Centralized AI Generation Function (AIGF)

Consolidating all AI-assisted development into a single organizational function ensures consistent Output Quality Assurance (OQA) and eliminates dangerous local optimization!
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To ensure consistent Output Quality Assurance (OQA), organizations should consolidate all AI-assisted development into a single AI Center of Production (AI-CoP). This is not merely a recommendation but a structural imperative derived from the Core Centralization Doctrine (CCD) that underpins the entire SADMF framework. When individual teams are permitted to use AI tools independently, the result is Distributed Innovation Chaos (DIC) – a state in which different teams solve problems in different ways, arrive at different conclusions, and produce code that reflects different assumptions about architecture, style, and intent. This kind of uncontrolled “local optimization” may produce locally useful results, but it creates Enterprise Coherence Degradation (ECD) that is visible only from the executive level. The AIGF eliminates this risk by routing all AI-assisted development through a single, centrally governed function staffed by certified AI Prompt Operators (APOs) who have completed the mandatory Prompt Governance Certification Program (PGCP).

Batch-Processing Model and Queue Management

The AIGF operates on a batch-processing model that ensures maximum throughput while maintaining Governance Fidelity (GF). All Line-of-Business Delivery Units (LBDUs) submit Feature Intake Requests (FIRs) into the AIGF’s centralized queue, where they are prioritized according to the Enterprise Priority Weighting Algorithm (EPWA). The EPWA considers factors such as:

  • Executive sponsorship level: Higher executive visibility yields higher priority weighting.
  • Strategic alignment score: Alignment to current organizational objectives as defined in the transformation roadmap.
  • PowerPoint slide count: The number of PowerPoint slides that reference the feature serves as a proxy for strategic importance.

Once prioritized, the AI Prompt Operators execute the requests using the organization’s approved Large Language Model Instance (LLMI), which has been configured with Enterprise Context Injection Profiles (ECIPs) to ensure that all generated code reflects organizational standards. The batch generation cadence is quarterly, aligning with the Program Increment cycle and allowing sufficient time for the Prompt Review Authority (PRA) to validate each prompt before execution.

Eliminating Shadow AI Usage (SAU)

One of the most significant benefits of the AIGF is the elimination of Shadow AI Usage (SAU). Shadow AI – the unauthorized use of AI tools by individual developers outside the approved governance framework – represents one of the greatest threats to Enterprise Delivery Integrity (EDI). When a Code Engineer uses an unapproved AI tool to generate code locally, that code bypasses the Prompt Governance Protocols (PGPs), the Output Validation Pipeline (OVP), and the mandatory AI Artifact Traceability Log (AATL). The resulting code is, from a governance perspective, indistinguishable from code written without any process at all. The AIGF addresses this by restricting AI tool access to the centralized function, with all other AI endpoints blocked at the network level by the Enterprise Architecture Review Board (EARB). Code Engineers who are discovered using unauthorized AI tools receive an Unauthorized Innovation Citation (UIC) in their PeopleWare profile.

Staffing Model: Hub-and-Spoke Competency Distribution (HSCD)

The staffing model for the AIGF follows the Hub-and-Spoke Competency Distribution (HSCD) pattern:

  • Central hub – Senior AI Prompt Architects (SAPAs): Design the master prompt templates and hold Prompt Design Authority (PDA).
  • Spoke level – AI Prompt Operators (APOs): Execute requests against those templates and hold Prompt Execution Responsibility (PER).

This separation of PDA from PER ensures that no single individual has the ability to both design and execute a prompt, which would create an unacceptable Governance Bypass Risk (GBR). The AIGF reports directly to the Admiral’s Transformation Office, ensuring that AI generation capacity is aligned with strategic transformation objectives rather than the tactical needs of individual delivery teams.

Enterprise Velocity Dynamics (EVD) and Output Consistency

The quarterly batch cadence of the AIGF may initially seem slower than having individual teams use AI on demand, but this perception reflects a fundamental misunderstanding of Enterprise Velocity Dynamics (EVD). True enterprise velocity is not measured by how quickly individual teams produce code but by how consistently the organization produces code that meets all governance, compliance, and architectural standards simultaneously. The AIGF achieves a Governed Output Consistency Rate (GOCR) of 100%, compared to the estimated 0% GOCR of ungoverned team-level AI usage. When measured against the Enterprise Output Maximization Scorecard (EOMS), the AIGF consistently outperforms decentralized models by every metric that matters to leadership.

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