Jan Bosch
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
As we’re taking ourselves through the R&D maturity model, we’ve reached the AI supercharger stage. This is where most of the team is substituted by AI agents. The idea is that an individual, or maybe a pair of people, supported by a set of AI agents, replaces the work of one or more Agile teams. … Read more
Product development requires several different skill sets. When focusing on the software part of a product, we can recognize the problem domain, technology, infrastructure and people as four main areas of competence. For a team to be effective, it needs to understand the problem or application domain so that it can realize new functions or … Read more
As part of this series on the AI-driven company, we discuss three maturity models that are the primary findings of the interview study we conducted during the spring. We completed the first one, AI in the business processes of the company. Now, it’s time to dive into the second maturity model, which focuses on the … Read more
