Over time, working across enterprise AI deployments, we have identified four root causes that account for the overwhelming majority of AI accuracy breakdowns. We call this diagnostic framework ARPO. Understanding it doesn't require a technical background. It requires an honest assessment of how your organization manages knowledge.
The Context Gap: What It Is and Why It Matters
Most organizations have context scattered across SharePoint sites nobody reads, documents that contradict each other, tribal knowledge held inside specific employees, and process documentation that hasn't been updated in years. When AI systems are pointed at this environment, they surface the chaos embedded in it.
This is the Context Gap: the distance between what an organization knows and what its AI systems can reliably access and use. Closing it is not a technical project. It is an organizational discipline problem.
Enterprise AI accuracy tends to stall somewhere between 65 and 75 percent, not because the models are incapable of doing better, but because the knowledge environment feeding them is too inconsistent to support higher accuracy. The ceiling is set by the context, not the capability.
For a full breakdown of why accuracy stalls and how ARPO explains the root cause of AI failure, see our white paper: The Enterprise Context Center of Excellence Imperative.
Introducing ARPO: Four Root Causes of AI Accuracy Failure
ARPO is a diagnostic framework built around the four failure modes that appear, repeatedly, in enterprises attempting to scale AI. It is not a theoretical model. It is a description of what we observe.
Knowledge exists but is unreachable, locked in people, siloed in systems, or simply never captured.
Knowledge is accessible but the wrong content surfaces, outdated, irrelevant, or stripped of context.
Knowledge exists and is retrieved, but there is no way to verify what is authoritative, current, or approved.
AI is deployed but no one is accountable for accuracy over time. Degradation is gradual and undetected.
Access: The AI Cannot Use What It Cannot Reach
The most critical procedures sit inside a longtime employee's head. The most current policy lives in an email archive. The most relevant institutional knowledge was never written down in the first place.
If knowledge is not captured in a form the AI system can access, the model cannot use it, regardless of how capable the model is. Access failures are often invisible at the pilot stage, when the scope is narrow and the team curates inputs carefully. At scale, the gaps surface fast.
Solving Access requires building repeatable processes for capturing context, not as a one-time documentation project, but as an ongoing organizational discipline. The organizations that do this well treat knowledge capture the way others treat code review: systematic, assigned, and reviewable.
Retrieval: The AI Grabs the Wrong Information
In many enterprise deployments, the AI technically has access to the right documents. It still produces wrong answers because it retrieves the wrong ones. Search returns an outdated procedure. The model surfaces a superseded policy. Chunked content loses its meaning when extracted from context.
Retrieval failures are where many RAG (retrieval-augmented generation) implementations quietly underperform. In demos, the retrieval environment is controlled. In production, the AI is rummaging through an ungoverned archive where relevance signals are weak and content quality varies widely.
The model is doing its best with what retrieval gives it. The retrieval is giving it the wrong things.
One practical approach to retrieval governance is what we call an AI Bill of Materials: a curated bundle of context that defines precisely what an AI system is authorized to draw from for a given task or domain. Rather than letting the AI retrieve freely from the full knowledge environment, the Bill of Materials creates boundaries, not to limit capability, but to improve reliability.
Provenance: The AI Cannot Tell What to Trust
An AI system encounters two documents making conflicting claims. Both appear in the knowledge base. Neither has clear context about authorship, recency, or approval status. The model has no reliable signal for which one is authoritative.
In this situation, the model will produce an answer. It may be the right one. It may not be. The organization has no way to know and no way to trace the reasoning afterward.
Provenance is the condition that makes traceability possible. Without it, AI governance becomes a theoretical ambition rather than an operational reality. You cannot audit what you cannot trace. You cannot trust what you cannot verify.
"If you cannot trace it, you cannot trust it. And if you cannot trust it, scaling it is not a technology decision, it is a risk decision."
Organizations with strong provenance practices maintain clear version histories, explicit ownership of documents, and consistent metadata standards. This is not glamorous work. It is, however, the foundation that makes AI reliability verifiable rather than assumed.
Oversight: Nobody Is Accountable for Accuracy Over Time
The system is deployed. It works reasonably well in the first weeks. Then the underlying knowledge environment changes, a policy updates, a procedure shifts, a product line changes and nobody updates the AI context to match. Performance degrades slowly, and nobody notices until the failures accumulate.
This is the most common failure mode in mature deployments: not a dramatic breakdown, but a gradual drift that nobody owns.
Oversight means establishing clear accountability for AI accuracy as an ongoing operational concern. It means quality metrics that surface degradation early. It means feedback loops that allow the people closest to the work to flag when AI outputs are wrong. It means someone is, in fact, on the hook.
For a deeper exploration of ARPO, how to operationalize context, and a roadmap to enterprise-level AI accuracy, download the whitepaper: The Enterprise Context Center of Excellence Imperative.