In four parts, we opened up the picture. We dissected the application zoo, outlined the greenfield architecture, reframed the make-or-buy question in the age of AI, and designed the cost-optimized symbiosis. What remains is the most uncomfortable question of all. How do we actually get there without a Big Bang, while keeping operations running, with a business that still needs to function tomorrow?
By office
Four building blocks, no ideological battles: Why license costs are only the symptom while architecture determines the real bill.
Custom development has become cheaper – but later and more unevenly than the hype suggests. That shifts the make-or-buy boundary, but it does not make “build everything yourself” the smart strategy.
In Part 1, we dissected the application zoo and showed why the best-of-breed logic of the 2000s no longer holds up. Diagnosis without therapy is unsatisfying. So in Part 2, we take the thought experiment further: If we could start today with a blank whiteboard, what would our recommendation look like?
Before talking about new software investments, it’s worth understanding what’s already in place. And that’s exactly what very few companies truly know. With this article, we’re launching a five-part series that rethinks the tool landscape in mid-sized businesses—self-critical and with the courage to start from a clean slate.
PLM selection without methodology is like building without blueprints. From strategic preparation through MoSCoW requirements to PoC and TCO assessment: how a structured 7-phase selection process works in practice.
40–70% of PLM projects miss their targets. Not because of the software – because of the methodology. The 5 cost traps, the 4 forgotten integration points, and why PLM selection is a 15-year decision that demands leadership.
Companies invest in AI, but often fail due to poor data quality and a lack of system integration. Without clean, connected foundations across ERP, PLM, and CRM, efficiency and innovation fall short. Only a solid data foundation truly unlocks the value of technology and AI.
Many AI projects don’t fail because of the technology — they fail because of chaotic data and siloed systems. Modern ERP and CRM systems are becoming the critical foundation for successful AI initiatives and sustainable digitalisation.
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