Jan Bosch
As I write this, I’m on my way home from a conference organized by the European automotive industry. Many will realize that this industry is having a very hard time right now. Low economic growth is causing people to not buy new cars. This is exacerbated by competition from the west and the east in … Read more
Over the last months, this series has explored the transition toward becoming an AI-driven company, with a particular focus on software-intensive systems industries such as automotive, industrial automation, telecommunications, energy and manufacturing equipment. These sectors face a unique constellation of challenges: long product lifecycles, safety-critical functions, complex supply chains, heterogeneous technology stacks and deeply embedded … Read more
When I started this series on the AI-driven company, we had developed three maturity models: AI in the company’s business process, the use of AI for the R&D process and AI in the product. Since then, however, we’ve developed a fourth model, which is concerned with AI in the business ecosystem that the company operates … Read more
In the final stage of the AI-enabled product maturity ladder, we cross a conceptual threshold. Instead of extending traditional products with AI capabilities, we design AI-first products. These are conceived around data and machine learning from the outset. Instead of being bolted on as an afterthought, AI is the core. AI-first products differ fundamentally from … Read more
Continuous ML is the fourth stage of our AI-enabled product maturity ladder. Here, models and systems don’t merely adapt occasionally; they improve continuously as new data becomes available. This represents a fundamental shift from periodic updates to ongoing evolution and systems that are learning and improving continuously. In order for this stage to be realized, … Read more
In the multi-ML stage on the AI maturity ladder for products, we move from individual to collective intelligence. Instead of relying on one model, we engineer systems composed of multiple machine learning models that work together to deliver complex functionality. As an example, a smart assistant system needs to integrate several ML models, such as … Read more
Static ML systems rely on pretrained models that are embedded in products and remain fixed throughout their operational life. Although this represents an important first step toward intelligence, such models are inherently limited. They can’t adapt when the context changes, when user behavior evolves or when operating conditions drift. The next stage in the evolution … Read more
Interestingly, in the companies I work with, there’s still some hesitation to include machine learning models in products. This is especially the case when there’s some form of safety certification associated with the product; we don’t always know how to ensure that the ML models won’t jeopardize critical product characteristics. That said, we need to … Read more
In this series on the AI-driven company, we’ve discussed the challenges that companies experience in becoming more AI-driven as well as the implications for business processes and for product R&D. The general pattern is that AI starts off as support for and augmentation of humans and, over time, becomes the main executor, with humans taking … Read more
In this series, we’ve explored the journey toward the AI-driven company. First, we looked at the business process view and recently, we’ve focused on the R&D process. Here, we see an evolution from AI assistants to compensators, superchargers and finally system generators. Each stage represents a deeper integration of AI into how we design, build … Read more
