Do less work. Create more value.

AI can now take part in many tasks: researching, analysing, specifying, coding and testing. For product teams that raises a concrete question: how do we organise our work so that these capabilities turn into better products?

Our perspective at Teklens starts with the work between the tools. Customer feedback, product decisions, Jira and code have to fit together. Where shared context is missing, someone has to search for information, reconstruct decisions and keep handovers up to date. That coordination work is exactly what we want to reduce.

Our manifesto describes the principles for this: clear goals, human judgement, shared context and continuous learning. People decide what matters and own the decisions. AI takes on suitable work. What the team learns along the way feeds into the next step.

The AI Monitor adds an empirical assessment to this perspective. It examines six preconditions for using AI: leadership, people and capabilities, technology foundations, data, external conditions and the ability to adapt. The self-assessments analysed show a tension: openness and support are comparatively strong. The ability to adapt structures and processes lags behind.

For us, that is a reason to look more closely at how work gets done. Which task contributes to the desired result? Which can be dropped? Where is knowledge missing? Who decides and approves? Only once these questions are settled does it make sense to decide what should be simplified, accelerated and automated.

This edition makes the results accessible. You can explore the six dimensions, compare readiness profiles and trace what individual statements rest on. The data offers orientation. The self-assessments do not replace an examination of your own workflows.

Our invitation: pick one workflow with your team where AI should create a concrete benefit. Clarify the goal, check the preconditions and record what improves. That is how a way of working emerges that learns from every experience.