AI-Native Maturity Assessment
Measure where your organization stands across the nine competencies that determine whether AI becomes how you work or remains something you are still trying to adopt. Five minutes. Actionable results.
This is the light version. Our full diagnostic includes hundreds of role-based data points, delivered as part of a Singularics engagement.
Our strategic outcomes are defined with enough clarity that AI could act on them without human interpretation.
Teams can explain how their daily work connects to the organization's declared strategic outcomes.
We have explicit, measurable acceptance criteria for our AI initiatives, not just goals.
We have redesigned workflows to include AI, not just added AI tools to existing processes.
Role definitions have been updated to reflect what work looks like with AI in the loop.
Our expectations for output quality, speed, and depth have been explicitly updated for an AI-assisted world.
We can see, at any given moment, which strategic outcomes have execution coverage and which do not.
We detect misalignment between strategy and execution in real time, not in quarterly reviews.
We measure AI adoption by behavior change, not by licenses deployed or training sessions delivered.
We know who in our organization is using AI, how, and with what results.
We have identified and supported internal AI champions who drive adoption peer-to-peer.
Our AI governance defines boundaries people can move freely within, not gates that require approval for each use case.
We have a clear strategy for handling sensitive data with AI that does not depend on sending it to third-party APIs.
Teams can determine whether a specific AI use case is within governance bounds without asking a committee.
Our executives can articulate what AI means for our organization beyond 'we need to adopt AI.'
We have funded organizational change management at the same level as AI technology investment.
What one team learns about AI adoption is systematically shared with other teams.
We update our AI approach based on what we learn, not based on the original roadmap.
We can point to specific ways our AI practices have evolved in the last 90 days.
People in our organization can get quality output from AI tools on the first or second attempt, not the fifth.
Our team members can tell when AI output is wrong, even when it looks plausible.
AI fluency expectations are role-specific: we define what good AI use looks like for POs, engineers, executives, and other roles differently.
Our leaders understand AI capabilities and limitations well enough to set realistic expectations for AI initiatives.
We can articulate why certain tasks are well-suited for AI and others are not, without defaulting to 'AI can do everything' or 'AI cannot be trusted.'
Our organization makes informed decisions about AI vendors, models, and integration approaches based on technical understanding, not just marketing.
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