1. Introduction: The Crisis of Enterprise Amnesia in the GenAI Era
By 2025, enterprise technology is undergoing a structural shift as generative AI becomes embedded in critical workflows. Yet one problem is growing rather than shrinking: institutional memory. Institutional memory is the accumulated knowledge, decision history, context, tacit practices, and precedents that allow an organization to act as a coherent system instead of fragmented individuals. Modern AI systems can reason, but they do not remember. Large language models are trained on public data; they do not know why a legal partner rejected a clause three years ago or which implicit agreements shaped a client relationship. That gap creates a new bottleneck: AI can generate fluent answers, but it cannot preserve the reasoning history that makes those answers dependable over time. The economic consequences are significant. In professional services, knowledge chaos turns into direct cost. High-paid experts spend large portions of their time searching for existing knowledge rather than using it. Tacit knowledge loss when key staff leave is costly because it erases the logic behind past decisions. This report analyzes why this happens and why current architectures fail. It also explains why temporal knowledge graphs represent the only credible path to durable enterprise memory.2. Context Window Limitations: The Illusion of Infinite Memory
2.1 The “Lost in the Middle” problem
Expanding context windows to millions of tokens looks like a shortcut, but retrieval accuracy drops when critical facts sit in the middle of long contexts. Models show strong primacy and recency bias; they remember the beginning and the end, but not the middle. In compliance or legal scenarios, that is unacceptable.2.2 Context decay and signal noise
Long-running interactions accumulate irrelevant details, stale instructions, and side discussions. The signal-to-noise ratio collapses. The system must prune or summarize, which deletes details that may become critical later. That destroys institutional memory integrity.2.3 Cost and latency
Large contexts are expensive. Attention still scales poorly, and time-to-first-token increases with context length. A million-token prompt for every query is economically and operationally infeasible. Summary: long context is useful for a single deep task, but it is not a durable memory layer. It behaves like RAM, not a hard drive.3. Persistent Memory Failures: The Vector search crysis
3.1 Semantic similarity vs structural truth
Vector databases retrieve similar text, but they do not understand temporal or causal structure. They cannot tell that a policy from 2024 supersedes a policy from 2023. The system returns both, and the model must guess which is true.3.2 Catastrophic forgetting in fine-tuning
Embedding knowledge into model weights causes forgetting. New training overwrites old knowledge. Continuous fine-tuning is expensive and destabilizing.3.3 The “right to be forgotten”
Regulations like GDPR require guaranteed deletion. Deleting a vector is not enough if the fact remains encoded in other chunks or in model weights. Machine unlearning is not reliable at scale.3.4 Missing decision memory
RAG stores artifacts, not decisions. Enterprises need to preserve the reasoning trail: who decided what, when, why, and with what outcome. Documents alone do not preserve this logic.4. Enterprise AI Landscape: Why Leaders Still Fail Memory
4.1 Horizontal enterprise search
- Glean builds an enterprise graph but focuses on search and access, not decision causality.
- Microsoft Copilot (M365 Graph) is strong at short-term context but weak at long-horizon reasoning.
- Salesforce Data Cloud / Agentforce excels at transactional data but misses tacit knowledge outside CRM systems.
4.2 Vertical solutions (LegalTech and AuditTech)
- Harvey and CoCounsel know case law and uploaded documents, but not a firm’s internal reasoning history.
- Legora and Ironclad enforce playbooks but do not preserve the exceptions and rationale that define real institutional knowledge.
5. The Temporal Knowledge Graph Paradigm
5.1 From documents to events
Temporal graphs shift the unit of memory from documents to events. Instead of indexing a file, the system captures the decision event, the actor, the timing, and the causal link. This preserves the decision chain, not just the artifact.5.2 The Membria approach: Who, What, When, Why
Membria structures institutional memory as:- Who: actors and decision owners
- What: decisions, artifacts, and actions
- When: precise timestamps
- Why: causal links and supporting evidence
- Outcome: the eventual result
5.3 Bi-temporality and invalidation
Temporal graphs store two times for each fact:- Valid time: when the fact was true in the real world
- Transaction time: when the system learned it