Info‑Tech, Digitalization, and Intelligent Digital: Lessons from 300+ Companies to Avoid Pitfalls

1. Informationization

Informationization simply moves offline, manual processes into computer systems, aiming to "do less work, handle more affairs" without changing the underlying business logic. Typical examples include attendance punch‑in and accounting software that replace manual sign‑ins and ledger books. The key traits are:

Processes become online.

Systems are siloed and do not share data.

The goal is to save time.

Because systems operate independently (e.g., OA for approvals, ERP for production, finance software for bookkeeping), data cannot flow between them, forcing users to export and merge data manually—a common reason why many feel the systems are useless.

2. Digitalization

Digitalization builds on informationization by actually using the data that has been captured. The essential distinction is: informationization = having data; digitalization = using data.

Case study: a chain supermarket had separate POS sales data, inventory data, and membership data that never communicated. By building a simple aggregation platform that synchronized sales to inventory and linked membership to sales, the company achieved real‑time stock alerts and precise promotional targeting. Digitalization therefore enables data‑driven decision‑making instead of relying on intuition.

Judgement: a company is truly digital when it can discover problems and identify optimization directions through data analysis rather than experience alone.

3. Intelligent Digital (数智化)

Intelligent digital is not a mere upgrade of digitalization; it represents a fundamentally different approach where technology is deeply embedded in business to allow systems to make autonomous decisions and execute actions without human supervision.

Common misconception: purchasing a sales‑forecast tool does not make a company intelligent digital. The real core is an automatic decision loop.

Case study: for a renewable‑energy equipment‑maintenance project, sensors collected vibration, temperature, and voltage data. A model analyzed anomalies and predicted a possible failure within three days, automatically generating a work order and notifying the production department for schedule adjustment. The entire workflow—from data collection to decision and execution—occurred without human intervention.

Key characteristics of intelligent digital:

Systems can self‑decide and act.

Technology is tightly woven into business processes.

Business models are restructured around autonomous operations.

Judgement: a company has reached the intelligent‑digital stage when it possesses scenarios where the system makes decisions and performs tasks without being constantly monitored.

4. How to Implement at Different Stages

1. Informationization Stage

Select high‑frequency, repetitive processes that employees use daily. Choose systems that fit the business type:

Manufacturing: ERP for production and procurement.

Service‑oriented: OA for approval and collaboration.

Retail: POS for sales management.

Avoid buying large, feature‑rich systems that end up being used only for basic bookkeeping.

2. Digitalization Stage

Step 1 – Inventory core data: Identify which data (sales, inventory, customer) exist and where they are stored.

Step 2 – Build a simple aggregation platform: Connect the identified systems to create a unified view.

Tools such as FineDataLink can integrate many system types with low implementation cost and support downstream analysis and intelligent‑digital pilots.

3. Intelligent‑Digital Stage

Start with low‑cost, quick‑win pilots rather than large AI projects. Typical pilots include sales forecasting for retail and equipment‑failure prediction for manufacturing. Success hinges on data quality; many projects fail because data are inaccurate or incomplete. Therefore, standardize data collection and cleaning processes before launching pilots.

In summary, informationization, digitalization, and intelligent digital all aim to let technology help the business get things done. Companies that chase the "intelligent‑digital" label without solid processes and clean data often waste money and miss real benefits.