Jan Bosch
One of the most powerful effects of technology isn’t that it makes things faster or cheaper; it’s that it fundamentally changes who gets access. For decades, access to expertise has been constrained by geography, cost and availability of professionals. If you needed medical advice, psychological support or even something as seemingly simple as a vision … Read more
Over the past decades, software-intensive systems companies have gone through several major paradigm shifts. We moved from hardware-centric products to software-defined systems, from waterfall to Agile, from projects to products and now superset platforms, and from episodic releases to continuous deployment. When each of these shifts became relevant, it was initially resisted and, intentionally or … Read more
One of the most persistent misconceptions I encounter when discussing data and AI is the idea that they’re primarily about better predictions, smarter dashboards or more advanced analytics. While all of these have value, they miss the deeper point. The real strategic power of data and AI lies in their ability to enable continuous learning … 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
