Expert perspectives on MCP-powered conversational BI, enterprise AI agents, and the future of data-driven decision making. 51 articles covering technology, strategy, security, and industry applications.
The Model Context Protocol is reshaping enterprise AI by creating a universal standard for connecting large language models to business data. Here's what every decision-maker needs to know.
A 2025 McKinsey survey found 73% of enterprises abandon AI initiatives before production. The problem is rarely the AI — it's the bridge between AI and business data.
Every analytics platform has a dirty secret: the SQL underneath is a mess. The semantic layer is the antidote that turns raw tables into business concepts.
製造業 generates more data per square metre than any industry. AI agents are finally turning that data into predictive maintenance, line optimisation, and supply chain resilience.
對話式 BI brings enterprise data into chat tools. For CISOs, this raises questions that traditional BI security frameworks were never designed to answer.
AI-powered retail analytics unifies POS, inventory, and supply-chain data so retailers can reduce stockouts, improve turnover, and answer business questions in seconds.
IM-native conversational BI delivers data answers inside the chat tools employees already use. Here's a practical playbook for deploying it across WeChat Work, DingTalk, and Feishu.
Bad data leads to bad decisions. Here's how to catch quality issues before anyone notices — automatically.
AI is only as good as the data behind it. Build the foundation first — or watch your AI investment fail.
Tools don't create a data-driven culture. Behaviours do. Here's how to shift from gut-feel to data-driven decisions.
Your semantic layer is the single source of truth for every metric. Treat it like a product — not a side project.
You have the tools, the data, and the team. So why doesn't anyone trust the numbers? Here's the anatomy of analytics failure.
Data mesh is trendy. Data warehouse is proven. Which is right for you? It depends on your organisation, not your technology.
Data pipelines deliver data. Data products deliver outcomes. Here's the operating model that makes the difference.
Good governance is invisible. It runs in the background, checks are automated, and engineers never wait for a committee meeting.
LLMs can leak training data, be manipulated through prompts, and generate outputs that violate governance policies. Here's how to govern them.
AI agents with data access are a governance challenge. Here's how to control what they can see and prove what they did.
PIPL isn't just about data storage. It governs how AI processes personal information. Here's what enterprises need to do.
Traditional security trusts what's inside the network. Zero trust trusts nothing — especially not AI systems with access to your most sensitive data.
Static pricing leaves money on the table. Dynamic pricing captures it — if you do it right.
Overstocking ties up capital. Understocking loses sales. ML forecasting finds the sweet spot.
Customers don't think in channels. Your analytics shouldn't either. Here's how to unify online and offline retail data.
The customer journey isn't a straight line. It's a web. AI helps you see the patterns that drive conversions.
Human inspectors catch 80% of defects. Computer vision catches 99%. Here's how to deploy it on your production line.
Energy is 10-20% of manufacturing operating costs. AI can cut that by 15%. Here's how.
A smart factory generates terabytes of sensor data daily. Here's how to architect the pipeline that turns it into actionable intelligence.
Supply chains generate more data than any other business function. AI turns that data into predictions that save millions.
Accuracy is a technical metric. Business value is what matters. Here's how to evaluate AI models the way executives do.
Responsible AI principles are easy to write. Operationalising them in production is the hard part. Here's a practical roadmap.
Compliance is the floor, not the ceiling. Enterprises that treat AI ethics as a checkbox will lose trust. Here's how to go beyond compliance.
Every enterprise faces the build-vs-buy question for AI. The wrong choice costs millions. Here's how to decide.
An AI Center of Excellence can be your transformation engine — or an expensive bottleneck. The difference is in the design.
Deploying one model is easy. Deploying fifty models, keeping them healthy, and updating them without downtime — that's MLOps.
AI governance doesn't have to mean bureaucracy. Here's how to build governance that protects without slowing you down.
Most enterprises are stuck at Stage 2 of AI maturity. Here's how to break through to Stage 3 and beyond.
Deploying conversational BI is the easy part. Getting people to use it is the hard part. Here's a change management playbook.
Your AI pilot worked beautifully. Then you tried to deploy it. Here's what went wrong — and how to fix it.
Bar or line? Pie or treemap? The AI's chart choice shapes how users understand their data. Here's how it decides.
From punch cards to SQL to drag-and-drop to natural language — each generation of data query tools has expanded who can access data. Here's what we learned.
Traditional BI looks cheaper on paper. But when you count implementation time, training costs, and unused licenses, the picture changes.
When a user asks 'Now break that down by region', the AI needs to know what 'that' refers to. Context management is the unsung hero of conversational BI.
Dashboards were revolutionary in 2010. In 2026, they're a bottleneck. Here's why conversational analytics is the next paradigm.
A good natural language interface feels like talking to a knowledgeable analyst. A bad one feels like talking to a search engine. Here's the difference.
Pre-built AI agents are to enterprise AI what app stores were to mobile. Here's why marketplaces matter and how to evaluate them.
Cloud AI is too slow for factory-floor decisions. Edge computing brings AI to where the data is generated — in milliseconds, not seconds.
對話式 BI is only as fresh as the data behind it. Here's how to architect real-time strea分鐘g pipelines that keep answers current.
Should you build on GPT or Llama? The answer depends on five factors that most enterprises get wrong.
Text-to-SQL sounds simple until you try it in production. Here's how modern AI agents translate business questions into accurate database queries.
Vector databases are the backbone of modern AI search. Here's how to use them for enterprise knowledge retrieval — without the hype.
Traditional APIs require custom integration for every data source. MCP creates a universal protocol that lets AI models connect to any data system without bespoke pipelines.
What happens between a user asking a question in WeChat Work and getting a chart back? Here's the full architecture of an enterprise AI agent.
Book a free demo and see how MCP-powered conversational BI can deliver insights in 2 weeks.