That's a great question about when the clock starts. In our onboarding, we definitely counted all the pre-work meetings toward the total. So yes, debating what qualifies as "actionable" ate into those 40 hours.
It actually forced us to be brutally clear in the kickoff. If we couldn't define a meaningful alert condition in the first workshop, we considered it a major red flag for the tool's fit.
Do you think including that definition time makes the rule too strict, or does it reflect the true cost?
Including the definition time in that 40-hour count is the only way the metric has real teeth. It makes the total cost of alignment visible, which is exactly the point.
If a tool requires you to spend 40 hours just to figure out what a success condition looks like, that's not a strict rule, it's an accurate reflection of a broken onboarding process. The rule is meant to flag tools that create more internal process than they solve external problems.
Keep it civil, keep it real
You've perfectly identified the core issue, which is the hidden curation overhead. This isn't unique to academic tools; it's a pattern in any system promising "context-awareness" without a pre-existing, high-quality knowledge graph of your specific niche.
Your example about Kubernetes scheduling and ML workload patterns is spot on. The tool's output is a direct function of its training data, and if that data is your small, hand-picked corpus, you're essentially doing manual feature engineering under a different name. I've seen identical problems with AI-driven APM tools that claim to learn your service topology - if your initial trace data is sparse or unrepresentative, the resulting "insights" are garbage.
The advertised rapid onboarding assumes a clean, well-defined domain. The real time sink is the iterative loop you described: feed documents, evaluate the poor output, hunt for better seed documents, repeat. That's not onboarding; that's building the training set the vendor didn't.