WITTI FIELD GUIDE · HONG KONG

AI implementation guide for Hong Kong SMEs

AI projects rarely succeed because the model is newest. They succeed when the workflow is right, data connects reliably, exceptions are handled and outcomes are measured against a baseline.

Start with step one ↓
01

Choose the workflow before the AI tool

A good starting point is repetitive, time-consuming work with accessible inputs and measurable outcomes. Map the process from start to finish, including manual entry, waiting, review and exceptions. This often reveals whether the answer is AI, conventional automation or process improvement first.

Examples: order entry, document verification, quality inspection, quotation follow-up and management reporting.
02

Prove value with real cases—not an endless demo

Use representative normal and exception cases, with success criteria agreed before work begins. Measure more than accuracy: handling time, human review and the impact of errors all matter. A prototype should produce a decision about whether production deployment is worthwhile.

Useful outputs: feasibility, known limits, expected return and a defined production scope.
03

Connect AI to systems, permissions and human checkpoints

Production requires APIs, ERP/WMS, document sources, access control, retention, retry logic and human escalation. High-risk or low-confidence results should not pass automatically; they should return to an assigned person for judgment.

An AI agent should complete what it safely can and return exceptions to people—not replace every person.
04

Record a baseline, then compare the same metric

Common metrics include handling time, error rate, missed cases, output, conversion, downtime and review hours. Include AI service, system maintenance and human-review costs before deciding whether to scale.

Measure one workflow, then expand into adjacent ones. This is usually safer than building a large platform at once.
05

Choose a partner that understands operations and integration

Ask potential partners how data flows, how existing systems connect, who owns exceptions, how security and privacy are handled and how production is monitored. The best solution may not use the most AI; it should reliably improve the business metric with the least unnecessary complexity.

WITTI approach: understand reality → prove quickly → integrate → improve continuously.

FAQ · HONG KONG AI

Questions before implementation

What should a Hong Kong SME prepare before an AI project?

Choose one clear workflow, collect representative real cases, list the systems and people involved, document exceptions and record a measurable baseline. You do not need to clean every company dataset first.

What is the difference between an AI consultant, software company and implementation partner?

A consultant usually supports strategy and selection. A software company builds to requirements. An implementation partner must also handle workflow design, data, integration, security, testing, training and post-launch improvement.

Is generative AI suitable for every workflow?

No. Deterministic rule-based work may suit conventional automation; visual recognition may need computer vision; generative AI is most useful when understanding unstructured documents or natural language.

Does a Hong Kong company always need a local model?

No. Choose public cloud, private cloud or local models based on data sensitivity, latency, cost, connectivity and control, with suitable permissions and human review.

Have a workflow to assess?

You do not need an AI solution first. Start by explaining how work happens today.

Talk to WITTI →