How to Use This Assessment

For each dimension, score your organization on a 1–5 scale using the rubric provided. A score of 1 indicates significant gaps; 5 indicates full readiness. Sum your scores across all eight dimensions for a total readiness score out of 40.

Complete this assessment with input from multiple stakeholders — IT infrastructure, data engineering, ML engineering, legal/compliance, and business leadership. Single-perspective assessments consistently overestimate readiness in areas outside the respondent's expertise.

This assessment is designed for organizations evaluating their readiness for a first enterprise AI deployment or scaling an existing AI program. It is not a substitute for a detailed technical assessment by qualified AI infrastructure engineers.

Dimension 1: Data Maturity

Data quality and availability is the most common readiness gap. AI models are only as good as their training data.

Scoring Rubric

• 1 — Critical gaps: Data is siloed across systems with no integration. Data quality is unknown or poor. No data governance program exists.
• 2 — Developing: Some data integration exists. Data quality is inconsistent. Basic data governance policies exist but are not enforced.
• 3 — Functional: Key data sources are integrated. Data quality standards exist for most systems. Data governance program is active.
• 4 — Advanced: Comprehensive data integration. High data quality with automated monitoring. Mature data governance with lineage tracking.
• 5 — AI-ready: Enterprise data platform with feature store. Excellent data quality with automated remediation. Full data lineage and governance for AI use cases.

Key Questions

• Do you have sufficient labeled data for your target AI use cases?
• Can you trace the lineage of data from source systems to model training?
• Do you have data quality metrics and monitoring in place?
• Are data access controls sufficient to protect sensitive training data?

Common Gaps and Remediation

Most organizations score 2–3 on data maturity. Common gaps: insufficient labeled data for supervised learning (remediation: data labeling program or weak supervision), poor data quality in source systems (remediation: data quality initiative before AI deployment), and lack of data lineage (remediation: data catalog and lineage tooling).

Dimension 2: Compute Infrastructure

Evaluate your current compute infrastructure against AI workload requirements.

Scoring Rubric

• 1 — No AI compute: No GPU infrastructure. All compute is CPU-based. No cloud GPU access.
• 2 — Limited: Some GPU access (cloud or on-premises) but insufficient for production workloads. No dedicated AI infrastructure.
• 3 — Functional: Dedicated GPU infrastructure for development and small-scale training. Production inference on cloud or limited on-premises capacity.
• 4 — Advanced: Production-grade GPU cluster for training and inference. Workload scheduling and resource management in place.
• 5 — AI-ready: Scalable GPU infrastructure with high utilization. Full MLOps platform integration. Clear capacity planning and refresh roadmap.

Key Questions

• What GPU hardware do you currently have access to (on-premises or cloud)?
• What is your current GPU utilization rate?
• Do you have workload scheduling and resource management for GPU infrastructure?
• What is your plan for scaling compute as AI workloads grow?

Dimension 3: Networking

Network infrastructure is often the hidden bottleneck in AI deployments.

Scoring Rubric

• 1 — Standard IT networking: 1GbE or 10GbE infrastructure. No high-speed interconnect for AI workloads.
• 2 — Upgraded: 25GbE or 100GbE available. No dedicated AI cluster networking.
• 3 — Functional: 100GbE storage and management networks. Some high-speed interconnect for AI.
• 4 — Advanced: Dedicated AI cluster network (InfiniBand or 400GbE RoCEv2). Separate storage and management networks.
• 5 — AI-ready: Non-blocking InfiniBand NDR or 400GbE fabric. Optimized for distributed training. Full network monitoring and management.

Dimension 4: Storage

AI storage requirements differ fundamentally from traditional enterprise storage.

Scoring Rubric

• 1 — Traditional storage: NAS/SAN designed for IOPS, not throughput. Insufficient for AI training workloads.
• 2 — Upgraded: All-flash storage with improved throughput. Still insufficient for large GPU clusters.
• 3 — Functional: High-throughput storage (50–100 GB/s aggregate) for small AI clusters. Object storage for datasets.
• 4 — Advanced: Parallel file system (GPFS, Lustre, or WEKA) with 200+ GB/s throughput. Tiered storage for hot/warm/cold data.
• 5 — AI-ready: Scalable parallel file system matched to GPU cluster size. Automated data tiering. Full integration with MLOps platform.

Dimension 5: Talent & Capability

Talent is consistently the binding constraint for enterprise AI programs.

Scoring Rubric

• 1 — No AI capability: No ML engineers or data scientists. No AI infrastructure expertise.
• 2 — Emerging: 1–2 data scientists. No ML engineering or AI infrastructure capability. Heavy reliance on external consultants.
• 3 — Developing: Small ML team (3–5 people). Some MLOps capability. AI infrastructure managed by general IT team.
• 4 — Capable: Dedicated ML engineering team. AI platform engineers. Some AI governance expertise.
• 5 — AI-ready: Full AI Center of Excellence. ML engineers, data scientists, AI platform engineers, AI governance specialists. Clear talent development roadmap.

Key Questions

• How many ML engineers and data scientists do you have?
• Do you have AI infrastructure (MLOps platform) engineering capability?
• Do you have AI governance and risk management expertise?
• What is your talent acquisition and development strategy?

Dimension 6: Governance & Risk Management

Governance gaps create regulatory exposure and are the highest-risk readiness failure.

Scoring Rubric

• 1 — No governance: No AI governance policies. No model risk management. No awareness of applicable regulations.
• 2 — Awareness: Governance gaps identified. Some policies drafted but not implemented. Regulatory requirements understood but not addressed.
• 3 — Basic governance: Model inventory exists. Basic validation process for high-risk models. Governance policies documented.
• 4 — Mature governance: Formal model risk management process. Independent validation capability. Regulatory compliance program active.
• 5 — AI-ready: Comprehensive AI governance framework. Automated monitoring and alerting. Proactive regulatory engagement. Board-level AI risk oversight.

Dimension 7: Security

AI-specific security controls extend beyond traditional cybersecurity.

Scoring Rubric

• 1 — Standard IT security: No AI-specific security controls. Training data and model weights not specifically protected.
• 2 — Basic: Some access controls on AI infrastructure. No AI-specific threat modeling or monitoring.
• 3 — Functional: AI infrastructure segmented from corporate network. Training data access controls. Basic model security.
• 4 — Advanced: Zero-trust architecture for AI infrastructure. AI-specific threat monitoring. Adversarial robustness testing for high-risk models.
• 5 — AI-ready: Comprehensive AI security program. Full threat model coverage. Automated security monitoring. Regular penetration testing of AI systems.

Dimension 8: Organizational Readiness

Technical readiness is necessary but not sufficient — organizational readiness determines whether AI investments deliver business value.

Scoring Rubric

• 1 — Not ready: No executive sponsorship. No AI strategy. Business units not engaged. No budget commitment.
• 2 — Exploring: Executive interest but no formal sponsorship. AI strategy in development. Limited business unit engagement.
• 3 — Committed: Executive sponsor identified. AI strategy approved. 2–3 business unit champions. Budget allocated for pilot.
• 4 — Aligned: C-suite AI strategy alignment. Multiple business unit champions. Change management program active. Multi-year budget commitment.
• 5 — AI-ready: Board-level AI strategy. Organization-wide AI literacy program. Strong change management capability. AI embedded in business planning.

Scoring & Interpretation

Any dimension scoring 1 is a critical gap that should be addressed before production AI deployment, regardless of total score. Governance and security gaps at score 1 create regulatory and operational risk that cannot be offset by strength in other dimensions.

Next Steps

After completing this assessment:

1. Prioritize gaps: Identify the 2–3 dimensions with the lowest scores and highest impact on your target use cases
2. Build a remediation roadmap: For each gap, define specific actions, owners, timelines, and success criteria
3. Engage infrastructure partners: For compute, networking, and storage gaps, engage qualified AI infrastructure partners for detailed assessment and solution design
4. Reassess quarterly: Track progress against the remediation roadmap and update scores quarterly
5. Align with use case timeline: Ensure readiness gaps are addressed before the planned deployment date for your first production AI use case

DCS Global's AI Infrastructure Assessment provides a detailed technical evaluation of your compute, networking, storage, and facility readiness — with specific hardware recommendations, architecture designs, and phased implementation plans. Contact our engineering team to schedule an assessment.