Our go-to-market data (CRM, MAP, product usage, revenue) is unified into a consistent structure.
Your CRM, marketing automation, product usage, and revenue data should tell a consistent story. Consider how often you see mismatched numbers or have to reconcile dashboards manually.
How aligned are your definitions for key metrics across teams?
Metrics like pipeline, ARR, churn, attribution, and stage definitions should be standardized. Think about whether Sales, Marketing, Finance, and RevOps use the same definitions.
Do you have a clear semantic layer or business data model?
Metrics like pipeline, ARR, churn, attribution, and stage definitions should be standardized. Think about whether Sales, Marketing, Finance, and RevOps use the same definitions.
How easily can you explain why performance shifted?
When metrics go up or down, you should be able to identify true drivers quickly. Think about last quarter: how long did it take to reach a defensible explanation?
How aligned are your teams on the narrative of what’s happening?
Teams should rely on one shared story, not competing dashboards. Recall the last performance review — did teams argue over different interpretations?
How well can you break down performance by segment, product, or channel?
Granular insight is critical to identifying real opportunities and risks. Consider whether you can easily isolate changes by region, product line, or persona.
How well is your AI grounded in your real business data and definitions?
Trusted AI must reflect your economics, segments, rules, and terminology. If you're using generic copilots without business grounding, score lower.
How strong are the guardrails that prevent hallucinations or incorrect insights?
AI should not produce answers that are unverified, ungrounded, or inconsistent. Consider whether you trust the outputs enough to present them to leadership.
How well can your AI explain why it generated a specific insight?
Explainability is essential for defending decisions to executives and boards. If the AI gives answers without causal reasoning, score lower.
How quickly can you get business-ready answers supported by evidence?
When executives need clarity, the system should provide it in minutes. Think about how long performance investigations take today.
How actionable and grounded are your AI’s recommendations?
Recommendations should be tied directly to your GTM economics and constraints. Consider whether AI outputs actually influence decisions or just create noise.
How easily can your organization move from insight to action to measurable impact?
Execution should be consistent once clarity is established. Think about whether insights lead to aligned action across teams or stall in debate.
