includes data modeling, system integrations , and how you handle deviations.

That's why many organizations are disappointed when they digitize a form, for example. They have built a workflow, but the real bottlenecks of the process lie in the data, exceptions, and dependencies between systems.

Digital process automation

Digital process automation (DPA) is often about automating processes that span multiple applications and that typically involve human interaction at some stages.

TechTarget describes DPA as the automation of processes that can span multiple applications and is often supported by platforms/tools.

A good way to think of DPA is as “the processes in the middle”: where people, rules, and system integrations need to interact, not just a single automation bot.

Intelligent process automation

Intelligent process automation (sometimes called intelligent automation or IPA) builds on automation with AI capabilities that can interpret, classify, and handle more variation than pure “if X then Y” rules.

IBM describes intelligent automation as a way to streamline processes by combining automation with intelligent technologies.

Where does AI fit in?

AI doesn't make process automation less important, it makes it more relevant. AI also enhances the value of your structured data .

For example, AI can:

• Classify incoming cases
• Reading and interpreting free text or documents
• Suggest next steps or decisions
• Find deviations that need manual processing

But AI only becomes valuable when it is built into a controlled flow with traceability and accountability. Hyperautomation is a concept that expresses intelligent automation as a clear strategic direction.

Gartner describes Hyperautomation as a disciplined approach to identifying and automating as many processes as possible, often using several different technologies in combination.

Business-specific process automation

Many processes fit well in a standard system or in a platform you already have in your application portfolio. But when the process is business-critical, crosses multiple functions, or requires customized and manageable support, it is extra important to have the right system in the right place . You need a system support that handles roles, registers, cases, rules, and integrations.

If you find yourself in that situation, a platform-based approach can be pragmatic: you build a customized flow on standardized components, and further develop as the process changes. This often becomes extra relevant when automation has to work in complex IT environments where integrations and traceability are at least as important as the interface.

Multisoft builds process automation solutions through the low-code platform Softadmin®, with a focus on flexibility and scalability and can also supplement with AI functionality and an integration platform that collects and creates an overview of integrated systems and flows.

How to choose the right process to automate first

The biggest mistake is to start with what seems to be the biggest annoyance without checking the data, exceptions, and dependencies. Instead, start by finding processes that are stable enough to automate, but important enough to justify the effort.

A good rule of thumb is to look for processes where a lot of time is spent moving information, checking rules, and chasing completions.

Below is a checklist you can use in an initial selection:

• High volume or high frequency: The process happens often (many cases/orders) or creates many recurring deviations – so the benefit of automating becomes clear.
• Clear start and finish: You can pinpoint a clear start trigger and a clear “done” state, so you can measure and control the flow.
• Rules can be expressed: You can formulate the decision rules so that they can be built into the system (for example, policies, thresholds, permissions and SLAs).
• The data exists, or can be obtained in place: Necessary information exists in systems (or can be structured) and it is clear which source is the “master” for each data.
• Exceptions are controllable: You can clearly define which deviations should be handled manually (and when the process should escalate to a human), while the rest can be handled automatically.

A simple scoring model that works in reality

If you want to keep it really simple, you can start with four questions:

1. What is the average processing time and where does it disappear, in waiting or in work?
2. How often do we redo the same thing, or enter the same data twice?
3. What are the most common exceptions and can we group them?
4. Which systems need to talk to each other for the process to be “complete”?

The answers are often enough to prioritize 2 to 3 processes that are worth an initial investment.

How an automated process is usually built

A robust process automation almost always consists of the same building blocks. This is not a “technical list.” Rather, it is a way to see what needs to be in place for the automation to work.

• Trigger : Something that starts the flow, such as an application, a file, or an event in another system.
• Validation : Checks, authorization, required fields and references.
• Enrichment : Retrieve data from other systems, registers, or past history.
• Rules engine : Decisions according to policy, agreement, risk level or priority.
• Orchestration : Create tasks, send notifications, call APIs, and update status.
• Exception handling : When something is unclear or deviates, it should go to the right person.
• Traceability : Logging, versioning of rules and the ability to follow what happened.

The exceptions that confirm the challenge

Many processes can be largely automated quite quickly. The challenge is that large parts (the deviations) cannot be automated easily. This has been a challenge for a long time .

A good design principle is therefore: Fully automate the standard flow, but make exceptions visible, well-structured, and measurable.

Then you can:

• See which exceptions increase over time
• Adjust rules or data flows
• Reduce the manual part step by step, without risking quality

Common pitfalls and how to avoid them

Process automation rarely goes bad because the technology doesn't work. It goes bad because you automate around problems that are actually process and data quality.

Here are a number of classic pitfalls, and what usually helps:

• You are automating a bad process : Start by simplifying and standardizing where possible, otherwise you are just codifying the mess.
• Unclear ownership : The process must have an owner in the business, otherwise it becomes an IT project without control.
• Data is not reliable : Automation can exacerbate data problems. Spend time on master data and validation early.
• Too many special cases from the start : Start with a core, build out in iterations.
• No monitoring : If you don't measure lead time, exceptions, and errors, you won't know if the automation is working.
• Security and authorization come last : Especially when many systems are connected, security, access, and traceability must be design requirements, not afterthoughts.