AI readiness assessment for engineering leaders

What you're about to get

This AI readiness assessment is informed by Gartner's AI Maturity Model, designed to help you understand exactly where your organisation stands and what to fix first.

Where you stand across 10 dimensions of AI maturity

How you compare to organisations like yours

A 90-day plan tied to what's actually holding you back

The one AI use case worth starting with first

Takes 2 minutes. Your results are available instantly.

How would you describe your current cloud and compute infrastructure for AI workloads?

On-premise only, no cloud usage Some cloud usage (general workloads), no AI-specific compute Cloud with CPU instances available for ML workloads Managed GPU/TPU instances or AI-optimised cloud services (SageMaker, Vertex, AzureML) Hybrid or multi-cloud AI platform with auto-scaling and spot/preemptible support

What best describes your data infrastructure maturity for AI pipelines?

Data lives in isolated databases and spreadsheets, no unified access A data warehouse exists but no streaming or feature engineering layer A modern data stack (dbt, Airflow, Spark) with basic orchestration A feature store and automated data quality monitoring are in place Real-time streaming + batch pipelines with schema enforcement, SLAs, and alerting

How well are your core business systems (CRM, ERP, product databases) integrated for AI consumption?

Systems are siloed with no APIs or integration layer Point-to-point integrations exist but are fragile and undocumented An event bus or API gateway handles some integration needs A data mesh or lakehouse architecture provides governed, unified access Real-time event streaming from all core systems to AI consumption layer

What is the current state of your MLOps / model deployment infrastructure?

Models are deployed manually from notebooks or scripts A basic CI/CD pipeline for model code, no model-specific tooling A model registry and serving infrastructure exist (e.g. MLflow, BentoML) Full MLOps pipeline: versioning, canary deployment, drift detection, auto-rollback Autonomous retraining pipelines triggered by drift thresholds, no manual intervention

How is AI/ML data access governed from a security and compliance standpoint?

No specific controls - AI workloads share general data access permissions Role-based access control exists but AI-specific policies are undefined Data classification and access tiers defined for AI training data PII masking, audit logging, and data lineage tracking in AI pipelines Zero-trust architecture, differential privacy, and regulatory mapping per AI system

Does your engineering team have the capacity and skills to own AI infrastructure long-term?

No engineers with ML infrastructure or data engineering experience 1–2 generalists with some exposure, stretched across other priorities A dedicated data engineering team, limited MLOps expertise ML engineers and data engineers with defined ownership of the AI platform A platform team owning AI infrastructure as a product, with embedded SRE support

How does your organisation approach the build vs. buy decision for AI components?

No structured framework - decisions are made ad hoc Tendency to build everything internally regardless of maturity of vendor options Evaluation criteria exist but vendor landscape knowledge is limited Clear policy: buy commodity components, build on proprietary differentiators A strategic vendor portfolio with active partnership management and exit criteria

How are AI systems monitored in production from an engineering perspective?

No production monitoring beyond basic server health Application-level monitoring (latency, uptime) but no model-specific metrics Model accuracy and prediction volume tracked, no automated alerting Automated alerting on prediction drift, feature distribution shift, and SLA breaches Full observability stack: traces, data drift, business KPI correlation, shadow models

What is your resilience posture for critical AI systems?

No documented rollback or fallback procedure for AI systems Manual rollback is possible but not tested regularly Model versioning and rollback procedures are documented Canary and blue-green deployments used, fallback to rule-based systems available Shadow models, adversarial testing, human-in-the-loop escalation for all critical systems

How actively is AI/ML technical debt identified and managed in your engineering practice?

No awareness or tracking of AI-specific technical debt Known issues are tracked informally but rarely prioritised AI tech debt is captured in the backlog alongside product debt Quarterly AI tech debt reviews with dedicated remediation capacity Automated model health scoring and deprecation pipelines integrated into the SDLC

AI readiness assessment and roadmap for engineering leaders