Focuses on AI-powered enterprise digitalization, sharing product and technology practices. Covers AI use cases, technical architecture, product design examples, and industry trends. Aimed at developers, product managers, and digital transformation professionals, providing practical solutions and insights. Uses technology to drive digitization and AI to enable business innovation.
The article analyzes why traditional hand‑over models fail as the number of AI agents grows, proposes a shared‑semantic and result‑driven management approach inspired by Salesforce and Palantir, and outlines a three‑layer organizational model with dual ownership to keep a digital workforce sustainable.
The article explains how to evolve a DDD‑based domain model into a cross‑system ontology that provides AI agents with unified facts, computable logic, and executable actions, using a six‑step process illustrated by a customer‑health‑score case and detailed governance practices.
After a decade of microservices, the article argues that AI agents expose semantic gaps across CRM, ERP, and other systems, and proposes an Ontology Service Layer that unifies objects, relationships, states, metrics, actions, policies, and evidence to enable controlled, auditable AI‑native execution.
The article outlines a deterministic, modular architecture for enterprise AI business‑analysis agents, detailing how to split reports into audited modules, assign clear tool contracts, perform rigorous attribution, generate evidence‑backed insights, and implement robust review, versioning, and evaluation practices.
The article analyzes why directly connecting large language models to a data warehouse often fails to deliver reliable analytics, outlines common failure modes, and presents a structured Skill‑based architecture—including intent routing, parameter compilation, metric‑first querying, review, and governance—to ensure trustworthy enterprise data queries.
The article analyzes how to construct enterprise AI management agents by distinguishing them from personal efficiency agents, defining data semantics and governance, designing two capability chains for query and analysis, and outlining a step‑by‑step implementation roadmap with security, evaluation, and ownership practices.
The article breaks down the 2026 mid‑year AI model landscape, compares tiered pricing across major providers, and offers concrete selection rules, caching tricks, tool‑chain setups, and habit‑based practices that together let teams minimize spend while maintaining high‑quality output.
By adapting the engineering practices of AI teams—goal definition, incremental execution, verification, and knowledge persistence—this article shows how anyone can transform ad‑hoc AI tool usage into a personal, loop‑engineered workflow, using Fable 5 principles, context engineering, and reusable SOPs for sustained collaboration.
The article applies Jevons Paradox to AI coding, arguing that as software becomes as cheap as water and electricity, previously unaffordable enterprises will become customers, unleashing a long‑tail of fragmented, on‑site software demand that rewards programmers with deep industry knowledge.
The article outlines how AI programming tools can shift coding from a scarce skill to a shared capability, detailing the creation of clear, maintainable engineering standards—Skills, hooks, templates, and checks—that transform individual productivity gains into coordinated team-wide processes.
