Why most AI pilots stall, and what the ones in production did differently
The gap between demo and daily use is rarely the model. It is data, ownership and how the work is run.
Read article →Field notes from the teams doing the work.
Perspectives, guides and explainers on architecture, data, AI and engineering, written by the people who build and run enterprise platforms.
- Perspectives
- Guides
- Field notes
Ideas shaping technology, data and AI.
Field notes from the teams architecting and running enterprise platforms.
AI-ready data starts long before the model
What a data product needs in place before it can feed an AI use case.
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Clean core, explained for the people who fund it
What an upgrade-ready SAP S/4HANA core means for cost, risk and speed.
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One team, around the clock: running a platform across time zones
Overlap windows, structured handovers and the operating habits that keep a distributed team moving as one.
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Modernizing a legacy application without freezing the roadmap
How to keep shipping while the platform underneath is rebuilt.
Read article →What we write about
Practical writing for technology leaders and engineers, grounded in the platforms our teams work on.
Applied and agentic AI
What it takes to move AI from demo to daily use, and to keep it safe once it's there.
Data platforms
Architecture, governance and the habits that make data trustworthy enough to act on.
SAP and clean core
Decision-maker guides to S/4HANA, BTP and the choices that shape an SAP roadmap.
Digital engineering
Modernization, platform engineering and shipping software without drama.
Distributed delivery
Operating models, handovers and governance for teams that run platforms across time zones.
Industry field notes
What our teams see in banking, telecom, healthcare and energy programs.
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