{"id":14453,"date":"2025-01-08T11:21:06","date_gmt":"2025-01-08T10:21:06","guid":{"rendered":"https:\/\/www.inpro.de\/?p=14453"},"modified":"2025-06-04T16:41:42","modified_gmt":"2025-06-04T14:41:42","slug":"ai-expertise","status":"publish","type":"post","link":"https:\/\/www.inpro.de\/en\/ai-expertise\/","title":{"rendered":"AI-Expertise"},"content":{"rendered":"

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Scalable data models for interoperability in the system development process<\/h1><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

Benefit from our expertise in the field of industrial AI and start your AI development with our extensive project experience and preconfigured and customizable shop-floor AI-tools<\/a>. Our free checklist gives you a comprehensive overview of what you need to consider for the successful implementation and use of AI.<\/p>\n<\/div>

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Start with industrial AI<\/h2><\/div>
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Integrating AI applications into industrial processes and managing software deployment and maintenance is a complex and often costly endeavor. Traditional software development paradigms often fall short when big data becomes a critical factor. In addition, successful implementation requires a high degree of coordination and collaboration between multiple teams. The development of application-specific models for tasks such as prediction, anomaly detection or classification usually requires collaboration between domain experts, data science, data engineering, infrastructure and application development teams.<\/p>\n<\/div><\/div><\/div>

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Achieving a meaningful ROI with AI solutions<\/h2><\/div>
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When evaluating AI use cases, it is important to focus on scalability, taking into account feasibility as well as effort and potential risks. To address the multiple aspects of scalability, we use established process frameworks for data analytics and AI projects such as CRISP-DM (CRoss Industry Standard Process for Data Mining) and TDSP (Team Data Science Process) as foundational models that are enriched with customized extensions for specific requirements.<\/div><\/div><\/div>
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Evaluation framework for AI use cases<\/h1><\/div>

AI business scenario<\/h2><\/div>
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Consideration of aspects such as ROI, development costs, availability of experts, restrictions on brownfield sites and technical gaps.<\/p>\n<\/div><\/div><\/div>

Data requirements<\/h2><\/div>
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Taking into account aspects such as data basis, development costs, data labeling, environmental influences and signal significance.<\/p>\n<\/div><\/div><\/div>

Data modeling<\/h2><\/div>
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Consideration of aspects such as preselection of ML methods, hyperparameter optimization vs. parameter reduction.<\/p>\n<\/div><\/div><\/div>

Use of AI<\/h2><\/div>
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Consideration of aspects such as the availability of documented IT processes, access to the solution architect’s network and alternative component agreements.<\/p>\n<\/div><\/div><\/div>

Operation and maintenance<\/h2><\/div>
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Aspects such as the setup of the monitoring, the maintenance costs of the solution, the integration of relevant system sources and the traceability of the predictions within the system environment are taken into account.<\/div><\/div><\/div>
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Download our free checklist for the introduction of AI<\/h1><\/div><\/div><\/div>

What you can expect from the checklist:<\/h2><\/div>
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