Jan Bosch
As we approach the end of 2025, it’s clear that the context in which software-intensive companies operate is shifting faster than ever. Over the year, working closely with businesses in domains such as automotive, telecom, industrial equipment and security, I’ve seen a growing divergence. Some organizations are accelerating, embracing data and AI to transform their … 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
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
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
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
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
