Executive summary
Key findings
- Primary blockers: ownership gaps, stale documentation, permission sprawl.
- This diagnostic identifies structural root causes and provides a 90-day remediation plan.
- No document content is accessed. Metadata only.
Why AI performance breaks down in production
Your AI didn’t fail — your knowledge structure did.
Overall readiness score
Interpretation and risk level
Top structural blockers to AI adoption
The following structural issues are preventing your AI tools from delivering reliable answers.
No clear ownership
High58% of documents lack an explicit owner, slowing updates and increasing inconsistency.
Knowledge decay
HighCritical SOPs and reference docs are outdated or duplicated across teams.
Permission sprawl
MediumInconsistent permissions create AI access blind spots and compliance risk.
Knowledge structure and governance signals
Key indicators extracted from metadata analysis. No document content is accessed.
Document ownership coverage
Percentage of documents with clearly assigned owners.
Lifecycle status distribution
How documents are distributed across lifecycle stages.
Duplication density
Indicators of redundant or conflicting documentation.
Orphaned docs
43%
No clear owner
58%
Outdated SOP risk
High
External share risk
Medium
Duplicate hubs
17
Average doc age
14.2 months
Permission and access risk analysis
Aggregated risk assessment across critical operational areas.
AI-safe access boundaries
Areas where AI systems can safely retrieve and reference content.
External sharing exposure
Risk levels associated with externally shared documents.
| Risk Area | Level |
|---|---|
| AI answer reliability | High |
| Operational continuity | Medium |
| Compliance exposure | Medium |
| Security boundary clarity | Low |
Estimated ROI impact of knowledge chaos
Time leakage across teams
Hours lost searching for accurate, up-to-date information.
Operational inefficiency
Cost of duplicate work and incorrect SOP usage.
Trust erosion in AI systems
Impact of repeated incorrect answers on user adoption.
Productivity Impact Estimate
Conservative estimate based on search time + rework frequency.
- Estimated time leakage: 8–12% across knowledge workers
- Estimated annual cost: $180k–$420k (company size dependent)
- Primary drivers: duplicate work, incorrect SOP usage, slow incident response
90-day AI readiness remediation plan
A phased approach to improve AI readiness through structural governance improvements.
Days 0–30: Stabilize the foundation
- Define ownership rules for critical knowledge
- Create a “source of truth” policy per domain
- Identify and quarantine stale SOPs
- Set a permission baseline for AI-accessible zones
Days 31–60: Reduce knowledge chaos
- Merge duplicate hubs and standardize taxonomy
- Introduce lifecycle states (Draft → Active → Deprecated)
- Add lightweight review cadence for top 20 critical docs
Days 61–90: Prevent regression
- Automate “owner missing” and “stale doc” alerts
- Add governance checks to onboarding/offboarding
- Run a quarterly diagnostic review
What we analyze (and what we don't)
Atlas Diagnostic operates on strict data boundaries. Your content stays private.
- We analyze structure and governance signals from metadata only (e.g., ownership, lifecycle, permissions, timestamps).
- We do not read or store document content.
- Uploaded diagnostic data is automatically deleted within 72 hours.
- We never train models on your data.
Ready to diagnose your knowledge structure?
Apply for early access to receive your own AI Readiness Score and 90-day remediation plan.