# Digital Maturity Assessment Report

> **Assessment Subject:** [Organization / Enterprise Name]
> **Assessment Scope:** [Business Domain / Organizational Boundary / Geographic Scope]
> **Assessment Period:** [YYYY-MM-DD — YYYY-MM-DD]
> **Version:** V[X.X]

---

## Table of Contents

1. [Executive Summary](#1-executive-summary)
2. [Assessment Methodology](#2-assessment-methodology)
3. [Current-State Analysis](#3-current-state-analysis)
4. [Maturity Level Determination](#4-maturity-level-determination)
5. [Gap Analysis](#5-gap-analysis)
6. [Benchmarking](#6-benchmarking)
7. [Key Findings](#7-key-findings)
8. [Improvement Recommendations](#8-improvement-recommendations)
9. [Implementation Roadmap](#9-implementation-roadmap)
10. [Appendices](#10-appendices)

---

## 1. Executive Summary

### 1.1 Assessment Overview

[Briefly describe the background, purpose, and overall conclusion of this assessment. Keep within one page.]

> **Example:** "This assessment evaluated the digital maturity of [XX Transport Group] across [3 subsidiaries and 12 business systems] over a [4-week] period. The results indicate that the group's overall digital maturity is **L2 (Systematized)**, a gap of two levels from the industry-leading benchmark (L4, Data-Driven). The core shortfalls are concentrated in the Data Capability and AI & Intelligence dimensions. This report proposes a three-phase improvement roadmap; reaching L3 is expected within [24 months]."

### 1.2 Assessment Results at a Glance

| Dimension | Score (out of 5.0) | Maturity Level | Industry Average | Gap |
|-----------|--------------------|----------------|------------------|-----|
| Strategy & Organization | [X.X] | [L1–L5] | [X.X] | [±X.X] |
| Business Digitalization | [X.X] | [L1–L5] | [X.X] | [±X.X] |
| Data Capability | [X.X] | [L1–L5] | [X.X] | [±X.X] |
| Technology Foundation | [X.X] | [L1–L5] | [X.X] | [±X.X] |
| AI & Intelligence | [X.X] | [L1–L5] | [X.X] | [±X.X] |
| **Composite Score** | **[X.X]** | **[L X]** | **[X.X]** | **[±X.X]** |

### 1.3 Top 5 Recommendations

1. [Recommendation 1: the most important and most urgent improvement direction]
2. [Recommendation 2]
3. [Recommendation 3]
4. [Recommendation 4]
5. [Recommendation 5]

---

## 2. Assessment Methodology

### 2.1 Assessment Framework

This assessment adopts the **Transportation Digital Maturity Model (T-DMM)**, which references the following international standards and industry frameworks:

- **International standards:** ISO/IEC 33000 series (process assessment), COBIT 2019, ISO/IEC 20000 (IT service management), ISO 9001 (quality management)
- **Industry references:** OECD Digital Government Framework; EU Intelligent Transport Systems (ITS) Action Plan; USDOT ITS Strategic Plan
- **Management references:** CMMI 2.0, COBIT 2019, ITIL 4, DAMA-DMBOK 2.0
- **AI-specific:** Gartner AI Maturity Model; OECD AI Principles; NIST AI Risk Management Framework

### 2.2 Assessment Dimensions and Weights

| Tier-1 Dimension | Weight | Tier-2 Dimensions (Sub-capability Areas) |
|------------------|--------|------------------------------------------|
| **Strategy & Organization** | 20% | Digital vision & strategy (5%) · Organization & talent (5%) · Culture & change management (5%) · Investment & budgeting (5%) |
| **Business Digitalization** | 25% | Core process digitalization (8%) · Customer/public service digitalization (7%) · Operations management digitalization (5%) · Ecosystem collaboration digitalization (5%) |
| **Data Capability** | 20% | Data governance (5%) · Data collection & integration (5%) · Data analytics & application (5%) · Data security & compliance (5%) |
| **Technology Foundation** | 20% | IT infrastructure (5%) · Application architecture (5%) · Technology architecture (5%) · Cybersecurity (5%) |
| **AI & Intelligence** | 15% | AI strategy & governance (3%) · AI technical capability (4%) · AI application scenarios (5%) · AI ethics & compliance (3%) |

### 2.3 Maturity Level Definitions

| Level | Name | Core Characteristics | Score Band |
|-------|------|----------------------|------------|
| **L1** | Initial | Reactive, dependent on individual capability, no systematic initiatives | 1.0 — 1.9 |
| **L2** | Systematized | Single-system / single-department build-out, initial informatization, data silos | 2.0 — 2.9 |
| **L3** | Integrated | Cross-system integration, cross-department collaboration, data convergence, partial intelligence | 3.0 — 3.9 |
| **L4** | Data-Driven | Data-driven business decisions, AI embedded in core operations, platform-based | 4.0 — 4.4 |
| **L5** | Ecosystem-Leading | Industry benchmark, capability export, AI-native, continuous innovation | 4.5 — 5.0 |

### 2.4 Data Collection Methods

This assessment combines the following five data collection methods:

| Method | Quantity / Scale | Description |
|--------|------------------|-------------|
| Executive interviews | [X] sessions | Group / corporate senior decision-makers, approx. [60–90] min each |
| Department / business interviews | [X] sessions | Covering [XX] departments, [45–60] min each |
| Questionnaire survey | [XX] responses | Targeting [management / backbone / frontline] staff; [X] returned (response rate XX%) |
| System / document review | [XX] items / sets | Design docs, O&M records, management policies, data dictionaries, etc. |
| On-site observation / system demos | [X] sessions | Field visits to [XX] sites; observation of [XX] systems in live operation |

### 2.5 Scoring Rules

- Each Tier-2 dimension is scored on a **0–5 scale** (to one decimal place)
- Tier-1 dimension score = weighted average of Tier-2 scores
- Composite score = weighted average of Tier-1 scores
- Evidence hierarchy: interview records > system / document evidence > survey statistics > industry benchmarking

### 2.6 Assessment Team

| Role | Name | Organization / Title | Responsibility |
|------|------|----------------------|----------------|
| Assessment Lead | [Name] | [Organization / Title] | Overall responsibility for methodology, quality control, report sign-off |
| Expert — Strategy | [Name] | [Organization / Title] | Strategy & Organization dimension |
| Expert — Business | [Name] | [Organization / Title] | Business Digitalization dimension |
| Expert — Data | [Name] | [Organization / Title] | Data Capability dimension |
| Expert — Technology | [Name] | [Organization / Title] | Technology Foundation dimension |
| Expert — AI | [Name] | [Organization / Title] | AI & Intelligence dimension |
| Assessment Assistant | [Name] | [Organization / Title] | Data collection, interview notes, analytics support |

### 2.7 List of Interviewees (Assessed Party)

| No. | Name | Department / Title | Interview Date | Duration | Topic |
|-----|------|--------------------|----------------|----------|-------|
| 1 | [Name] | [Group decision-maker / GM] | [YYYY-MM-DD] | [XX] min | Strategy, vision & objectives |
| 2 | [Name] | [CIO / CDO / Head of IT] | [YYYY-MM-DD] | [XX] min | Overall digital status |
| 3 | [Name] | [Operations Management] | [YYYY-MM-DD] | [XX] min | Operations digitalization |
| ... | ... | ... | ... | ... | ... |
| N | [Name] | [Frontline Operator] | [YYYY-MM-DD] | [XX] min | Actual system usage experience |

---

## 3. Current-State Analysis

### 3.1 Dimension 1: Strategy & Organization (Weight 20%)

#### 3.1.1 Digital Vision & Strategy (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Describe the assessed party's current state of digital vision and strategic planning. Is there a formal digital strategy document? Is it aligned with the business strategy? Is the strategy well communicated?]

> **Evidence example:**
> - A digital development plan (the "1-3-5-N" framework) was published in [2023] as part of the group's medium-term plan.
> - However, the plan has not been decomposed into annual execution plans; implementation varies by department.
> - In interviews, [XX%] of middle managers could not accurately restate the core digital objectives.

**Strengths:**

- [Strength 1]
- [Strength 2]

**Weaknesses:**

- [Weakness 1]
- [Weakness 2]

#### 3.1.2 Organization & Talent (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Describe the digital organization structure (CIO/CDO role, digital decision committee, dedicated digital unit), talent profile (IT staff ratio, digital skill level), and the talent recruitment & development operating model.]

> **Evidence example:**
> - A "Digital Transformation Steering Committee" has been established, chaired by the CEO (meets quarterly).
> - An IT center (12 staff) handles IT O&M, but there are no dedicated digital planning / data / AI roles.
> - IT staff represent [X%] of total headcount, below the industry average of [Y%].
> - Attrition over the last two years was [X%]; only [X] staff remain for core system O&M.

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.1.3 Culture & Change Management (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Describe the digital culture (data awareness, innovation climate, collaboration) and change management (is there a change-management process? track record of major change successes / failures).]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.1.4 Investment & Budgeting (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Describe the last 3 years of IT / digital spend, share of revenue / total expenditure, budget preparation & execution process, and the investment-benefit evaluation operating model.]

> **Evidence example:**
> - In 2023, IT spend was [$XX M], representing [X%] of operating revenue (industry average 1.5%–2.5%).
> - Of this, [X%] went to O&M ("keeping the lights on"), only [X%] to new build / innovation.
> - No post-implementation benefit review process; no ROI analysis has been done on live systems.

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

---

### 3.2 Dimension 2: Business Digitalization (Weight 25%)

#### 3.2.1 Core Process Digitalization (8%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Assess the digitalization level of the following core business processes one by one. Typical transport-sector processes include but are not limited to:]
- [Road maintenance management (inspection, dispatch, acceptance)]
- [Toll / congestion charging operations (ETC / mobile payment / MTC)]
- [Dispatch & command (crew scheduling, real-time dispatch, emergency response)]
- [Asset management (equipment ledger, maintenance, decommissioning)]
- [Engineering management (design, construction, supervision, acceptance)]

| Core Process | Digitalization Level | System Name | Coverage | Key Shortfall |
|--------------|----------------------|-------------|----------|---------------|
| [Maintenance mgmt] | [High/Med/Low] | [XX system] | [XX corridor / XX%] | [e.g., no mobile app] |
| [Toll operations] | [High/Med/Low] | [XX system] | [XX toll plaza] | [e.g., data not integrated] |
| [Dispatch & command] | [High/Med/Low] | [XX system] | [XX line] | [e.g., manual dependency] |
| ... | ... | ... | ... | ... |

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.2.2 Customer / Public Service Digitalization (7%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Describe public / customer-facing service channels (app / mini-program / web portal / hotline), service content (queries / ticketing / complaints / transactions), UX level, and service data accumulation.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.2.3 Operations Management Digitalization (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Describe the build-out and usage of OA / ERP / finance / HR management systems, and the management cockpit / BI dashboard capability.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.2.4 Ecosystem Collaboration Digitalization (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Describe data sharing and business collaboration with upstream/downstream enterprises, regulators, and industry associations.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

---

### 3.3 Dimension 3: Data Capability (Weight 20%)

#### 3.3.1 Data Governance (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate the data governance system: Is there a data governance organization (data committee / data owner / data steward)? A data standards system? A data quality management system? What is the state of master data management?]

> **Evidence example:**
> - No formal data governance organization exists; data standards are missing — "every system has its own coding."
> - Data quality issues are significant: [XX%] of asset ledger records lack location data; [XX%] have duplicate asset IDs.

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.3.2 Data Collection & Integration (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate data collection coverage (IoT / sensors, business systems, external sources), data integration (data warehouse / data lake), data timeliness, and data dictionary completeness.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.3.3 Data Analytics & Application (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate analytics capability: reporting, self-service BI, advanced analytics (prediction / optimization / simulation), and the maturity of data products.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.3.4 Data Security & Compliance (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate data security management: data classification & grading, access control, data masking, privacy protection (GDPR / NIS2 compliance), backup & disaster recovery.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

---

### 3.4 Dimension 4: Technology Foundation (Weight 20%)

#### 3.4.1 IT Infrastructure (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate the network (wired / wireless / 5G / private network), compute (servers / cloud / edge), storage, data center / server room, and cloud adoption rate.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.4.2 Application Architecture (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate the application inventory, integration approach (interfaces / ESB / API gateway), architecture soundness, technical debt, and legacy systems.]

> **Evidence example:**
> - [35] active applications in total; [12] have been in use for over 8 years; [5] are in an "unmaintained" state.
> - Integration is predominantly point-to-point ([60+] interfaces), with no unified integration platform.
> - Technical debt: core systems scored [Grade D] on code / architecture health.

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.4.3 Technology Architecture (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate architecture modernity: microservices / containerization, DevOps maturity, technology stack consistency, open-source vs. proprietary mix, and new-technology exploration (digital twin, blockchain, etc.).]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.4.4 Cybersecurity (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate the cybersecurity posture: regulatory compliance (e.g., NIS2 / ISO 27001), security management system, security operations (SOC / threat detection), historical incidents, and security investment.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

---

### 3.5 Dimension 5: AI & Intelligence (Weight 15%)

#### 3.5.1 AI Strategy & Governance (3%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate AI strategy: Is there an AI development plan? An AI ethics charter? A sound AI project decision framework? An AI talent pipeline?]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.5.2 AI Technical Capability (4%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate the AI technical base: MLOps platform / AI middle-platform, model development & deployment, compute resources (GPU / NPU), data labeling, and algorithm team capability.]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.5.3 AI Application Scenarios (5%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Inventory the deployed AI scenarios and their effectiveness. Typical transport-sector AI scenarios include but are not limited to:]

| AI Scenario | Deployment Status | Technical Approach | Effect / ROI | Maturity |
|-------------|-------------------|--------------------|--------------|----------|
| [License-plate / vehicle recognition] | [Deployed / Pilot / Planned] | [Deep-learning CV] | [Accuracy XX%] | [High/Med/Low] |
| [Traffic-flow forecasting] | [Deployed / Pilot / Planned] | [Time-series / GNN] | [MAPE XX%] | [High/Med/Low] |
| [Incident detection] | [Deployed / Pilot / Planned] | [CV + NLP] | [Recall XX%] | [High/Med/Low] |
| [Intelligent assistant] | [Deployed / Pilot / Planned] | [LLM / FAQ] | [Resolution rate XX%] | [High/Med/Low] |
| ... | ... | ... | ... | ... |

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

#### 3.5.4 AI Ethics & Compliance (3%)

**Score: [X.X] / 5.0**

**Evidence Description:**

[Evaluate AI ethics governance: algorithmic fairness review, explainability, privacy protection, and regulatory compliance (e.g., EU AI Act / OECD AI Principles).]

**Strengths:**

- [Strength 1]

**Weaknesses:**

- [Weakness 1]

---

## 4. Maturity Level Determination

### 4.1 Composite Scoring

| Tier-1 Dimension | Weight | Score | Weighted Score | Corresponding Level |
|------------------|--------|-------|----------------|---------------------|
| Strategy & Organization | 20% | [X.X] | [X.X] | [L X] |
| Business Digitalization | 25% | [X.X] | [X.X] | [L X] |
| Data Capability | 20% | [X.X] | [X.X] | [L X] |
| Technology Foundation | 20% | [X.X] | [X.X] | [L X] |
| AI & Intelligence | 15% | [X.X] | [X.X] | [L X] |
| **Composite Score** | **100%** | — | **[X.X]** | **L[X]** |

### 4.2 Radar Chart Description

[Describe the five-dimension radar shape in words, e.g., "dumbbell distribution," "Strategy and AI lead on the two wings while Data and Technology sag in the middle."]

```
       Strategy & Organization
              △
             /|\
            / | \
   AI ◄─────┼─────► Business Digitalization
            \ | /
             \|/
              ▽
     Technology Foundation ←→ Data Capability

[Text description: relative position and overall shape of each dimension]
```

### 4.3 Maturity Profile

**Current-state description:** [Use 1–2 paragraphs to sketch the typical profile of this maturity level.]

> **L2 example:** "The group has completed build-out of core business systems, but they exist as isolated islands. The IT team is O&M-centric, lacking planning and innovation capacity. Managers decide by experience; data is used mainly for reporting and has not formed a closed loop of analysis-driven improvement. AI remains in scattered pilot trials."

---

## 5. Gap Analysis

### 5.1 Gap Overview

| Dimension | Current Score | Target Score (L3/L4) | Gap | Gap Magnitude |
|-----------|---------------|----------------------|-----|---------------|
| Strategy & Organization | [X.X] | [X.X] | [X.X] | [Large/Med/Small] |
| Business Digitalization | [X.X] | [X.X] | [X.X] | [Large/Med/Small] |
| Data Capability | [X.X] | [X.X] | [X.X] | [Large/Med/Small] |
| Technology Foundation | [X.X] | [X.X] | [X.X] | [Large/Med/Small] |
| AI & Intelligence | [X.X] | [X.X] | [X.X] | [Large/Med/Small] |

### 5.2 Key Gap Details

#### Gap GAP-01: [Gap Name]

- **Dimension:** [Dimension name]
- **Current state:** [Description]
- **Target state (L3/L4):** [Description]
- **Root-cause analysis:** [Use 5-Why or fishbone analysis]
- **Impact scope:** [Which businesses / capabilities this gap affects]
- **Improvement difficulty:** [High/Med/Low] (considering technology, organization, culture, investment)
- **Estimated duration:** [X] months

> **Root-cause analysis example (5-Why):**
> 1. Why is data quality poor? → Because each system uses different data standards.
> 2. Why are standards inconsistent? → Because there is no unified data standard.
> 3. Why is there no standard? → Because there is no data governance organization.
> 4. Why no governance organization? → Because leadership has not yet recognized data as an asset.
> 5. Why hasn't leadership recognized it? → Because data value has not been quantified and communicated.

#### Gap GAP-02: [Gap Name]

[Same structure as above]

#### Gap GAP-03: [Gap Name]

[Same structure as above]

#### Gap GAP-04: [Gap Name]

[Same structure as above]

#### Gap GAP-05: [Gap Name]

[Same structure as above]

---

## 6. Benchmarking

### 6.1 Benchmark Selection

| Benchmark Type | Benchmark Object | Rationale |
|----------------|------------------|-----------|
| Industry leader | [XX Transport Group / XX Motorway Operator] | [Reason: a comparable peer that has attained XX certification / won XX award] |
| International benchmark | [Singapore LTA / Japan NEXCO / Rijkswaterstaat (NL) / Transport for London] | [Reason: globally recognized best practice in XX domain] |
| Cross-industry benchmark | [Maersk / DHL / Uber] | [Reason: outstanding performance in data-driven / digital operations] |

### 6.2 Benchmarking Results

| Comparison Dimension | Our Organization | Benchmark A: [Name] | Benchmark B: [Name] | Benchmark C: [Name] | Gap Analysis |
|----------------------|------------------|---------------------|---------------------|---------------------|--------------|
| Strategy & Organization | [X.X] | [X.X] | [X.X] | [X.X] | [Analysis] |
| Business Digitalization | [X.X] | [X.X] | [X.X] | [X.X] | [Analysis] |
| Data Capability | [X.X] | [X.X] | [X.X] | [X.X] | [Analysis] |
| Technology Foundation | [X.X] | [X.X] | [X.X] | [X.X] | [Analysis] |
| AI & Intelligence | [X.X] | [X.X] | [X.X] | [X.X] | [Analysis] |
| **Composite** | **[X.X]** | **[X.X]** | **[X.X]** | **[X.X]** | **[Analysis]** |

### 6.3 Lessons from Benchmark Practices

**Benchmark A — [Specific Practice Name]:**

[Describe what Benchmark A does in this domain and what our organization can learn from it.]

**Benchmark B — [Specific Practice Name]:**

[Describe what Benchmark B does in this domain and what our organization can learn from it.]

**Benchmark C — [Specific Practice Name]:**

[Describe what Benchmark C does in this domain and what our organization can learn from it.]

---

## 7. Key Findings

### 7.1 Top Ten Findings

| No. | Finding | Impact Level | Urgency | Suggested Priority |
|-----|---------|--------------|---------|--------------------|
| F01 | [Finding 1] | [High/Med/Low] | [Urgent/Short-term/Mid-term] | [P0/P1/P2] |
| F02 | [Finding 2] | [High/Med/Low] | [Urgent/Short-term/Mid-term] | [P0/P1/P2] |
| F03 | [Finding 3] | [High/Med/Low] | [Urgent/Short-term/Mid-term] | [P0/P1/P2] |
| ... | ... | ... | ... | ... |
| F10 | [Finding 10] | [High/Med/Low] | [Urgent/Short-term/Mid-term] | [P0/P1/P2] |

### 7.2 Highlights (Practices Worth Recognizing)

1. [Highlight 1: description + evidence]
2. [Highlight 2: description + evidence]
3. [Highlight 3: description + evidence]

### 7.3 Pain Points (Critical Issues to Resolve)

1. [Pain point 1: description + root cause + impact]
2. [Pain point 2: description + root cause + impact]
3. [Pain point 3: description + root cause + impact]

### 7.4 Opportunities (Potential High-Value Improvement Areas)

1. [Opportunity 1: description + expected value]
2. [Opportunity 2: description + expected value]
3. [Opportunity 3: description + expected value]

---

## 8. Improvement Recommendations

### 8.1 Recommendation Overview

| ID | Recommendation | Linked Finding | Category | Priority | Est. Investment | Expected Benefit |
|----|----------------|----------------|----------|----------|-----------------|------------------|
| R01 | [Rec 1] | [F01, F03] | [Strategy/Business/Data/Tech/AI] | P0 | [€XX M] | [Description] |
| R02 | [Rec 2] | [F02] | [Strategy/Business/Data/Tech/AI] | P0 | [€XX M] | [Description] |
| R03 | [Rec 3] | [F04] | [Strategy/Business/Data/Tech/AI] | P1 | [€XX M] | [Description] |
| ... | ... | ... | ... | ... | ... | ... |

### 8.2 Key Recommendation Details

#### Recommendation R01: [Recommendation Title]

- **Priority:** P0 (launch immediately)
- **Linked gap:** [GAP-XX]
- **Content:** [Detailed description of the recommendation, action steps]
- **Key initiatives:**
  1. [Initiative 1]
  2. [Initiative 2]
  3. [Initiative 3]
- **Estimated investment:** [Staff / budget / time]
- **Expected effect:** [Quantified KPI improvement]
- **Risks & mitigation:** [Implementation risks and responses]
- **Prerequisites:** [What must be completed first]
- **Accountable unit:** [Lead unit / supporting units]

#### Recommendation R02: [Recommendation Title]

[Same structure as above]

#### Recommendation R03: [Recommendation Title]

[Same structure as above]

---

## 9. Implementation Roadmap

### 9.1 Three-Phase Master Roadmap

| Phase | Period | Theme | Core Objective | Maturity Target |
|-------|--------|-------|----------------|-----------------|
| Phase 1 | [202X Q1–Q4] | [Build foundation, close gaps] | [Objective] | L2 → L2.X |
| Phase 2 | [202X Q1–Q4] | [Build platforms, connect data] | [Objective] | L2.X → L3.X |
| Phase 3 | [202X Q1–Q4] | [Enable intelligence, create ecosystem] | [Objective] | L3.X → L4.X |

### 9.2 Phase 1 Detailed Plan (First 90 Days)

| Week | Task | Milestone / Deliverable | Owner |
|------|------|-------------------------|-------|
| W1–W2 | [Establish Digital Transformation Working Group] | [Appointment doc, charter] | [Group Office] |
| W3–W4 | [Develop data standards system] | [Data Standards V1.0] | [Data Working Group] |
| W5–W6 | [Upgrade / select XX system build or replacement] | [Tech selection report] | [IT Dept] |
| ... | ... | ... | ... |
| W11–W12 | [Phase 1 review & Phase 2 detailing] | [Phase report] | [Transformation Office] |

### 9.3 Resource Investment Estimate

| Phase | # Projects | External Spend (€M) | Internal Effort (person-months) | Key Resource Needs |
|-------|-----------|---------------------|--------------------------------|--------------------|
| Phase 1 | [X] | [XXXX] | [XXX] | [XX-domain expert / X-type servers / X software licenses] |
| Phase 2 | [X] | [XXXX] | [XXX] | [XX] |
| Phase 3 | [X] | [XXXX] | [XXX] | [XX] |
| **Total** | **[X]** | **[XXXX]** | **[XXX]** | — |

### 9.4 Governance Operating Model

- **Decision layer:** Digital Transformation Steering Committee (chaired by CEO, quarterly)
- **Execution layer:** Digital Transformation Office (headed by CIO, bi-weekly)
- **Coordination layer:** Department digital liaisons (monthly)
- **External support:** Consulting advisor team (phase-based on-site)

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## 10. Appendices

### Appendix A: Scoring Rubric

[Detailed scoring criteria and evidence sources for each Tier-2 sub-item]

| Dimension | Sub-item | Score | Basis | Evidence Source |
|-----------|----------|-------|-------|-----------------|
| Strategy & Organization | Digital vision & strategy | [X.X] | [Basis] | [Interview/Doc/Survey] |
| Strategy & Organization | Organization & talent | [X.X] | [Basis] | [Interview/Doc/Survey] |
| ... | ... | ... | ... | ... |

### Appendix B: Interview Notes Summary

| No. | Interviewee | Date | Key View / Quote | Linked Dimension |
|-----|-------------|------|------------------|------------------|
| 1 | [Name] | [Date] | "[Verbatim quote]" | [Dimension] |
| 2 | [Name] | [Date] | "[Verbatim quote]" | [Dimension] |
| ... | ... | ... | ... | ... |

### Appendix C: System Inventory

| No. | System Name | Function Domain | Go-live Year | Vendor | Tech Architecture | Status | Linked Systems | Data Volume |
|-----|-------------|----------------|-------------|--------|-------------------|--------|----------------|-------------|
| 1 | [System] | [Domain] | [Year] | [Vendor] | [Arch] | [Live/Retired/Building] | [X] | [XX TB] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |

### Appendix D: Survey Statistics Summary

| Q No. | Question Summary | Sample | Mean | Median | Std Dev | Positive % | Negative % |
|-------|------------------|--------|------|--------|---------|------------|------------|
| Q1 | [Question] | [N] | [X.X] | [X.X] | [X.X] | [XX%] | [XX%] |
| ... | ... | ... | ... | ... | ... | ... | ... |

### Appendix E: Glossary

| Term | Full Name | Explanation |
|------|-----------|-------------|
| T-DMM | Transportation Digital Maturity Model | A framework for assessing transport-organization digital maturity |
| RICE | Reach-Impact-Confidence-Effort | A prioritization scoring framework |
| TCO | Total Cost of Ownership | Full life-cycle cost of a system |
| MEC | Multi-access Edge Computing | Distributed compute at the network edge |
| ... | ... | ... |

### Appendix F: Reference Documents

| No. | Document Name | Source | Version / Date |
|-----|---------------|--------|----------------|
| 1 | [Group medium-term digital plan] | [Internal] | [202X-XX] |
| 2 | [National / regional smart-mobility plan] | [Transport authority] | [202X-XX] |
| ... | ... | ... | ... |

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> **Report prepared by:** [Assessment team name]
> **Date prepared:** [YYYY-MM-DD]
> **Reviewed by:** [Reviewer name / title]
> **Approved by:** [Approver name / title]
>
> **Disclaimer:** This report is based on information obtained during the assessment period and reflects the assessed party's digital maturity at the assessment date. Benchmark data are drawn from public sources and are provided for comparison only. Citation and use of this report shall comply with the confidentiality terms agreed between the parties.
