Jan Bosch
For most of the history of software engineering, we’ve operated under a deceptively simple model. Engineers specify behavior. Systems execute it. When the behavior is wrong, engineers fix it. When requirements change, engineers rewrite it. Between releases, the system is inert. It doesn’t learn from what it observes in production. It doesn’t adapt to how … Read more
For decades, software quality has been a solved organizational problem, or at least a well-understood one. Teams write tests. Tests run automatically. When a change breaks something, the pipeline catches it before it reaches production. This discipline, built up painfully over thirty years of software engineering practice, is why modern development teams can ship multiple … Read more
Over the last decade, many of the companies I work with through Software Center have made significant investments in data. Sensors have been deployed, systems instrumented and pipelines built to collect and store vast amounts of information. In principle, this should provide a strong foundation for data- and AI-driven innovation. In practice, however, a recurring … Read more
For decades, regulatory compliance has been treated as a necessary burden. It sits adjacent to engineering rather than inside it. Teams build products and, at some later point, documentation is assembled, controls are reviewed and auditors are invited in to determine whether the organization meets the relevant standards. Compliance becomes an event, a checkpoint on … Read more
One of the defining shifts in modern software development has been the gradual automation of feedback. We no longer wait for quarterly reviews to understand performance; we instrument systems, collect telemetry, run experiments and close loops continuously. Yet, one feedback loop has remained stubbornly manual: user feedback. From a Radical and continuous value delivery perspective, … Read more
The history of industry is littered with failed projects. Projects where a team was asked to build a product based on a specification, a budget and a timeline. And failed. According to the research I’ve conducted, 60-80 percent of all IT projects are considered failures in some sense, either because they failed completely or failed … 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
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 companies seek to adopt continuous practices, one of the claims I run into a lot is that customers don’t want DevOps. This argument is often used as a way to cut off the discussion as we obviously shouldn’t do what customers don’t want. Instead, we should keep things as they are as customers are … Read more
