What is Intelligent Automation?
AI combines artificial intelligence, that is, systems that can interpret data and learn patterns, with various types of automation tools that do the actual work. IBM describes IA as a combination of AI , process management and software robots that create more consistent and efficient workflows throughout the organization.
IA is a way of using AI and machine learning together with an automation platform to automate repetitive tasks while collecting data that provides better decision-making support. IA creates processes that can think, learn and adapt themselves over time, for example by interpreting documents and suggesting decisions.
The definition of Intelligent Automation
Intelligent Automation is when AI helps understand what needs to be done, and automation ensures that it actually gets done.
In practice, it's about building workflows where systems can read, interpret, make decisions within given frameworks and then perform actions in your existing systems.
AI usually consists of three main parts: artificial intelligence, some form of process or workflow engine, and tools that perform the actual work in the systems, for example through integrations and automation platforms.
What is included in IA?
Most descriptions fall into three main parts, although the wording may vary slightly between providers.
AI and machine learning
AI, artificial intelligence, is a collective term for technologies that can recognize patterns and make decisions based on data. Machine learning is a part of AI where models are trained on historical examples, for example to learn to recognize a certain type of case or detect anomalies.
It is precisely the combination of AI and machine learning that makes automation “intelligent”, as it can handle more varied and complex tasks than pure, pre-written rules engines can handle.
Automation engines that do the job
Once AI has interpreted what needs to be done, an engine is needed to carry out the work itself. This could be a case or workflow system, a platform that manages processes step by step, or software robots that click around in existing systems.
AWS describes IA as automation that is enhanced by AI on top of underlying tools and systems.
Governance, rules and follow-up
AI only becomes valuable when it is linked to clear rules, permissions and metrics. A common way to describe AI is as a combination of cognitive techniques that together optimize both processes and decision-making, not just individual elements.
In practice, this often means that you have a special layer where you define how processes should run, which exceptions require manual handling, and how results should be followed up.
So you can see AI as an interaction. AI interprets the world, a process or automation engine controls the flow, and various technologies perform the concrete steps in your systems.
What problems does Intelligent Automation try to solve?
If you look at your everyday life, there are often three recurring problems that IA addresses.
- First, there are all the tasks that are necessary but repetitive. This could be registering information in multiple systems, looking up the same type of data every time a case comes in, or checking that documents are complete. IA is well suited to taking over just such flows, so that you and your colleagues can focus more on deviant and value-creating work.
- Secondly, the amount of information is growing faster than people can read it. It can be emails, forms, attachments, logs or customer data. Here, AI can help read, sort and interpret, while the automation part ensures that the right thing happens in the right system depending on what is found.
- Third, customers and users are placing increasing demands on speed, transparency and accessibility. Many want to be able to follow their cases, receive notifications in real time and avoid having to wait for manual processing steps that actually follow clear rules. Here, IA can help by connecting the front end, such as e-services or customer portals, with automated processes in the background.
Key concepts: IA, AI, RPA, DPA and Hyperautomation
Before we go any deeper, we need to clarify some concepts that are often confused.
Artificial intelligence and machine learning
Artificial intelligence, AI, is the collective name for technologies where systems can recognize patterns and make decisions based on data. Machine learning is a part of AI where models are trained on historical examples (but also real-time data). In the context of AI, it is often about:
- Read and understand text, such as emails, forms, or PDFs
- Recognizing document types
- Assess risk, prioritize cases, or suggest next steps
An older analysis from McKinsey estimates that widespread automation could boost global productivity growth by around 0.8–1.4 percentage points per year, while a more recent study on generative AI points to an additional 0.1–0.6 percentage points per year in potential productivity gains by 2040, if the technology is widely adopted and the transition is well managed.
RPA – software robots in interfaces
RPA stands for Robotic Process Automation . These are software robots that do the same kind of click-through jobs that a human would do in a user interface. They log in, fill in fields, copy and paste information, and follow set rules.
In an AI solution, RPA is often just one of several tools. Once the process is defined and AI has provided the basis, robots can, for example:
- Posting or updating records in systems that lack good APIs
- Retrieve data from legacy systems
- Implementing complex but rule-based sequences
A moderate amount of RPA in an AI architecture tends to work best. Forrester emphasizes that successful initiatives require a balance between new AI functionality and more traditional automation.
DPA – the process level where everything is sewn together
DPA stands for Digital Process Automation and is about automating the entire process instead of focusing on individual clicks or sub-steps. You describe the flow in a system that holds the process together, with what steps there are, what roles are involved and which systems need to be updated.
For example, a DPA solution can:
- Keeping matters together from start to finish
- Control which steps are automatic and which require manual handling
- Call in AI or bots at the right steps
- Provide follow-up on lead times, volumes and deviations
For Multisoft, DPA is a natural part of how we use our platform Softadmin. The process logic is in Softadmin, then integrations, automation and regulations are connected around it.
Hyperautomation – the strategy behind it all
Hyperautomation is another term that often appears alongside AI. Hyperautomation is a business-driven, disciplined approach where organizations systematically identify, evaluate, and automate as many business and IT processes as possible, using a variety of technologies such as AI, low-code/no-code platforms , RPA, and other tools.
- IA describes how the technology works in an individual flow
- Hyperautomation describes the level of ambition across the entire organization
How does AI work in practice?
AI that reads and understands content
A typical IA solution starts with the system needing to understand what a case is about. This could be:
- Emails from customers
- Applications via a web form
- Attachments such as PDFs or scanned documents
With natural language processing and document interpretation, AI can, for example:
- Determine which category the case belongs to
- Pick out key fields such as social security number, case type or amount
- Assess if something important is missing
Here, AI acts as a smart assistant that does the heavy lifting on the front line.
Workflows that hold the process together
Once the information is interpreted, a process engine takes over. It represents how you want the flow to look from start to finish. There you decide:
- Which steps are fully automatic?
- When a case should go to a human handler
- Which systems should be updated?
- What rules govern prioritization and exceptions?
In Multisoft's world, this often means that the process is built visually in Softadmin, then integrations, automation and regulations are connected around it. The advantage is that you can change the process when reality changes without rebuilding the entire system map each time.
Implementation in existing systems
Finally, something needs to do the actual work. In an IA architecture, this could be:
- Integrations via APIs or message queues
- Robots working in interfaces where there is a lack of good integrations
- Built-in functionality in platforms and business systems
The point is that AI doesn't require you to replace everything you already have. You add a smarter layer on top, which knows when something can be automated end to end and when a human needs to take over.
Example: AI in everyday life
Customer service and case management
In a traditional customer service environment, employees read incoming emails, interpret what they are about, look up information in various systems, and respond or forward the case. It is time-consuming and difficult to scale.
With IA you can instead:
- Let AI read every email and determine the case type
- Automatically create cases in your case management system
- Send standardized responses whenever possible
- Let more complex cases be forwarded to a case manager with a ready-made summary
These types of IA solutions enable support and service organizations to move from reactive to more proactive management, where data and insights are used to predict peaks and patterns.
Finance and back office
In finance and back office processes, the combination of documents, rules and recurring flows is made for IA. A typical journey might look like this:
- AI reads an invoice, interprets amount, supplier, references and date
- The process checks against orders and regulations
- Deviations are flagged for administrators
- Everything that is green automatically goes to posting and payment.
Onboarding and self-service
When you work with onboarding customers, members or employees, or with applications of various kinds, IA is a way to tie together the entire journey:
- Forms on the web or in a portal
- A process that takes in applications, performs checks and creates cases
- Automation that updates underlying systems
- Notifications and status updates to the user
The result is that the applicant can follow their case, while you gain better control over lead times and bottlenecks.
Industry example: Efficiency gains for industry
For larger organizations, this means that manual intermediate steps are reduced, while control and traceability are increased. An analysis from McKinsey describes how successful automation transformations within industrial companies can yield efficiency gains of approximately 20 to 40%, and in one specific case, it is about 20-30% in core processes.
At the same time, they emphasize that the path to achieving this requires clear investments in skills, technology and change management .
How do you get started with Intelligent Automation?
1. Start with the process, not the technology
The most practical way to start is to choose a concrete process where:
- Volumes are relatively high
- The rules are known or documented
- There are clear problems with time, quality or workload
Together with the business, you describe the current situation. Then you look at which steps:
- Requires genuine human judgment
- Follows clear rules
- It's about reading and interpreting information.
Only then do you start talking about which techniques fit where. This order reduces the risk of buying tools that will not find the right use.
2. Think platform rather than point solutions
For IA to scale, it is wise to think in terms of a common hub for processes. This could be a DPA or low-code platform that:
- Keeps cases and process logic together
- Provides interface for users internally
- Integrates with other systems
- Can call on AI services and robots when needed
Multisoft's offering is based on this. With Softadmin® , we deliver tailor-made business systems that function as a stable process hub in complex IT environments. The IT department gets extra development muscle to plug holes in the system map without building everything from scratch, while the business gets system support that actually follows their processes.
3. Working together between IT and operations
Intelligent Automation rarely works if it is driven solely by IT or solely by the business. A pragmatic approach is to:
- Let the business describe problems, goals and real-world scenarios
- Let IT set the framework for architecture, security and technology choices
- Together, develop a requirements specification and a first version of the process
This is also how Multisoft typically works. We start with requirements gathering together with you. Then we implement the first version in Softadmin and further develop the solution as processes and needs change. This allows IA to be introduced step by step as the organization learns what works.
