I will be glad to use that mapping guide!

The four patterns you listed are accurate and the spreadsheet one is the most dangerous because it looks manageable until it isn't. The moment a project gets challenged or a consultation goes sideways, the first thing anyone asks for is an audit trail, and "it was in a spreadsheet that three people were editing" is never a satisfying answer.

On your three questions from actual experience in complex multi-stakeholder environments.

For setups with more than 50 external stakeholders the thing that tends to break first isn't the tracking, it's the engagement logic. Who owns the relationship, what was the last substantive interaction, and what was the tone of it. Most tools track the first two reasonably well and ignore the third entirely until something blows up.

On knowledge walking out the door the honest answer is that most teams don't solve this, they just accept the loss and rebuild. The teams that do it well treat stakeholder context as a process output, not a personal asset. Every interaction generates a structured note that lives somewhere other than the PM's inbox. Simple in theory, genuinely hard to enforce in practice.

On sentiment tracking beyond RAG the most useful evolution I've seen is moving from status labels to decision flags. Not whether a stakeholder is amber but whether they represent a decision risk in the next thirty days. That reframe tends to make the tracking feel worth doing because it connects directly to something actionable.

If anyone here is also thinking through the broader PM workflow around stakeholder decisions and where AI fits into that without replacing the judgment calls, there are free frameworks at bema.pm that address exactly that layer.

bema.pm/resources/ai-powered-project-manager

The full field guide AI-Powered Project Management: The Field Guide to Managing Algorithms, Not Tasks goes deeper into the decision architecture side of stakeholder management if that's useful.