From Informatization to Intelligent Digitalization: The Three Stages of Enterprise Transformation Explained

Many enterprises talk about "transformation," "digitalization," and "intelligent digitalization," but few can clearly distinguish their current stage. This article clarifies the three progressive stages: informatization, digitalization, and intelligent digitalization, emphasizing that each step involves not just technology upgrades but shifts in mindset, organizational structure, and management logic.

Summary Diagram

The article opens with a summary diagram illustrating the three stages.

Stage 1: Informatization — Moving Processes onto Systems

What Informatization Does

Informatization is the initial phase of putting systems in place. Its purpose: move paper-, people-, and experience-based processes into systems, using tools to run workflows.

Financial vouchers from handwritten ledgers into accounting software.

Leave approvals from paper forms to OA system workflows.

Sales orders and inventory records from Excel to ERP.

Attendance from punch cards to access control + HR system integration.

Core Keywords: "Record + Automation + Efficiency"

Systems are tools; the goal is to reduce manual labor, lower error rates, and improve execution efficiency. Examples: automated leave routing saves HR chasing; financial auto-reports cut reconciliation time; warehouse system auto-alerts on stock levels.

The target is faster, more accurate, more standardized execution , but data merely records processes and does not necessarily "tell you where the problem lies."

Typical Characteristics of Informatization

Limitations

Informatization solves process standardization and efficiency but cannot address "invisibility, inaccuracy, slow judgment" issues:

Cannot see which customers are most profitable.

Don't know which products are inventory "landmines."

Cannot distinguish slow turnover from poor forecasting.

Data exists, but insights and actions are absent.

Stage 2: Digitalization — Making Data Usable for Decision-Making

Informatization "puts processes on systems"; digitalization "turns data into assets." The core is not adding another system but connecting systems so data can "speak."

Key Changes in Digitalization

Break Data Silos : Sales data → planning → procurement → suppliers → inventory → finance, enabling end-to-end flow. Finance knows business data, business knows customer data, warehouse knows sales trends.

Build Data Assets : Data standards (single customer ID across systems), data models (product profit model, customer lifetime value model), master data management (standardize people, goods, places, organization, time, location).

Enable Analysis + Decision Support : BI dashboards for daily GMV, warehouse pressure; auto-alerts for inventory anomalies, profit drops, margin exceptions; assisted judgments like sales forecasting, performance analysis, procurement collaboration.

Core Keywords: "Integration + Analysis + Decision Support"

Digitalization's value is not collecting more data but enabling:

Clear panoramic view (operations landscape).

Identify problems (which cost rose? which SKU margin fell?).

Keep pace (real-time data, no month-end wait).

Judge quickly (data-backed, not gut feel).

Example: E-commerce Company After Digitalization

Sales sees daily channel GMV and conversion rates.

Procurement uses sell-through data for smart replenishment.

Warehouse refreshes "inventory health dashboard" daily to handle overstock early.

Finance reviews weekly "operating profit analysis" to spot problem SKUs instantly.

Boss opens BI dashboard: complaints, order fulfillment rate, inventory turnover at a glance.

Now "numbers" are not for bookkeeping but for "running the business."

Typical Characteristics of Digitalization

Stage 3: Intelligent Digitalization — Systems That Learn and Optimize

Many think intelligent digitalization is just digitalization plus AI. It's more fundamental: the core is that decision logic and business execution can auto-iterate.

Core Characteristics: "Self-Optimization + Dynamic Adjustment"

System "pushes" problems instead of waiting for queries : e.g., system alerts "SKU return rate spiked last 7 days, recommend pausing promotion."

System "predicts" trends instead of post-mortem : auto-generates next-month sales forecast, headcount plan, scheduling suggestions.

System "auto-experiments" to pick optimal paths : for same order, automatically selects best warehouse, lowest delivery cost, fastest route.

Core Keywords: "Intelligent Collaboration + Model-Driven + Business Closed Loop"

Not isolated modules; the system "learns," "adjusts," "proposes solutions" autonomously.

Pricing system auto-suggests prices based on velocity, competitor pricing, margin.

Smart scheduling generates production plans based on order urgency, equipment load, material availability.

Labor efficiency system dynamically identifies which roles yield highest marginal output per shift, assisting roster adjustments.

Three-Step Roadmap for Enterprises

Don't rush into AI, data platforms, or algorithm teams. Transformation is capability upgrade, not concept stacking or tech leapfrogging. Build invisible capabilities layer by layer.

Enterprises transform not by concepts but by step-by-step foundation-laying, gap-filling, and structuring, building "invisible capabilities" one layer at a time.

Step 1: Informatization — Streamline Processes, Land Data

Ignore AI and BI. Single goal: run core business, standardize processes, let data stay.

What to Do

Implement ERP to digitize order, shipping, finance flows.

Implement WMS to digitize inbound, outbound, inventory counts.

Implement HR system to unify attendance, payroll, performance.

Move offline approvals and Excel flows to OA.

Establish "data input standards": unified forms, fields, owners.

Problems Solved

Avoid chaos of "rely on memory, experience, group shouts."

Reduce duplicate entry, errors, lost orders, delays.

Make data "queryable and trustworthy."

Stage Milestones

Business data runs in systems.

Departments collaborate via system, not shouting.

Reports show "recorded values," not hearsay.

Tip: System newness doesn't matter; what matters is real usage, smooth operation, stability.

Step 2: Digitalization — Connect Data, Use Data

Systems exist, processes run. Next: connect "settled data" and start "viewing business with data." Keywords: connect, integrate, analyze, assist judgment.

What to Do

Define master data: unify customer ID, material codes, SKU names, org codes.

Define data calibers: how to calculate revenue, allocate cost, split profit.

Introduce BI tools (FineBI, PowerBI) for executive dashboards and departmental reports.

Build data mid-platform (even logical layer) to aggregate ERP, CRM, WMS, finance data.

Establish "visualization + multi-dimensional" business analysis system.

Problems Solved

Departments "speak different data languages."

Boss asks "Customer A net profit?" — need to pull three tables.

Business opaque, judgment by gut, problems discovered too late.

Stage Milestones

All business data visible in one view: customer performance, product margin, order fulfillment, inventory structure.

Finance and business share same "profit statement."

Reporting shifts from "post-fact output" to "real-time monitoring, auto-refresh."

Tip: Digitalization isn't pretty PPTs; it's unified calibers, understandability, interactivity — everyone speaks with the same data.

Step 3: Intelligent Digitalization — From Visualization to "Judgment and Optimization"

Many stall at BI: "We see, but still don't know how to change, adjust, predict." Intelligent digitalization solves this: turn data into "self-thinking assets" that drive business optimization.

What to Do

Build business metric models: e.g., Customer Lifetime Value (LTV), SKU contribution model, product structure optimization model.

Identify "prediction points" and "optimization points" in business processes: which need forecasting (hit product prediction, inventory turnover, return alerts)? Which actions can be model-driven (smart replenishment, pricing suggestions, scheduling optimization)?

Introduce AI models, algorithm engines, small recommender systems — start small, run one point.

Implement "smart rules + auto-suggestions": e.g., order delay auto-alert, margin anomaly auto-analysis.

Problems Solved

Shift from "know after it happens" to "predict before it happens."

Shift from "people hunt problems" to "system auto-surfaces problems."

Shift from "passive dashboard viewing" to "system suggests, tunes parameters, controls rhythm."

Stage Milestones

Models, predictions, optimization suggestions exist.

Boss can ask system: "Which product worth restocking?" "Which customer has most potential?"

Problems avoided proactively, not discovered in retrospectives.

Advice: Use "lighthouse project" mode to start intelligent digitalization.

Pick one business line (warehousing, returns, pricing).

Define measurable goal (reduce inventory occupancy, improve return prediction accuracy).

Build a small closed loop: data collection → analysis → suggestion → write back to system.

Visible results, calculable ROI, organizational visibility, willingness to follow.

Example: "AI-assisted pricing system" — uses historical sales + seasonality + margin + inventory status to give sales a price range suggestion. Or a "hit-product early warning system" that nightly outputs 10 SKUs with abnormal sales trends for merchandising review.

Run one thing end-to-end, shift one team's mindset, then replicate.

Final Summary

"Informatization," "Digitalization," "Intelligent Digitalization" are not slogans nor simple system swaps; they are three fundamentally different mindsets and management modes.

One-sentence summary:

Informatization makes enterprises "standardized," digitalization makes them "see clearly," intelligent digitalization makes them "run fast."

Don't fear being behind; fear not knowing the gap and the path forward. With clear goals and step-by-step upgrades, even starting from Excel can lead to intelligent management.