Corporate Intelligence Survey | AI Native Systems Engineering | ATRISI

Built for operators, engineering leads, and transformation owners. This goes deeper than a short maturity score so ATRISI can recommend audit, pilot, or academy next steps.

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Organization / institution (optional)

Workflows

How work actually moves across teams today

How mature are your critical workflows for AI assistance today?*

Which workflow classes are highest priority to redesign?*

Rate how clearly your teams can describe a target AI-native workflow.*

Integration

Systems, data, and delivery interfaces

How ready are your systems for integrating AI into production paths?*

What integration surfaces are available today?*

Rate confidence shipping an AI feature into an existing production system in 90 days.*

Knowledge

Reuse of institutional memory and playbooks

How reusable is institutional knowledge for AI systems (playbooks, SOPs, cases)?*

Where does critical knowledge currently live?*

Decisions

How AI informs operating decisions

How often do operators use AI outputs in consequential decisions?*

Rate how well decision quality is measured after AI-assisted actions.*

Governance

Risk, policy, and adoption controls

How mature is AI risk / policy governance in your org?*

Which controls already exist?*

Capability

Team skills and hiring leverage

How strong is your internal team for AI-native systems engineering?*

Which roles are in place for AI delivery?*

Sponsorship

Executive mandate and budget reality

What executive sponsorship exists for AI-native transformation?*

Budget posture for a 90-day pilot / academy engagement?*

Who owns the commercial decision?*

Aspiration

The operating outcome you want in 12 months

What scale of outcome are you optimizing for in 12 months?*

Rate urgency to show a measurable operating win this quarter.*

If ATRISI could help your organization achieve only one operating outcome over the next 12 months, what should that be?*

Help us understand your operating system for AI-native work