- FOCUS TOPIC
A modern digitization project without AI - is that even conceivable today?
The answer is as sobering as it is liberating: YES!
Many current problems can still be solved effectively with classic process automation. While companies invest considerable resources in AI initiatives, the return on investment of which often takes years to materialize, proven automation approaches can deliver measurable results within months.
The key challenge lies not in the technology itself, but in the correct allocation: which approach addresses which problem most effectively? This article guides you through the strategic decision-making process and highlights the differences between classic process automation, intelligent process automation (IPA) and autonomous AI agents.
AI or classic process optimization - the fundamental differences
The three automation approaches differ fundamentally in terms of functionality, area of application and requirements:
Classic process automation
Classic process automation executes rule-based, predefined processes precisely and repeatably. The technology is ideal for stable, high-volume processes with low variability – such as invoice processing, data capture or report generation. Its strengths lie in complete traceability, reliability and scalability. The maintenance effort for complex sets of rules and the lack of adaptability to unforeseen situations mark the limits of this approach.
Intelligent Process Automation (IPA)
Intelligent process automation (IPA)orchestrates various processes with the integration of AI components. Systems in this category analyze context, make data-based decisions and dynamically adapt process flows. Typical applications include document classification, intelligent ticket distribution or intelligent customer service (chatbot / voicebot). IPA offers seamless system integration and anticipates potential bottlenecks, but requires high-quality data and API-enabled systems.
AI agents
AI agents makeautonomous decisions, learn continuously and pursue defined goals independently. Multi-agent systems are particularly effective in complex, strategic processes with numerous variables and dependencies – for example in autonomous customer advice, dynamic price optimization or self-learning quality control. The challenges lie in non-transparent decision paths, unexpected system behavior and an increased need for control.
The strategic recommendation
Classic automation as the foundation, selective use of IPA for decision-making processes and AI agents where autonomy and continuous learning generate real added value.
The key questions for the right solution
The decision-making process can be condensed into five central questions:
What does the process look like?
- Stable, rule-based processes require classic automation.
- Processes with decision points and context dependency benefit from IPA.
- Complex, variable scenarios that require learning justify the use of AI agents.
What decisions need to be made?
- Deterministic decisions are suitable for rule-based systems.
- Contextual evaluations – such as urgency assessment based on tone, content and history – require intelligent automation.
- Autonomous goal pursuit with adaptive strategies requires autonomous agents.
How high can the degree of autonomy be?
- Classic automation offers complete control.
- IPA enables monitored autonomy.
- AI agents act largely independently within defined framework conditions.
The governance requirements increase considerably as the degree of autonomy increases.
How much time & budget is available?
- Classic automation can be implemented comparatively quickly and delivers the first measurable results within a few weeks to months.
- IPA projects require a higher initial outlay for data integration and AI training, which means that the implementation phase takes several months.
- AI agents are the most complex category – it typically takes 12-18 months or longer from conception, training and testing to productive use.
Are the basics ready?
Technical infrastructure, data quality, available expertise and readiness for change significantly determine the probability of success. Without these foundations, even technologically mature solutions will fail.
A critical self-analysis based on these dimensions prevents costly bad investments and unrealistic expectations.
Prerequisites for successful AI implementation
Successful AI projects are based on robust foundations that go far beyond technological aspects:
Data quality as a critical success factor
AI systems are only as good as the data they work with. Inconsistent, incomplete or low-quality data inevitably leads to suboptimal results. Systematic data cleansing and structuring is essential.
Technical infrastructure
API-enabled systems, clean interfaces and MLOps capabilities form the technical basis. Without this infrastructure, even the most sophisticated AI solution will be ineffective.
Organizational competence
A basic understanding of AI/ML mechanisms within the team is required. Not every project requires specialized data scientists, but a sound basic knowledge enables realistic assessments and effective management.
Governance and compliance
GDPR compliance, compliance with the AI Regulation and clarification of liability issues are not optional. Governance structures must be established from the outset, not added later.
Change management
Technological excellence does not guarantee success if the organization does not go along with it. Creating acceptance, building skills and constructively addressing resistance is crucial for sustainable effectiveness.
Before making significant investments in AI solutions, it is advisable to first systematically establish the organizational structures and technical requirements.
Conclusion: Technological sobriety as a success factor
The relevant question is not “How quickly can AI be introduced?”, but “Which solution addresses the problem most effectively?”
Contact us for a use case sparring session
We would be happy to help you find the right approach for your use cases and support you in building an AI-capable organization.
Whether classic automation, intelligent process automation (IPA) or autonomous AI agents:
The trick is to choose the right technology for the respective challenge and maturity level. Because not everything that is technically possible also brings real added value.
Our decision matrix helps:
From the introduction of RPA in customer communication to intelligent portfolio optimization, we support you pragmatically and effectively.
What we bring with us:
- Process analysis with AI readiness assessment –not every process is ready for AI
- Vendor-neutral advice –the most suitable solution for your goals
- Pragmatic roadmaps – from quick win to strategic transformation
- Change management –empowering and developing people
- AI governance frameworks –integrating compliance right from the start
Do you want to find out what your AI potential is?
Then get started today and contact us for a free joint use case sparring session.