Insights
Insights and practical guidance
Every article carries key takeaways and a source field, so it can be circulated and checked internally.
Hong Kong's First Five-Year Plan: 7 AI Business Opportunities Most Companies May Be Missing
: The biggest opportunity may not be another AI tool. Hong Kong is building a new industrial chain that connects computing power, data, talent, governance and enterprise adoption.
Learn moreCan AI Really Change a Life? One Student's Reaction Made Me Want to Explain This Clearly
A person can move from dismissing an idea with “I don't know how” to asking, “How much could I make possible?”
Learn moreThe Core of AI Governance Is Managing Judgement, Not Tools
Once AI enters a company, what changes is not only the speed of work but how decision rights are allocated.
Learn moreA Company’s Costliest Gap Is Not Missing Data but Organisational Amnesia
Management may remember what the company did, yet forget why it did it.
Learn moreAI in Sales Should Not Start with Writing Scripts
The real gap in sales performance is often not how well something is said, but whether you correctly understand what is happening with the customer.
Learn moreCompany Culture Is Not a Slogan on the Wall but a Reward Algorithm Running Every Day
What staff really learn is what the company actually rewards, what it tolerates, and who pays the price.
Learn moreWhat AI Really Gives Individuals Is Not More Busyness but Greater Income and Choice
Efficiency only means finishing existing work faster; the market pays more when you solve more important problems.
Learn moreWhy AI pilots stall in Hong Kong enterprises
The blocker is rarely model capability. It is process ownership, data readiness and integration.
Learn moreAn AI governance checklist for Hong Kong companies
Ten questions that should have answers before the first use case goes live.
Learn moreConnecting AI to CRM and ERP without replacing them
Starting with an integration layer and master-data discipline delivers faster than a full replacement.
Learn moreHong Kong Enterprise AI Transformation Consulting Guide
A complete framework covering readiness assessment, use-case prioritisation, governance and rollout pacing, written for leaders who answer to a board.
Learn moreCorporate AI Training in Hong Kong: Programme Design Checklist
One training deck cannot serve leadership, operational staff and frontline teams equally well. This guide breaks down what each layer should learn, and how.
Learn moreAI Agent Workflow Automation: From Pilot to Governance
An AI agent can execute multi-step work automatically, which also means it can execute multi-step mistakes automatically. This guide covers the safe path from pilot to governed scale.
Learn moreIntegrating AI with CRM and ERP Without Replacing Core Systems
Most enterprises do not need to rip out their CRM or ERP to adopt AI. What they need is a clear integration path, a data cleanup plan and an interface strategy.
Learn moreAI strategy & operating systems
From context and data to a reusable system
Written by Harry Lui, extending ideas from his book into everyday Hong Kong operating situations.
Stop Asking AI for Answers: Start with Business Context
Most companies do not lack AI tools. They ask the tool for an answer before they have made the situation clear.
Learn morePrompts Are Not the Core: Turn Enterprise Data into AI Fuel
The same AI tool may produce generic copy for one company and reveal lost orders, rework and product opportunities for another. The difference is usually data, not prompts.
Learn moreWhy Customers Do Not Buy: Use AI to Connect CRM, Enquiries and Sales
A company’s most valuable customer evidence may not be its completed orders, but the conversations that nearly became business and then stopped.
Learn moreThe One-Person Company’s First AI Team: Three Practical Workflows
The greatest constraint in a one-person company is not headcount. It is having to remember, decide and restart every kind of work with the same brain.
Learn moreFunding guides
Hong Kong funding reference for AI and training
Publicly available official material on NITTP, PTSGLS and the BUD Fund. Eligibility and approval rest with the scheme administrator.
NITTP technology training funding: a starting guide for Hong Kong companies
How the New Industrialisation and Technology Training Programme supports advanced technology training for company staff, and what to confirm first.
Learn morePTSGLS smart and green logistics training funding: AI and automation courses
How logistics practitioners claim a course-fee refund for pre-approved smart logistics, AI and automation courses, including the four-month deadline.
Learn moreThe BUD Fund and AI upgrading: scope, ceilings and common misreadings
How the BUD Fund treats upgrading and market development projects with AI elements, and why buying AI software does not automatically qualify.
Learn moreAI Transformation Glossary
The same term often means different things across departments. These are the definitions we work with.
- AI transformation
- The process of embedding AI into everyday processes, systems and governance — measured by outcomes, not tool purchases.
- Readiness assessment
- A structured review of data, process, systems and risk to determine which AI use cases are feasible now.
- AI agent
- A software role that completes multi-step tasks using defined rules and tools, still bounded by human review and permissions.
- Human-in-the-loop
- A design that retains human review or approval at higher-risk or customer-facing steps.
- Systems integration
- The engineering work that lets CRM, ERP, commerce platforms and workflows exchange consistent data.
- Master data management
- Establishing a single trusted version and maintenance rules for core records such as customers, products and suppliers.
- AI governance
- The rules and review cadence covering who may use which data, how output is checked, and how issues are logged and escalated.
- Adoption rate
- The share of staff actually using the new process or system — a key indicator of whether a project worked.
- Retrieval-augmented generation (RAG)
- Retrieving relevant content from your own documents before generating an answer, reducing unsupported responses.
- Data residency
- Where data is physically stored and processed, often constrained by contract or regulation.
Start with an AI readiness session
Sixty minutes to review your systems, your most time-consuming processes and your goals — followed by initial observations and a suggested scope.