Context: Cribl is the data engine for IT and Security, scaling fast with an executive AI vision that needs to translate into daily adoption across every function. This interactive prototype walks through how I'd operate the AI CoE in the first 90 days, using the same function-by-function activation framework I've run at Netflix. Click through the tabs to see the diagnostic, roadmap, governance model, and measurement approach.
Every function has a different AI readiness profile. Activation sequencing matters. This is my initial hypothesis based on the JD and Cribl's operating model. Would refine rapidly with real data in weeks 1-4.
High volume of repetitive tasks (discovery prep, demo scripts, competitive battlecards). LLM copilots and AI knowledge assistants deliver immediate leverage.
QBR prep, health scoring narratives, renewal positioning. Structured, pattern-rich work where AI delivers real time savings and quality uplift.
Forecasting support, deal analysis, reporting automation. Strong ROI but requires governance partnership on CRM data sensitivity.
Content ideation, campaign analysis, persona workshopping. High readiness, moderate ROI, good candidate for quick Champions Network win.
The JD names this as a key co-design partner. People Ops workflows (onboarding, perf cycles, policy authoring) compound when AI-native.
High ROI in vendor analysis and reporting automation, but governance and data sensitivity require more prep before activation.
Extremely high readiness, already using AI tools natively. CoE role here is governance and knowledge sharing, not activation.
Legal is a governance working group member more than an activation target. Partnership over program for now.
Programs that aren't measured don't earn the right to grow. Three tiers of metrics, each answering a different question leadership will ask.