Diagnosing Information, Digital, Data‑Intelligent, Intelligent and Smart Maturity in Enterprises

Informationization

Informationization replaces manual, paper‑based work with computerised record‑keeping; the core goal is to ensure that every transaction is electronically captured. It is usually implemented as isolated, department‑level tools that do not share data.

Typical tools: accounting software (e.g., Kingdee), Excel payroll sheets, handwritten sales logs, punch‑card attendance.

Quick assessment (2 days):

List all systems/tools used by each department.

Identify processes that remain completely manual.

Check master data consistency (e.g., product codes).

Trace a recent order through the entire workflow to spot manual hand‑offs.

Common problems: data silos, inconsistent master data, duplicated manual‑electronic work, and data that is recorded but never analysed.

Improvement path:

Week‑long “quick win”: move pure‑paper steps to free spreadsheet tools.

One‑month “foundation”: unify core master data (product, customer, supplier codes).

2‑3 months “partial integration”: connect the two most tightly coupled systems (e.g., sales order → warehouse).

Case: a community supermarket introduced a cash‑register system, a simple inventory module and unified product codes, achieving the basic informationization layer.

Digitization

Digitization extends beyond isolated data capture to run entire business processes online, enabling automatic data flow and eliminating manual data transfer.

Key indicator: end‑to‑end automation rate = automated nodes ÷ total nodes.

Assessment (3 days):

Measure automation of two core chains – order‑to‑cash and purchase‑to‑pay.

Check whether core systems (ERP, CRM, WMS, OA, finance) have interfaces or rely on manual Excel imports.

Validate data consistency by asking three departments for the same metric (e.g., monthly sales).

Verify real‑world flow: interview frontline staff to confirm that no parallel offline steps exist.

Typical problems: system silos, separate business‑finance data sets, digital copies of inefficient offline processes, and poor data quality.

Improvement path:

Two‑week “quick win”: integrate the two most frequently used systems (e.g., sales order → ERP).

3‑6 months “core chain integration”: achieve business‑finance integration so that sales orders automatically generate financial vouchers.

Process optimisation before go‑live: eliminate redundant approvals and steps that were merely replicated online.

Case: a trading company linked its sales, inventory and finance systems, reducing month‑end reconciliation from three days to half a day.

Data‑Intelligent (数智化)

Data‑Intelligent combines digitization with AI/analytics to let data drive decisions rather than merely supporting processes.

Assessment:

Check for a unified data lake or middle‑platform that aggregates all source systems.

Interview decision‑makers to see whether choices are based on data or gut feeling.

Evaluate depth of data use: static reports vs. real‑time monitoring and predictive alerts.

Verify a single definition for core metrics (sales, profit, inventory turnover).

Typical problems: data scattered across systems, lagging “after‑the‑fact” reports, low adoption of analytics, and inconsistent metric definitions.

Improvement path:

One‑month “quick win”: build a real‑time dashboard with 5‑10 key KPIs for the leadership team.

Two‑month “metric standardisation”: publish a company‑wide metric dictionary.

3‑6 months “data foundation”: consolidate source data into a unified platform and enforce data governance.

Enable business units with self‑service analytics tools for data‑driven optimisation.

Case: a food manufacturer created a unified data middle‑platform, a real‑time “cockpit” for revenue, inventory and profit, and eliminated metric disputes in meetings.

Intelligent Automation (智能化)

Intelligent automation applies AI, algorithms and rule‑based engines so that systems make decisions and act without human intervention.

Assessment steps:

Catalogue all AI/automation scenarios across departments (e.g., chat‑bots, invoice OCR, auto‑replenishment, AI visual inspection).

Quantify human‑resource replacement and accuracy for each scenario.

Determine whether automation tools are isolated or part of a unified AI platform.

Identify high‑pain, high‑volume manual tasks as priority targets.

Typical problems: fragmented pilots, wrong use‑case selection, low accuracy that adds workload, and AI projects pursued for hype rather than value.

Improvement path:

Select “low‑hanging fruit” – rule‑clear, high‑volume, error‑prone tasks (e.g., invoice OCR, FAQ chat‑bot, data entry).

Run small pilots, validate ROI and accuracy before scaling.

Consolidate reusable AI capabilities (OCR, NLP) into a company‑wide platform.

Adopt a human‑in‑the‑loop model: aim for ~80 % automation with 20 % manual fallback.

Case: a finance team reduced invoice processing from three people for three days to one person for one day, achieving 90 % automatic posting with an AI‑driven invoice audit system.

Smart (智慧化)

Smart maturity means the entire business ecosystem can perceive, decide and act autonomously, forming a global “brain” that self‑optimises.

Assessment checklist:

Does a closed‑loop of perception‑decision‑execution exist for a core chain (e.g., demand sensing → forecast → procurement → supplier order)?

Can the system adapt rules automatically to external changes (holidays, price spikes)?

Is cross‑departmental optimisation in place, or are only isolated smart modules deployed?

Does the underlying algorithm continuously learn from new data?

Typical problems: buzzword‑driven claims without a real brain, weak data/algorithm foundation, siloed smart tools, and massive investment with unclear ROI.

Improvement path:

Do not skip levels – solidify informationization → digitization → data‑intelligent → intelligent before attempting smart.

Start with a single end‑to‑end smart loop (e.g., smart supply chain) and expand gradually.

Quantify cost‑savings, efficiency gains and revenue impact for every smart initiative.

Case: a leading e‑commerce platform built a smart supply‑chain that automatically forecasts demand, adjusts inventory and logistics, and issues real‑time alerts, requiring minimal human intervention.

Key Takeaways for Efficient Maturity Assessment

Assess from the bottom up: verify informationization before moving upward.

Focus on core “order‑to‑cash” and “purchase‑to‑pay” chains; peripheral processes are secondary.

Validate findings with three perspectives – senior management, middle management and frontline staff – and trace 1‑2 real orders end‑to‑end.

Measure real business value: revenue increase, cost reduction, error reduction and labor savings.