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AI Large-Model Wave and Transformation Guide

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Latest from AI Large-Model Wave and Transformation Guide

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AI Large-Model Wave and Transformation Guide

Oct 12, 2026 · Artificial Intelligence

Starting AI Ontology Implementation: A 5-Step Methodology for Scenario-Driven Success

This article presents a free, validated 5-step methodology for implementing AI ontology in enterprises, emphasizing scenario selection over technical starting points, illustrated with an HR case study where a manufacturing company achieved turnover prediction in three months.

AI ontologyEnterprise AIHR systems

0 likes · 10 min read

Starting AI Ontology Implementation: A 5-Step Methodology for Scenario-Driven Success

AI Large-Model Wave and Transformation Guide

Oct 10, 2026 · R&D Management

ERP Top-Level Design: Why Your Learning Path Fails & The 4-Stage Fix

This article explains why ERP professionals struggle with top-level design despite years of experience, identifying the wrong learning path as the root cause, and provides a four-stage learning roadmap covering business fundamentals, product mastery, architecture methodology, and real-case analysis, plus common pitfalls and a self-assessment test.

BPMNERPEnterprise Architecture

0 likes · 9 min read

ERP Top-Level Design: Why Your Learning Path Fails & The 4-Stage Fix

AI Large-Model Wave and Transformation Guide

Oct 10, 2026 · Artificial Intelligence

Personal FDE Works Locally — But Don't Deploy It to Enterprise Without These 4 Fixes

The article explains why personal Frontend Inference Environment (FDE) setups — local LLM workflows with Harness, Skills, and Agents — cannot be directly transferred to enterprise production, detailing four critical gaps: environment differences, data security risks, stability requirements, and operational responsibilities, and provides a consultant's framework for bridging the prototype-to-production divide.

AI agentsAI governanceFDE

0 likes · 9 min read

Personal FDE Works Locally — But Don't Deploy It to Enterprise Without These 4 Fixes

AI Large-Model Wave and Transformation Guide

Sep 11, 2026 · Industry Insights

Hangchi's Manufacturing Software Pain Points & OntoL Ontology Solutions

This article analyzes six core software implementation challenges at Hangchi, a heavy equipment manufacturer, including heterogeneous system data silos, BOM version chaos from frequent ECNs, WIP-cost accounting misalignment, master data governance issues, planning-execution disconnect, and broken traceability chains, and maps each to OntoL's ontology-based knowledge graph solutions.

BOMECNERP

0 likes · 11 min read

Hangchi's Manufacturing Software Pain Points & OntoL Ontology Solutions

AI Large-Model Wave and Transformation Guide

Sep 11, 2026 · Industry Insights

Why Material Issuance ≠ Cost Transfer: OntoL's Ontology Modeling Fixes Business-Finance Disconnect

OntoL uses dual ontology modeling — separating business material flow from financial cost recognition — to decouple warehouse issuance from profit-and-loss impact, enabling automatic WIP tracking, real-time discrepancy detection, and full audit traceability, cutting month-end closing from seven days to half a day in manufacturing case studies.

TBox/ABoxWIP cost accountingaudit traceability

0 likes · 13 min read

Why Material Issuance ≠ Cost Transfer: OntoL's Ontology Modeling Fixes Business-Finance Disconnect

AI Large-Model Wave and Transformation Guide

Sep 11, 2026 · Artificial Intelligence

Palantir's Ontology Decoded: Six Mechanism Layers for Trustworthy Enterprise AI

This article dissects Palantir's ontology into six mechanism layers—semantic transparency, constrained query, controlled action, organizational unification, scenario generalization, and governed evolution—showing how each solves a specific AI deployment failure mode, why alternatives fall short at scale, and when the investment pays off.

AI deploymentEnterprise AILLM limitations

0 likes · 54 min read

Palantir's Ontology Decoded: Six Mechanism Layers for Trustworthy Enterprise AI

AI Large-Model Wave and Transformation Guide

Sep 11, 2026 · Backend Development

Why 90% of ERP Implementations Fail: The Hidden Semantic Gap OntoL Ontology Fixes

This article explains why ERP systems with identical workflows produce vastly different outcomes, revealing that business semantics and rules—not processes—determine success, and demonstrates how OntoL's ontology modeling with TBox/ABox layers and inference engines standardizes constraints, versioning, and traceability across manufacturing scenarios.

BOMECNERP

0 likes · 25 min read

Why 90% of ERP Implementations Fail: The Hidden Semantic Gap OntoL Ontology Fixes

AI Large-Model Wave and Transformation Guide

Sep 10, 2026 · Artificial Intelligence

Enterprise Ontology: Beyond Semantics to Organizational Change for Trustworthy AI

The article argues that enterprise ontology for AI is not merely a technical semantic layer but a catalyst for organizational transformation, requiring continuous governance, cross-functional consensus, and structural changes to prevent silent semantic decay and build trustworthy AI agents.

AI governanceEnterprise OntologySemantic Decay

0 likes · 13 min read

Enterprise Ontology: Beyond Semantics to Organizational Change for Trustworthy AI

AI Large-Model Wave and Transformation Guide

Sep 9, 2026 · Artificial Intelligence

Why LLMs Fail at Critical Decisions: Ontologies Provide the Missing World Model

The author details how LLMs failed in underwater battlefield simulations despite trying prompts, agents, RAG, and knowledge graphs, and explains why ontologies — formal, reasoning-capable world models — are essential for trustworthy AI decisions, illustrating with a concrete case and a six-step ontology engineering process.

AI decision-makingLLM limitationsOntoL

0 likes · 12 min read

Why LLMs Fail at Critical Decisions: Ontologies Provide the Missing World Model

AI Large-Model Wave and Transformation Guide

Sep 9, 2026 · Industry Insights

Three-Tier Ontology Deployment Framework: Matching Solution Depth to Customer Maturity

This article presents a three-tier framework for deploying ontology products—Starter (single-scenario pilot), Growth (domain-level iteration), and Mature (enterprise semantic layer)—with criteria for tier selection, delivery methods, deliverables, team structures, commercial models, key metrics, risks, and upgrade paths, emphasizing effect-first validation and explicit gap disclosure.

Domain-Driven Designcustomer maturity modeldeployment framework

0 likes · 19 min read

Three-Tier Ontology Deployment Framework: Matching Solution Depth to Customer Maturity