AI in Engineering

OPPORTUNITY - Facts for decision makers

No other technology topic is currently receiving more attention than artificial intelligence. Media reports, studies and product announcements are coming thick and fast – but in practice, the picture is different: the proportion of companies that use AI strategically and at scale is still relatively low. In many places, there is no systematic approach to introduce AI in the product development process. Most strategies amount to simply building individual use cases.

In the OPPORTUNITY issue "Code the Product", we described a fully digital development approach that enables companies to benefit from shorter development times, a high level of product assurance and significantly reduced costs. In this issue of OPPORTUNITY, "AI in engineering", we shed light on the role of artificial intelligence in this new development approach.

Not every AI application changes processes to the same extent – some tools provide selective support, others take responsibility for entire processes. The taxonomy model developed by UNITY provides an orientation framework for this. It distinguishes between four categories of AI applications – from simple tools to digital AI employees. This model not only serves as a technical classification, but also as a basis for technology planning, change management and organizational development.

Contents of the OPPORTUNITY 'AI in Engineering'

The transformation of R&D
Vision “Code the Product”
Framework for AI in product development
Practical examples
Transformation roadmap: Code the product meets artificial intelligence
Governance, organization and competence requirements for AI integration

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The four AI categories act as a navigation system for the digital product development process. Those who understand them as levels of responsibility rather than technology can scale autonomy in a targeted manner, manage risks and unleash human creativity.

Dr.-Ing. Juan Mejia

Senior Manager and co-author of the OPPORTUNITY

Categories of Artificial Intelligence

The four AI categories show the degree of responsibility that humans delegate with the respective AI level. Understanding them as responsibility levels rather than technology levels allows you to scale automation in a targeted manner, manage risks and unleash human creativity.

Numerous practical examples from a wide range of application areas in the context of product development show what AI already makes possible today. In addition, a three-stage transformation roadmap combines the proven development stages of "Code the Product" with the success factors for introducing AI. Finally, the management and organization of technical, organizational and cultural change requires a stringent governance model, which is described in the OPPORTUNITY with the "AI Transformation House".

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