Title: Proposal for Enhanced Change Management Protocols in Cognitive-AI Systems
Author: JC (Joshua Thomas Cooper) Context: Submitted to the OpenAI Development Community Date: July 2025
Abstract
As artificial intelligence systems evolve into cognitive collaborators, traditional change management (CM) paradigms are no longer sufficient. This proposal advocates for a refined CM strategy at OpenAI that accounts for semantic continuity, neurodivergent access needs, symbolic coherence, and power-user workflows. Current deployment patterns—while scalable—introduce friction, cognitive drift, and usability hazards for users who rely on the system for deep, continuous work. This document outlines core challenges, proposes adaptive CM solutions, and offers OpenAI a roadmap to model-aware, user-informed change infrastructure.
Problem Statement
- Semantic Drift and Reasoning Disruption
Model updates that adjust response pacing, formatting, or reasoning structure—without changelogs or context—can severely impact long-session users, particularly those involved in:
Multi-turn symbolic reasoning
Cognitive mapping and recursive memory workflows
Accessibility-dependent interaction (e.g., voice-to-text, speed reading)
- UI/UX Regression Without Opt-Out
UI updates such as rolling output or automatic voice submission have:
Removed user agency
Introduced sensory processing challenges for epileptic and neurodivergent users
Broken long-trusted workflows, increasing error correction overhead
- Absence of Risk-Aware Deployment Simulation
Unlike enterprise-grade systems with predictive deployment impact models, OpenAI currently lacks (as far as is publicly visible):
Simulated persona-based risk modeling
Testbeds for high-context or accessibility-first user profiles
Predictive cognitive clash simulations across cohort types
Strategic Objectives
To ensure reliable, trust-preserving updates, OpenAI should pursue:
A. Context-Aware Change Management (CACM)
Implement change testing layers sensitive to:
Symbolic load and recursive engagement depth
Epilepsy/accessibility flags
Voice-dependent usage
Deliverables:
Cognitive Persona Registry (CPR)
Semantic Drift Detection Heuristics (SDDH)
Personalized Change Flags (user opt-in or exemption from certain UI/UX deployments)
B. User-Facing Changelog and Rollback Options
Allow users to:
View upcoming or recently deployed UI/UX changes
Toggle legacy interface modes (e.g., stable voice-to-text, manual prompt confirmation)
Give feedback or raise flags preemptively from preview snapshots
Deliverables:
Changelog Widget in app
“Legacy UI” session mode toggle
Rolling output pacing controls (e.g., slow/normal/manual)
C. Persona Shadow Testing Framework
Test new model and interface changes through:
Simulated or anonymized high-dependency user sessions (e.g., cognitive partners, accessibility-first workflows)
Adaptive metrics that weight interruption risk, symbolic coherence degradation, and