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AI Large-Model Wave and Transformation Guide
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AI Large-Model Wave and Transformation Guide

Focuses on the latest large-model trends, applications, technical architectures, and related information.

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Latest from AI Large-Model Wave and Transformation Guide

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AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 9, 2026 · Industry Insights

AI's Easy Wins Are Over: Why Enterprise Adoption Now Demands Software Infrastructure

The article argues AI's initial easy adoption in high-tolerance, online creative work is saturating, and the next phase requires deep integration with enterprise software infrastructure to handle cross-system SOPs, accuracy, and stability, giving established software companies an advantage over pure model providers.

AI AdoptionAI agentsFDE
0 likes · 10 min read
AI's Easy Wins Are Over: Why Enterprise Adoption Now Demands Software Infrastructure
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 8, 2026 · Backend Development

How Domain Ontologies Act as Semantic Adapters for Cloud ERP Integration

This article proposes using domain ontologies as semantic adapters in cloud ERP architectures to resolve multi-tenant data heterogeneity, semantic drift, and system mismatch through a three-layer mechanism covering concept mapping, granularity conversion, and rule formalization, with a sidecar deployment pattern and applicability guidelines.

OWLRDFcloud ERP
0 likes · 10 min read
How Domain Ontologies Act as Semantic Adapters for Cloud ERP Integration
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 8, 2026 · Artificial Intelligence

AI Agent Development: The Dual Challenge of Thinking Engineering & Distributed Systems

This article argues that AI agent development shifts from traditional coding to dual-system engineering: single agents require thinking logic design (prompt engineering, reasoning frameworks), while multi-agent systems demand distributed architecture skills (task graphs, state management, concurrency control), combining probabilistic reasoning with system reliability challenges.

AI agentsLLM agentsLangGraph
0 likes · 14 min read
AI Agent Development: The Dual Challenge of Thinking Engineering & Distributed Systems
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 7, 2026 · Artificial Intelligence

Why Enterprise AI Deployments Fail: Ontologies Are the Missing Semantic Layer

Enterprises mistakenly believe that combining LLMs with RAG over internal documents creates customized AI, but chaotic, unstandardized knowledge bases cause inaccurate answers; ontologies provide the necessary semantic layer to define terms, relationships, and validation rules, making AI reliable for business-critical tasks.

AI deploymentBusiness KnowledgeEnterprise AI
0 likes · 9 min read
Why Enterprise AI Deployments Fail: Ontologies Are the Missing Semantic Layer
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 7, 2026 · R&D Management

ERP Implementation from an Ontology Perspective: Hidden Pitfalls No One Warns You About

This article analyzes ERP implementation failures through an ontology engineering lens, revealing how conceptual misalignment, logical inconsistencies, and physical migration risks derail projects, and proposes four principles: ontology-first design, 20% customization limit, master data governance, and phased domain evolution.

ERPEnterprise ArchitectureImplementation
0 likes · 14 min read
ERP Implementation from an Ontology Perspective: Hidden Pitfalls No One Warns You About
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 7, 2026 · Industry Insights

Palantir's 8 Commercial Case Studies: Ontology Modeling Saves Millions Without Replacing Legacy Systems

This article analyzes eight Palantir Foundry deployments across aerospace, consumer goods, energy, and insurance, showing how ontology-based data integration enables real-time decision-making, cuts costs by millions, and accelerates digital transformation without replacing existing ERP or MES systems.

Case StudiesDecision IntelligenceDigital transformation
0 likes · 12 min read
Palantir's 8 Commercial Case Studies: Ontology Modeling Saves Millions Without Replacing Legacy Systems
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 7, 2026 · Artificial Intelligence

Palantir Ontology: The AI Decision OS Unifying Data, Logic, Actions & Security

This article dissects Palantir's Ontology, a semantic operational layer integrating data, logic, actions, and security, explaining its three core primitives (Objects, Links, Actions), layered architecture, differences from knowledge graphs, and demonstrating via Airbus Skywise case how LLMs use OSDK to query and execute actions safely.

AIPAirbus SkywiseData Decision Platform
0 likes · 13 min read
Palantir Ontology: The AI Decision OS Unifying Data, Logic, Actions & Security
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 5, 2026 · Industry Insights

Why 40 Smart Manufacturing Scenarios Fail in Practice: The Semantic Gap OntoL Solves

The article explains why most factories cannot implement the 40 official smart manufacturing scenarios, identifying semantic fragmentation across MES, ERP, PLC, and other systems as the core blocker, and shows how OntoL's ontology-based unified semantic layer enables incremental, pain-point-first deployment without ripping out existing IT.

ISA-95OntoLOver-Engineering
0 likes · 10 min read
Why 40 Smart Manufacturing Scenarios Fail in Practice: The Semantic Gap OntoL Solves
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 5, 2026 · Artificial Intelligence

The Accuracy Paradox: Why 99% Test Accuracy Fails in Production

This article argues that pursuing higher model accuracy on clean test sets undermines real-world AI deployment because semantic understanding requires tolerance for linguistic variation, ambiguity, and noise — not precision — and proposes evaluating semantic tolerance metrics like dialect robustness and intent diversity coverage instead of pure accuracy.

AI deploymentEvaluation Metricsintent recognition
0 likes · 12 min read
The Accuracy Paradox: Why 99% Test Accuracy Fails in Production
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 3, 2026 · Industry Insights

OntoL: Ontology Modeling & Semantic Layer for Enterprise Knowledge Governance

This article analyzes OntoL, an ontology modeling tool for enterprise knowledge governance, detailing its three-layer architecture, core capabilities like visual modeling and versioning, applicable scenarios, current technical limitations, and future roadmap including SHACL constraints and federated query.

Knowledge GovernanceKnowledge GraphOWL
0 likes · 9 min read
OntoL: Ontology Modeling & Semantic Layer for Enterprise Knowledge Governance