Jan Bosch
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
After the business process maturity ladder and the first three steps on the R&D maturity ladder, ie AI assistants, AI compensators and AI superchargers, we discuss the fourth level: the AI system generator. Here, the intent is to go through a fundamental shift from augmenting humans in their roles to fully autonomous end-to-end creation of … 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
Software engineering has been one of the industries most actively adopting AI. There are few, if any, companies in this discipline that aren’t looking at large language models (LLMs) in some way, shape or form. For most, however, the technology is predominantly used in the lowest step of our maturity model, ie the AI assistant. … 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
As part of this series, we look at three maturity models: AI in the product, AI in product development and AI in the business and business processes. For the latter maturity model, we’ve now reached the final stage, the AI-first organization. In many ways, this is our definition of Nirvana, where every activity in the … Read more
