Report Overview
"AI Readiness White Paper: Enterprise Digital Transformation Path and Assessment Guide" is a joint industry report published by KPMG and Cisco in May 2025. The report provides a comprehensive analysis of how enterprises can prepare for AI transformation, covering strategic frameworks, infrastructure requirements, and assessment methodologies.
Key Insight: AI represents the next frontier in enterprise digital transformation. Successful AI adoption requires a holistic approach encompassing infrastructure readiness, data governance, organizational capabilities, and strategic alignment. Enterprises must evolve from fragmented AI experiments to systematic, organization-wide AI transformation.
Key Data Points
Key Insights Summary
AI Transformation Requires Holistic Readiness
Enterprise AI readiness extends beyond technology to encompass seven core dimensions: technology, data, business, strategy, governance, talent, and organizational structure. Both "hard capabilities" (technology, data, business) and "soft capabilities" (strategy, governance, talent, organization) are essential for successful AI transformation.
Infrastructure as the Foundation
76% of surveyed enterprises prioritize AI infrastructure (compute, network, storage) as their top strategic focus. The evolution from hardware-centric to AI factory infrastructure requires integrated solutions that balance performance, flexibility, and cost-effectiveness.
Hybrid Deployment Dominates
61% of enterprises prefer hybrid AI deployment (combining on-premises and public cloud), compared to 32% opting for fully on-premises and only 7% for public cloud-only approaches. This reflects the need to balance data security, compliance, and scalability.
Data Quality Determines AI Competitiveness
As AI models evolve, data quality becomes the decisive factor in model performance. Enterprises face challenges with Chinese language corpus scarcity, limited data sources, vertical domain data shortages, and slow data updates, requiring comprehensive data governance frameworks.
Business Value Assessment is Critical
73% of enterprises identify business value assessment (ROI analysis) as their primary AI transformation requirement. However, quantifying AI value remains challenging, particularly for decision-making scenarios where returns are difficult to measure.
Security and Governance Gaps Persist
Only 29% of Chinese enterprises can comprehensively detect and prevent unauthorized tampering in AI applications. As AI adoption accelerates, enterprises need end-to-end security frameworks covering infrastructure, models, and applications throughout the AI lifecycle.
Content Overview
Document Contents
Emerging AI Wave and Industry Opportunities
The report traces the evolution of AI through Perception AI, Generative AI, Agentic AI, and Physical AI. Chinese AI development is shifting from "brute force aesthetics" to "cost-effectiveness revolution," with trends including:
- Foundation model open-sourcing + vertical domain privatization accelerating AI democratization
- Generative AI penetrating various industries, with 302 generative AI services completing registration by end of 2024
- Computing demands continuing to rise, with computing ecosystems shifting toward distributed computing networks
- Data quality determining model competitiveness, requiring comprehensive upgrades
Enterprise AI Transformation Journey
Based on a survey of 42 enterprises across China, the report identifies key patterns in enterprise AI adoption:
Technical Architecture: Enterprises typically start with scenario-based system design, adopt hybrid deployment approaches, and address cloud risks, private domain security, and model hallucination mitigation.
Data Governance: Companies focus on data governance framework establishment and data quality improvement, forming common choices like data standardization systems and intelligent cleaning tools.
Infrastructure: Enterprises balance innovation with pragmatism, weighing AI foundational capability building against business requirement fulfillment through hybrid deployment and collaborative management.
Organizational Systems: Companies build on agile and collaborative organizational mechanisms,大力推进AI-related team capability building and employee risk response.
AI Ready Hard Capabilities Analysis
The report provides a detailed framework for analyzing enterprise AI hard capabilities across infrastructure, model service & orchestration, and service governance layers.
Infrastructure Layer
Compute: AI computing chips are the "core engine" of AI development. The shift from general-purpose CPUs to specialized AI chips (GPUs, TPUs, NPUs) requires different capabilities for cloud, edge, and endpoint scenarios.
Network: Intelligent network architecture serves as the "accelerator" for enterprise AI transformation, evolving from Software-Defined Networking (SDN) to Intent-Based Networking (IBN).
Storage: High-performance storage systems must handle PB-level multimodal data with requirements for speed, capacity, and reliability, with distributed storage becoming essential.
Data: Dynamic data governance, multimodal data governance, and synthetic data generation address the "not enough," "not good enough," and "not usable" challenges in data management.
Model Service & Orchestration Layer
This layer acts as the central hub of enterprise AI capabilities, bridging infrastructure and business applications through multi-model management, AI agent orchestration, and standardized communication protocols.
Service Governance Layer
Covering security, trustworthiness, and full-stack governance, this layer ensures compliant, secure, and sustainable operation of enterprise AI systems through infrastructure security, model security, application security, and trustworthy AI principles.
AI Ready Transformation Assessment System
The report presents a comprehensive assessment framework with 4 dimensions, 13 primary indicators, and 41 secondary indicators:
Enterprise Architecture: Assesses strategic decoding, architecture governance, and architecture evaluation capabilities.
Data Assets: Evaluates data requirements, design/development, operations, and retirement processes.
Infrastructure: Measures on-demand configuration, technology selection, flexible adjustment, and stable operation.
Organizational Systems: Examines organizational structure, governance mechanisms, and talent management.
The assessment system classifies enterprises into five maturity levels: Initial, Managed, Stable, Quantitatively Managed, and Optimizing.
Conclusion and Outlook
AI represents an exponential technology with rapid development pace. The report anticipates several key trends:
- Open-source ecosystems accelerating AI democratization across industries
- Human-machine collaboration deeply integrating into enterprise operations and management
- Products and services evolving from "function implementation" to "experience reconstruction"
- Data assets becoming increasingly prominent as core enterprise competitive advantages
- Secure and trustworthy AI compelling enterprises to strengthen internal governance and risk management systems
The report concludes with a 7-step AI Ready transformation action guide: Assessment-Driven Development, Value as Anchor, Security as Principle, Architecture First, Strengthen Foundation, Internal-External Cultivation, and Rapid Iteration.
Note: The above is only a summary of the report content. The complete document contains extensive data, charts, and detailed analysis. We recommend downloading the full PDF for in-depth reading.