Successfully implementing AI projects
For readers in a hurry:
- Predictive vs. Generative: Understand the difference between predictive and generative AI and why this distinction is critical to the success of your AI projects.
- AI agents of the future: Find out how AI agents are changing automation and what potential lies dormant in areas such as sales, helpdesk and information research.
- 5 success factors: Learn about the five most important factors for successfully implementing your AI projects - from data quality to managing complexity.
On April 3, 2025, the InnoFACTOR workshop took place at the Business + Innovation Center Kaiserslautern - all about "Artificial Intelligence in Healthcare". Business Automatica GmbH presented practical insights into successfully implemented AI projects and showed how companies can make targeted use of the potential of artificial intelligence to successfully implement AI projects. The aim of the presentation was to give participants a realistic view of the opportunities and challenges of modern AI applications.
Understanding different types of AI projects
When implementing artificial intelligence in companies, it becomes clear time and time again that it is not the technology itself, but the correct understanding of its possibilities and limitations that determines whether companies can successfully implement AI projects.
Before we delve deeper into the topic, it is important to differentiate between two main categories of AI projects: predictive and generative AI. This distinction is of central importance, as they each require different approaches and entail different strengths and weaknesses.
Prognostic AI systems
These systems have been trained on the basis of cause-and-effect relationships with historical data, recognize these quantitative relationships and make predictions about future events or states based on them.
Typical fields of application:
- Identification of atrial fibrillation based on ECG recordings (medicine)
- Forecasting price developments (trading, stock exchange)
- Maintenance requirements for machines (industry)
- Fluctuations in demand (logistics)
Practical example:
Business Automatica GmbH developed a predictive maintenance solution that uses sensor data such as pressure, temperature, vibration or mass flow to detect faults at an early stage and avoid downtime.
Find out more about our predictive maintenance solution
Discover our solution in action and find out how you can minimize unplanned downtime.
Generative AI systems
These systems - e.g. ChatGPT - generate new content. They can summarize and classify texts or even create them completely independently. A major advantage: unstructured data is structured and made machine-readable by the AI giving this data a content-related (semantic) meaning.
Typical application examples:
- Automatic conversion of PDFs into electronic invoices
- Generation of software code on the basis of requirements
"Our experience shows that AI-supported software development only requires an average of 500 working hours - compared to 3,700 hours for conventional development. That means cost savings of up to 85%! A clear advantage for companies that want to implement their AI projects efficiently and successfully."
What AI can really do - and what it can't
A realistic understanding of their capabilities is crucial for the success of an AI project. As already mentioned, AI is excellent at understanding and summarizing texts, applying statistical methods to data, creating and analyzing images, videos and music, and even developing software.
Despite its impressive capabilities, artificial intelligence also has clear limitations. It is not capable of real logical thinking or in-depth reasoning in the human sense. Its weaknesses are also evident in computational tasks - more complex calculations often lead to incorrect results. In addition, AI models do not always work reliably: They tend to generate so-called "hallucinations" - i.e. factually incorrect or invented information. They cannot solve really complex problems independently.
These limitations must be taken into account when planning and implementing AI projects. AI is not a panacea, but a specialized tool. Successful projects use AI where it is really strong - and supplement it with specialist knowledge, control and clear target definitions.
AI agents: The future of automation
One particularly exciting trend is so-called AI agents. They take on tasks independently, coordinate tools via interfaces and carry out processes autonomously until they are successfully completed.
Examples:
- Digital sales support
- Intelligent helpdesks
- Automated information research
- Voice-controlled database queries and evaluations
Although this technology is still young, it already shows enormous potential for the process automation of tomorrow - and therefore for innovative ways to successfully implement AI projects.
Successfully implementing AI projects: The 5 most important success factors
Based on our practical experience, we have identified five key success factors that will make a significant contribution to the successful implementation of your AI projects:
- Try it out instead of assuming: AI is a practical field - what counts are experiences, not theories.
- Data quality is the key: only complete, well-structured data leads to reliable results.
- Integrate systems: AI must fit into your existing IT environment - only then will it add real value.
- Technical understanding: If you don't understand the domain, you can't build a meaningful AI solution.
- Managing complexity: AI projects are complex - a clear focus and good project management are essential.
Conclusion and recommendations for action
Artificial intelligence is a powerful tool. Companies that want to successfully implement AI projects need not only technical expertise but also a realistic picture of its possibilities and limitations.
The important thing is:
- Distinguishing between predictive and generative AI
- Ensure data quality
- Formulate requirements clearly
- Use iterative development
- Recognizing limits and compensating for deficits
- Only through the combination of technical know-how, professional understanding and organizational embedding can lead to sustainable success.
"The potential of AI is enormous - companies that invest wisely today will secure a decisive advantage tomorrow."
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About Business Automatica GmbH:
Business Automatica reduces process costs by automating manual activities, increases the quality of data exchange in complex system architectures and connects on-premise systems with modern cloud and SaaS architectures. Applied artificial intelligence in the company is an integral part of this. Business Automatica also offers automation solutions from the cloud that are geared towards cyber security.
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