EMRGF: A Practitioner Framework for Governance-Driven Enterprise Technology Modernization

• URL: http://arxiv.org/abs/2605.06703v1
• Date: Wed, 06 May 2026 03:37:20 GMT
• Title: EMRGF: A Practitioner Framework for Governance-Driven Enterprise Technology Modernization
• Abstract summary: This article presents the Enterprise Modernization Reliability and Governance Framework (EMRGF)<n>EMRGF comprises four interlocking modules -- Cloud and Legacy Modernization Governance, Data Platform Reliability Integrity, AI-Enabled Automation Governance, and Mission-Critical Reliability and Root-Cause Routines.<n>The framework is explicitly aligned with U.S. national policy mandates including CSF 2.0, AI RMF, and Executive Orders 14028 and 14110.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enterprise technology modernization programs fail at a documented and costly rate, yet the dominant explanation -- inadequate engineering capability -- is incorrect. The primary failure mode is a governance deficit: the absence of structured, repeatable operating routines for how organizations plan, execute, validate, and hand off complex technology change. Existing frameworks -- ITIL, COBIT, TOGAF, scaled agile methodologies, and cloud provider well-architected frameworks -- address adjacent concerns but do not provide an integrated, portable institutional operating model for controlled modernization across migrations, data platforms, and AI-enabled automation. This article presents the Enterprise Modernization Reliability and Governance Framework (EMRGF), a practitioner-developed governance operating model derived from 24 years of applied delivery experience across financial services, industrial manufacturing, and retail enterprises. EMRGF comprises four interlocking modules -- Cloud and Legacy Modernization Governance, Data Platform Reliability and Evidence Integrity, AI-Enabled Automation Governance, and Mission-Critical Reliability and Root-Cause Routines -- operationalized through five implementation tools and a training-of-trainers institutionalization model. Empirical application at scale has produced a 30% reduction in development effort, a 35% reduction in testing cycles, zero-disruption migrations across high-volume data estates, and 99.9% data reliability in mission-critical analytics pipelines. The framework is explicitly aligned with U.S. national policy mandates including NIST CSF 2.0, NIST AI RMF, and Executive Orders 14028 and 14110, and is designed for institutional adoption without ongoing external dependency.

Related papers

• AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities [55.23913617185508]
  Agent is an open-source, lightweight, and infrastructure for evaluating Large Language Models (LLMs)-based agents.<n>It organizes the evaluation process around three independent components, namely Benchmark, Harness and Environment.<n>It features a fault-tolerant runtime and comprehensive trajectory analysis tools to transparently diagnose failure modes like reward-hacking.
  arXiv  Detail & Related papers  (2026-07-15T11:11:17Z)
• Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models [63.96252564303143]
  Embodied-R1.5 is a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities.<n>We build a large-scale data system of over 15B tokens, and design a multi-task balanced RL recipe.<n>With only 8B parameters, Embodied-R1.5 SOTA on 16 out of 24 embodied VLM benchmarks, surpassing leading models like Gemini-Robotics-ER-1.5 and GPT-5.4.
  arXiv  Detail & Related papers  (2026-06-09T18:07:50Z)
• Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces [0.0]
  This paper introduces a zero-trust socio-technical orchestration framework that operationalizes a six-layer SSbD reference architecture.<n>We propose a shift from reactive automation to autonomous governance through "Professional Proxies"<n>This framework provides technology managers with a verifiable, evidence-based pathway toward resilient, net-zero Industry 5.0 ecosystems.
  arXiv  Detail & Related papers  (2026-06-08T09:02:02Z)
• Beyond Model Readiness: Institutional Readiness for AI Deployment in Public Systems [0.0]
  Many public-sector artificial intelligence systems fail not at the point of model development, but at the point of deployment.<n>Existing responsible AI and model evaluation frameworks are valuable, but they primarily assess models, datasets, and developer-side processes.<n>We introduce Institutional Alignment Readiness (IAR), a five-dimensional framework for assessing deployment readiness in public systems.
  arXiv  Detail & Related papers  (2026-05-17T00:11:09Z)
• Requirements Debt in AI-Enabled Perception Systems Development: An Industrial RE4AI Perspective [0.5729426778193398]
  This study conceptualises and empirically investigates how evolving functional and non-functional requirements create and propagate Requirements Debt (ReD)<n>We conducted 16 semi-structured interviews with experts from 13 international automotive companies and 3 European research institutes.<n>As one of the first empirical studies connecting technical debt theory with RE4AI, the work identifies key ReD mechanisms.
  arXiv  Detail & Related papers  (2026-04-30T13:11:35Z)
• TDD Governance for Multi-Agent Code Generation via Prompt Engineering [2.013170973610094]
  Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained circumstances.<n>We present an AI-native TDD framework that operationalizes classical TDD principles as structured prompt-level and workflow-level governance mechanisms.
  arXiv  Detail & Related papers  (2026-04-29T12:43:22Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• A Governance Model for IoT Data in Global Manufacturing [0.0]
  Industrial IoT platforms in global manufacturing environments generate continuous operational data across production assets, utilities, and connected products.<n>While data ingestion and storage capabilities have matured significantly, enterprises continue to face systemic challenges in governing IoT data at scale.<n>This paper presents a federated governance model that emphasizes contract-driven interoperability, policy-as-code enforcement, and asset-centric accountability.
  arXiv  Detail & Related papers  (2026-01-10T21:10:10Z)
• Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization [72.20212909644017]
  Deliberate Practice Policy Optimization (DPPO) is a metacognitive Metaloop'' training framework.<n>DPPO alternates between supervised fine-tuning (competence expansion) and reinforcement learning (skill refinement)<n> Empirically, training a vision-language embodied model with DPPO, referred to as Pelican-VL 1.0, yields a 20.3% performance improvement over the base model.<n>We are open-sourcing both the models and code, providing the first systematic framework that alleviates the data and resource bottleneck.
  arXiv  Detail & Related papers  (2025-11-20T17:58:04Z)
• Bridging the Mobile Trust Gap: A Zero Trust Framework for Consumer-Facing Applications [51.56484100374058]
  This paper proposes an extended Zero Trust model designed for mobile applications operating in untrusted, user-controlled environments.<n>Using a design science methodology, the study introduced a six-pillar framework that supports runtime enforcement of trust.<n>The proposed model offers a practical and standards-aligned approach to securing mobile applications beyond pre-deployment controls.
  arXiv  Detail & Related papers  (2025-08-20T18:42:36Z)
• The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data [0.0]
  The Uniform Appraisal dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to machine-readable formats.<n>This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems.<n>We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements.
  arXiv  Detail & Related papers  (2025-08-04T05:24:25Z)
• AI/ML Life Cycle Management for Interoperable AI Native RAN [50.61227317567369]
  Artificial intelligence (AI) and machine learning (ML) models are rapidly permeating the 5G Radio Access Network (RAN)<n>These developments lay the foundation for AI-native transceivers as a key enabler for 6G.
  arXiv  Detail & Related papers  (2025-07-24T16:04:59Z)
• FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance [6.494553545846438]
  We present the first AI-native framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents.<n>The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation.<n>We show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance.
  arXiv  Detail & Related papers  (2025-06-02T08:22:28Z)
• DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning [56.887047551101574]
  We present DS-Agent, a novel framework that harnesses large language models (LLMs) agent and case-based reasoning (CBR) In the development stage, DS-Agent follows the CBR framework to structure an automatic iteration pipeline, which can flexibly capitalize on the expert knowledge from Kaggle. In the deployment stage, DS-Agent implements a low-resource deployment stage with a simplified CBR paradigm, significantly reducing the demand on foundational capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-02-27T12:26:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.