AI Infrastructure & AI Readiness Assessment

Assess whether a company’s infrastructure, operations, and governance can reliably support AI workloads in production — not experiments.

This assessment is focuses on what breaks when AI meets reality: cost, scale, security, latency, and control.

What This Assessment Evaluates

1. Data Foundations & Readiness

2. Compute, Scaling & Cost Control

3. AI‑Ready Architecture Patterns

4. MLOps, Observability & Operations

5. Security, Compliance & Control

Who This Assessment Is For

- CTOs, Heads of Engineering, Platform, Infrastructure, or DevOps

- SaaS, digital platforms, data‑driven companies

Teams that:

- Already run production systems in cloud or hybrid environments

- Plan to introduce AI features (LLMs, ML, computer vision, analytics)

- Face data residency, compliance, or cost pressure

How the Assessment Works

You receive:

- 10–12 multiple‑choice questions

- Based on real production scenarios, not theory

- Focused on how AI systems would behave under load, failure, or audit

⏱ 6–8 minutes

🧠 Infrastructure & operations focused

Section 1: Data Foundations & Readiness

Q1. How are data sources for AI workloads defined and owned?

Data sources are ad-hoc or unclear

Key sources known, ownership informal

Data sources documented, ownership defined

Data products clearly owned, versioned, and governed

Q2. How is sensitive or regulated data handled in AI workflows?

No clear controls or tracking

Basic access restrictions

Data access logged and reviewed

Full auditability, residency rules enforce

Section 2: Compute, Scaling & Cost Control

Q3. How do you provision compute for AI workloads?

Manual provisioning, no isolation

Shared infrastructure with basic limits

Separate environments for AI workloads

Dynamic provisioning with clear guardrails

Q4. Do you understand the cost of running AI features in production?

No visibility into AI-related costs

Rough monthly estimates

Cost tracked per workload or service

Cost tracked per model / feature with alerts

Section 3: AI-Ready Architecture

Q5. How isolated are AI services from core systems?

Fully coupled to core application

Dedicated AI services with controlled interfaces

Strong isolation and failure containment

Q6. How do you handle AI model changes or failures in production?

Manual fixes, downtime expected

Limited rollback options

Versioned deployments with rollback

Automated rollout, rollback, and traffic control

Section 4: MLOps, Observability & Operations

Q7. How are AI systems monitored in production?

No AI-specific monitoring

Basic infra metrics only

Latency and error monitoring

Full observability including cost and performance signals

Q8. Who owns AI system reliability and incidents?

Shared or unclear responsibility

Defined on-call ownership

AI incidents integrated into SRE / incident workflows

Section 5: Security, Compliance & Control

Q9. Who can access AI models, endpoints, and data?

Broad or unmanaged access

Basic role‑based access

Restricted access with reviews

Least‑privilege access with audit trails

Q10. How are third‑party AI services evaluated for risk?

Informal technical review

Security and compliance checks

Formal risk, legal, and compliance assessment