Author: Matthias Patzak

AI agents can’t work across teams because they lack the domain knowledge that exists only in developers’ minds (e.g., architectural patterns, business rules, design constraints). When agents make changes to another team’s code, they usually fail. Not because the agent lacks capability, but because it doesn’t know that team’s context. You could supervise the agent […]

“Agentic AI’s primary role in organizations is that of an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.” —2025 DORA State of AI-Assisted Software Development report AI coding assistants amplify your organization’s strengths—and its dysfunctions. Ninety percent of developers now use these tools to write code faster than before. […]

  “The speed of typing out code has never ever been the bottleneck for software development (not since keyboards became widespread from the 60s or 70s)” —Gergely Orosz Software development is a complex value delivery system involving many interdependent roles, including developers, product managers, and platform engineers. Dependencies create potential bottlenecks, such as pull request […]

In response to CFOs demanding returns on technology investments, CTOs must measure software development productivity across four dimensions—business value, speed to market, delivery reliability, and team health—to effectively demonstrate how their teams create measurable impact and business growth.

Software development teams have always struggled to deliver features within the timeframe that businesses and customers expect. A recent BCG study found that over a third of respondents’ internal software development projects were delayed. Late delivery delays ROI and frustrates everyone involved. Organizations face numerous challenges when accelerating software delivery, but one stands out for its outsized […]

Maximize AI’s value without breaking the bank – this guide explores cost-optimization strategies across the AI lifecycle, from model selection and data management to FinOps practices.

For maximum generative AI impact, centralize foundations (infrastructure, data governance) but decentralize innovation across business domains. This hybrid model balances robust AI governance with agile delivery, enabling rapid prototyping aligned with strategic goals.

For more than a decade, organizations have embraced data lakes to overcome the technical limitations of data warehouses and evolve into more data-centric entities. While many organizations have used data lakes to explore new data use cases and improve their data-driven approaches, others have found the promised benefits hard to achieve. As a result, the […]

Communication is a sign of dysfunction. It means people aren’t working together in a close, organic way. We should be trying to figure out a way for teams to communicate less with each other, not more. —Jeff Bezos, Executive Chairman of Amazon Traditional organizations focus on optimizing efficiency, trying to specialize and maximize the use […]

In the dynamic realm of software development, fostering a positive developer experience is critical to organizational success. However, certain practices inadvertently sabotage this experience, hindering productivity and innovation. Let’s examine these pitfalls and explore actionable strategies for IT leaders to overcome them and ensure a vibrant and efficient development ecosystem that attracts and retains talent. […]