Anyone who brings clarity to the AI jungle has a good chance of turning the countless and initially hypothetical benefits of AI into real business value.
A stable and rewarding approach to this lies in intelligent automation, a combination of Artificial Intelligence (AI) and automation technologies. The result is a practical approach that, among other things, promises successful and efficient customer service.
What is Intelligent Automation (IA)?
Definition
Intelligent automation (IA) – also referred to as cognitive automation – describes a process in which Artificial Intelligence (AI) is used to achieve self-improving software automation. The terms intelligent process automation (IPA) and hyperautomation are also commonly used as synonyms.
IA represents the interface between AI and automation technologies, which together autonomously handle tasks within the enterprise. By combining multiple advanced technologies in this way, human input can be emulated in real time. Important to note: AI makes automation adaptive, but not autonomous; training and human oversight are still required.
Example
A practical example of intelligent automation is the relief of customer service employees, up to and including AI agents. In a simple case, software robots (bots) answer customer inquiries, confirm receipt, and inform customers when a detailed response can be expected.
Background: Intelligent automation should not be understood purely on a theoretical level, but rather approached from a practical perspective. After all, the technology is undergoing rapid and continuous change and is best understood through concrete benefits and applications.
What does Intelligent Automation consist of?
These are the fundamental components of intelligent automation:
- Artificial Intelligence (AI)
- Robotic Process Automation (RPA)
- Process management
- Automation tools
- Data
At its core, intelligent automation is based on the interaction between Artificial Intelligence and Robotic Process Automation. AI acts as a central control unit and cognitive component that enables so-called hyperautomation.
Worth knowing: The true value of intelligent automation does not arise simply from combining individual technologies, but from their process-centric orchestration—supported by well-documented processes and high data quality. Companies with such orchestration demonstrably achieve higher levels of automation than those using isolated bots.
What are the benefits of Intelligent Automation?
Intelligent automation creates something that did not previously exist: processes that largely run on their own, not just in isolated steps. Because of its intelligence, employees can largely rely on the technology, allowing it to function like a virtual employee.
Here is an overview of the benefits:
Benefit #1: Intelligent automation can handle even complex tasks precisely and efficiently.
Benefit #2: Processes are streamlined and workflows run smoothly.
Benefit #3: Errors are reduced and better results can be expected.
Benefit #4: Employees can focus more on strategic, value-creating, and creative activities, which in turn increases their productivity.
Benefit #5: Companies can achieve significant cost savings in some cases.
Benefit #6: Resources can be allocated more optimally, enabling companies to operate in a resource-efficient manner.
Benefit #7: Customer satisfaction increases as IA proactively anticipates needs, promotes seamless experiences, and strengthens brand loyalty.
Benefit #8: Thanks to exceptionally high accuracy and consistency, IA protects companies from risks in compliance-related tasks.
Why is Intelligent Automation important?
Intelligent automation is a key success factor for modern IT Service Management and Enterprise Service Management (ESM). It enables organizations to standardize, accelerate, and cost-effectively scale service processes across IT, HR, Finance, and other departments. This has numerous positive effects, both on operating costs and on service quality and relevant business objectives.
The most important business factors
The following aspects reflect the business value in the ITSM and ESM context:
- Higher service efficiency and productivity
Automating recurring service requests such as tickets, requests, and approvals significantly reduces processing and resolution times such as Mean Time to Resolution (MTTR). Service teams can therefore successfully close more tickets per employee without building up additional resources.
- Reduction of service costs (cost to serve)
By reducing manual effort, operational costs such as cost per ticket can be measurably lowered.
- Scalable Enterprise Service Management
Automated workflows enable the expansion of ITSM to other business areas (Enterprise Service Management) without costs rising proportionally. This allows organizations to implement efficient service processes across departments in a cost-effective manner.
- Improved service quality and user satisfaction
Faster response times and consistent processes increase first contact resolution rates and end-user satisfaction (Customer Satisfaction Score, CSAT).
How does Intelligent Automation differ from Robotic Process Automation?
Intelligent automation (IA) is an advanced technology that leverages multiple AI and automation capabilities. It represents the overarching construct of which Robotic Process Automation (RPA) is one component.
Robotic Process Automation
Robotic Process Automation handles task execution and follows defined rules. It is an important component of chatbots and enables the automation of recurring tasks such as data entry, form completion, or order processing.
Intelligent Automation – AI and Process Automation Combined
In intelligent automation, the most important component added is the AI application as a metaphorical brain, which can also take over cognitive tasks traditionally performed by humans: AI makes decisions, understands context, and personalizes responses.
Together with integrated process management, automation tools, and data-driven operations, this enables automation that thinks for itself and goes far beyond mere rule-following. While RPA takes over routine tasks and simple recurring steps, IA acts as a virtual agent that provides comprehensive relief to employees and can significantly increase efficiency.
Use cases for Intelligent Automation
Intelligent automation is used in many different areas—and is conceivable for even more. Important industry-specific examples can be found in manufacturing, such as in the automotive industry; in insurance, for calculating payments and estimating tariffs; or in retail, for invoice processing.
In customer service, IA is specifically about generating suggestions, supporting customers, and gaining data and insights that can be used, for example, for personalization.
Application in ITSM and Enterprise Service Management (ESM)
In ITSM and Enterprise Service Management (ESM), intelligent automation offers numerous opportunities to design service processes that are efficient, scalable, goal-oriented, and measurably value-creating.
The following factors are combined for this purpose:
- rule-based automation
- workflow orchestration (process and workflow management)
- decision logic (AI application)
Here are some examples of applications in ITSM and ESM:
- Automatic ticket classification: Incoming tickets and service requests are automatically analyzed, prioritized, and assigned to the appropriate team.
- Self-service and virtual agents: Standard requests can be resolved automatically via self-service portals and chatbots.
- Incident and problem management: IA can automatically detect, analyze, and in some cases even independently resolve recurring incidents.
- HR service automation: Onboarding, offboarding, and role changes can be automated with regard to accounts, access rights, or hardware.
- Facility and workplace services: Workplace, access, and repair requests can be handled automatically.
Intelligent Automation – Current developments in ITSM
Hyperautomation is emerging, but is only just finding its footing. As a key future technology, intelligent automation is gaining momentum in IT Service Management as well as in Enterprise Service Management, but it has by no means been established everywhere yet.
Especially for small and medium-sized enterprises, this means acting with care and building capabilities in this area at an appropriate pace. In many cases, it makes sense to perfect the base technology—such as existing workflow automations—gradually implement AI applications, and iteratively transition to intelligent automation.
AI-powered knowledge management, AI chatbots, and intelligent ticket handling through automatic classification, prioritization, and AI-generated summaries are key steps toward hyperautomation.
Many organizations now find themselves balancing the adoption of current developments such as AI-driven automation with day-to-day operations as well as existing technological and budgetary constraints. A proven approach is to experiment gradually and explore AI applications through flexible service models.
More information on the ITSM maturity of small and medium-sized enterprises, as well as topics such as automation and AI usage, can be found in our SMB Report 2026 based on primary data.
FAQ
Below you will find some frequently asked questions about intelligent automation.
FAQ #1: Which technologies does IA use?
Intelligent automation combines many different technologies. Artificial Intelligence is essential, acting analytically and predictively based on machine learning (ML) and deep learning (DL). Typically, Robotic Process Automation (RPA), low-code application platforms, and additional technologies such as process management, Natural Language Processing (NLP), and process mining are also used.
FAQ #2: Which software solutions for intelligent automation are particularly suitable for mid-sized companies?
For mid-sized businesses, software solutions that offer a broad range of capabilities are recommended—combining workflow automation, self-service, AI assistance, and other helpful features, while being quick to deploy and scalable.
Important use cases include ticket routing, chatbots, AI-based classification, and self-service workflows.
FAQ #3: How can I implement intelligent automation in a company quickly and cost-effectively?
Intelligent automation can be implemented most quickly and cost-effectively as a focused business initiative. This requires clear priorities, measurable goals, and a pragmatic approach. It is advisable to start with standardized, rule-based processes such as ticket classification.
The project should not function solely as an IT initiative, but should be embedded in a broader context, leverage existing capacities and capabilities, and promise a return on investment (ROI) as early as possible.
Conclusion
Intelligent automation (IA) represents one of the next important steps in the application of AI. This novel combination of different technologies offers the major advantage of uniting an evolutionary component—advanced automation—with a revolutionary aspect—agentic AI with strategic capabilities and decision-making authority. Also known as hyperautomation, it provides organizations not only with greater flexibility but also with measurable business results.
The foundation lies in combining automation technologies with the application of Artificial Intelligence. For example, many ITSM solutions already include a process engine and process, workflow, and ticket automation as a foundation that providers can combine with AI integrations.