Hi Lis P., We built something like this at LifeHack. One thing I'd flag — your spec is really two different jobs. The first is enablement — use cases, training, pilots, tool eval. That's a real job but it's a finite one. You push adoption, it works, people start using AI. Done. The second job is what shows up after adoption succeeds. Everyone's using AI, but the output across teams doesn't match, nobody's maintaining shared standards, and someone's spending hours reconciling everything before it can ship. That job isn't in your spec but it's the one that'll eat this role alive by month 6. At LifeHack the role started as Job 1 and quietly became Job 2. If I were writing this spec again I'd scope them separately — or at least decide up front which one this person actually owns. Because "measuring adoption" and "measuring business impact" will point this person in opposite directions pretty fast. How are you thinking about success criteria for this — what does "it worked" look like in 6 months?
Naren I just watched the demo — the Slack-native approach is smart, especially for smaller teams who aren't going to check yet another dashboard. Genuine question: who specifically is receiving these alerts? A founder wearing the finance hat needs a different message than a RevOps person. Right now it feels a bit one-size-fits-all. Might work well for startups where one person IS the revenue team — but worth being deliberate about that.
Naren Yeah, the 20-min lag example — picture this: prospect views the contract a third time at 1am, DocuSign webhook fires instantly, your engine tags it high signal. But the AE had a call 30 min earlier where the prospect said they're going with someone else. AE updated the CRM to closed-lost at 12:45. Your last CRM poll was 12:40. So your engine sees: 3 views in 36h + active deal + late-stage + assigned owner. Everything passes. Manager gets a "high intent" alert on a deal that's already dead. At your current scale it's probably rare enough to shrug off. But when you've got 50 integrations with different refresh rates, every signal is potentially sitting on stale context somewhere — and the manager can't manually verify each one without defeating the purpose of the engine. That's why I'd bake staleness into scoring now rather than later. Doesn't need to be complex — even just flagging "CRM context is X min old" in the confidence output changes how the recipient treats it. System that knows what it doesn't know compounds trust. System that presents stale context with full confidence erodes it. I agree with targeting managers, that's the right call. Signals need someone with authority to act, not just observe. The thing I'd watch for: if every signal routes to one manager, you've just moved the drowning problem up a level. The architecture question is really about ownership routing — signal goes to the person who can close it, manager sees the pattern across signals, not every individual alert. That's what separates a decision engine (which I believe you're building towards) from a "smarter" dashboard. Happy to see the demo — send it over whenever!
I agree, Marketing AI seems to be the biggest gap. Sales and Support have clearer feedback loops — you know fast when it breaks. Marketing AI can drift for months before anyone notices the damage.
The detail here is helpful! Refresh mismatch is where things crack, yeah. The problem isn't staleness — it's if/when the decision fires before context catches up. So for example, 20-min CRM lag + real-time webhook = system potentially acting on partial truth. Implications really depend on the specific customer/service it's for. "Good enough" coherence to me means the system knows what it doesn't know. Confidence degrades when context is stale, not just when signals are weak. Is staleness a scoring input for you yet, or just signal strength? Btw — is this a product you're building or internal for a company?
Interested in this — the cross-system noise problem is real. What's your approach to maintaining signal coherence when the sources have different data schemas/refresh rates? That's usually where decision engines either compound or fragment.
