With the digital transformation, companies increasingly engineer systems that evolve throughout their economic life, causing systems and software engineering to become virtually the same discipline. We need to remove the hard distinction between the two. janbosch.com/blog/index.php…

Many companies have an espoused purpose that has little to do with their day-to-day operations. Companies must embrace their purpose and put a stake in the ground as to what they stand for. You can’t be everything to everyone. You need to make choices. janbosch.com/blog/index.php…

Rather than treating customer support as the stepchild in the organization, it should be treated as one of the most valuable sources of market feedback. This requires R&D and customer support to be integrated, similar to DevOps. janbosch.com/blog/index.php…

R&D teams in many companies have a much closer relation with their customers than with the rest of the company for which they work due to aligned business models, continuous value delivery and continuous qualitative and quantitative feedback loops. janbosch.com/blog/index.php…

When adopting DevOps, teams tend to become more cross-disciplinary because Intra-team coordination is orders of magnitude more efficient. The challenge is scale: when organizations are large and consist of many teams as close collaboration is needed. janbosch.com/blog/index.php…

Innovation affecting existing products should be integrated into the development process. With the adoption of DevOps, software-intensive companies have a fast data-driven feedback loop with the products in the field that allows for new ways of working. janbosch.com/blog/index.php…

Traditionally, there were very clear differences between product and service companies, included the nature of the customer relationship, business model and type and timing of R&D investments. With digitalization, this distinction is becoming irrelevant. janbosch.com/blog/index.php…

DevOps requires a a superset platform, removing products from an R&D perspective. Each product is a configuration of the platform. Consequently, the distinction between platform and product is disappearing and is rapidly becoming outdated. janbosch.com/blog/index.php…

Sales and R&D traditionally have an antagonistic relationship. When digitalizing the offering to the customer base, this needs to change as the business model and the product need to be deeply integrated to ensure revenue and growth opportunities. janbosch.com/blog/index.php…

In a world in rapid change, we need to be careful not to get stuck with a set of outdated concepts. In this series, I hope I’ve managed to have you rethink some of the concepts you use in your day-to-day life and work. We need a new paradigm! janbosch.com/blog/index.php…

For all the promise of AI and specifically agentic AI, companies and individuals experience fear, uncertainty and doubt, causing slow adoption of AI. In this series, we discuss the challenges and the evolution of companies, R&D processes and products. janbosch.com/blog/index.php…

For all the promise of agentic AI, we see that the adoption beyond the individual in most companies is still quite limited. Beyond the generally low innovation absorptive capacity of companies and industries, there are several challenges to be considered. janbosch.com/blog/index.php…

Many companies struggle with identifying and realizing AI use cases. This is caused by a variety of factors such as technology understanding. Companies need to clearly assign ownership for AI initiatives and provide sufficient mandate to the AI team. janbosch.com/blog/index.php…

Adopting AI models and AI agents requires companies to mature their data practices, requiring many decisions on what to collect, at what frequency, where to store it, how to process it and how to make sure it can be used both for training and inference. janbosch.com/blog/index.php…

The third challenge to becoming an AI-driven organization is organizational and cultural. Companies that don’t fundamentally reinvent themselves are simply going to be outcompeted by the new entrants and incumbents that do. You simply have no choice. janbosch.com/blog/index.php…

The fourth main area of challenges for companies seeking to become AI-first is regulatory compliance. There are five main concerns: difficulty of interpretation, risk avoidance, need for human oversight, non-deterministic behavior and lack of automation. janbosch.com/blog/index.php…

In this post, I present a maturity model for how companies evolve and develop using AI through five stages: playtime, automation 2.0, local AI first, super-agents and the AI-driven company. Please share your thoughts and insights! janbosch.com/blog/index.php…

Playtime is the first stage in our maturity model and the impact on individual professionals can be quite profound. E.g. automating routine cognitive tasks, managing information overload, improved decision-making, sparring or task execution assistant. janbosch.com/blog/index.php…

The second step in our maturity model is automation 2.0. Here, we automate or significantly support individual steps in a business process that couldn’t be automated without the help of AI agents. We discuss three main challenges and three main enablers. janbosch.com/blog/index.php…

In the third step, “local AI-first,” we adopt a zero-based design approach, rethinking the business process from the ground up. This reengineering starts from: if we were to design this process for AI from the start, what would it look like? janbosch.com/blog/index.php…