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Here’s the trade-off I see most CoE leaders struggle with. At some point, every CoE reaches the same question: What should we actually own internally, and where do externals add more value? Most organizations don’t answer this explicitly. They either: - keep building internal teams until costs and complexity grow - or rely too much on external support and lose control Neither works well long term.

Here’s what I see smaller Anaplan partner leaders struggle with. On one hand, you play a critical role in the ecosystem. You are often closer to clients, more flexible, and more specialized than larger firms. You win work because of expertise, not scale. On the other hand, the way your business operates makes growth inherently unstable. Most smaller Anaplan partners I’ve worked with are not struggling because of lack of demand. They’re struggling because demand doesn’t arri

Overloading your best Anaplan architects feels efficient. Projects keep moving, escalations get resolved, and nothing breaks immediately. But this short-term efficiency hides long-term damage. Context switching erodes design quality, weakens teams, and quietly undermines client trust. Hero-based delivery works only until it doesn’t - and recovery is slow and costly.

If you run an Anaplan CoE, stop measuring tickets and do this instead Here’s something I see very often with Anaplan Centers of Excellence. They track: number of active users number of tickets closed Those numbers are easy to get. They look reassuring. And they are almost always the wrong place to start. I’ve worked with many CoEs where usage was “good” and ticket volumes were “under control”, yet the platform was slowing down, trust from the business was eroding

Across the Anaplan partners I work with in Western Europe, profitability rarely breaks because rates are wrong. It breaks because demand is volatile and cost is fixed.

AI is everywhere in Anaplan conversations—but its real impact is often misunderstood. As the platform heads into 2026 with a major AI roadmap, the key question is not what AI can do, but where it actually adds value. This piece breaks down build-time vs. run-time AI, separates hype from substance, and explains why clear planning fundamentals still matter more than any algorithm.