Foundeon Diagnostics
Enterprise Identity Readiness Assessment
EIRA evaluates whether your institutional logic, ownership, governance, and decision boundaries are explicit enough for AI to operate from your organization's own meaning, rather than inferring it from defaults.
6 minutes
A focused sitting, not a survey.
Six
The foundations AI operates on.
Free
Yours to keep and to share.
The Six Dimensions
What EIRA evaluates
- 01
Trade-Off Articulation
How competing priorities are explicitly ranked
- 02
Decision Boundary Definition
Where automation ends and human judgment begins
- 03
Institutional Logic Digitalization
Whether operating logic is machine-actionable
- 04
Ownership & Accountability Clarity
Who governs which decisions and data
- 05
Governance Maturity
Whether standards persist and drift is detected
- 06
Delegation Preparedness
What should and should not be handed to AI
Readiness Segments
Generate your Enterprise Identity Readiness Report.
Rate each statement against your current-state evidence, not your target-state ambition. Your answers will produce a readiness score, segment, dimension profile, and board-ready summary.
- Readiness score
- Segment classification
- Dimension breakdown
- Board-ready line
- Recommended next step
Trade-Off Articulation
Our organization can explicitly describe how we prioritize competing objectives (e.g., speed vs. quality, cost vs. customer experience).
When AI systems face conflicting priorities, we have documented decision hierarchies that reflect institutional values.
Trade-offs that define our competitive positioning are formalized beyond individual judgment.
Decision Boundary Definition
We have clear, documented criteria for when decisions should be automated vs. require human oversight.
Escalation thresholds are explicit and consistent across similar decision types.
Authority boundaries for AI systems are defined architecturally, not just in policy documents.
Institutional Logic Digitalization
Our institutional 'way of doing things' is documented in machine-actionable formats, not just prose.
Domain concepts (customer, risk, value, quality) have consistent definitions across systems and teams.
Strategic intent translates into specific, measurable parameters that systems can honor.
Ownership & Accountability Clarity
Decision rights and data ownership are explicit enough that AI systems know which authority governs which domain.
When institutional knowledge exists, we can identify who owns it and who is accountable for its accuracy.
Knowledge management extends beyond documentation to operational ownership structures.
Governance Maturity
Our governance is institutional rather than project-based (standards persist beyond individual initiatives).
Data quality and semantic consistency are enforced systematically, not case-by-case.
We have mechanisms to detect when AI systems drift from institutional intent.
Delegation Preparedness
Before deploying AI in a workflow, we articulate what success means beyond efficiency metrics.
We can distinguish between what should be delegated to AI and what requires human judgment.
Our AI deployment strategy accounts for institutional coherence, not just technical capability.
Answer all 18 statements to see your score.