AI and Intelligent Automation Assessment
Evaluates AI pipeline and MLOps maturity across operating model and platform, ML security engineering, data, feature and label engineering, model evaluation and validation, LLMOps and GenAI engineering, pipeline automation and release, observability and drift, serving reliability and cost, and measurement and improvement.
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Head of ML platform, head of AI engineering, MLOps director. Secondary: data science and data engineering leads, model risk and validation teams, AI security engineers and SRE leads.
Shows where ML and GenAI delivery still depends on manual steps and individuals, where models cannot be reproduced, validated or rolled back, where GenAI changes ship untested, and where failures and drift go unseen. Gives each gap a concrete next step and a measurable target.
Many ML and GenAI programs stall between a promising notebook or prompt and a dependable production service. Models are trained by hand, prompts change without testing, retrieval indexes go stale and drift is found by customers.
This assessment follows the order in which a dependable ML capability is built: a lifecycle standard scaled to risk, a platform run as a product, and ML-specific security; then automated dataset, lineage, quality, feature and label practice; tracked experiments, a model registry, evaluation, peer review and independent validation; prompt versioning, evaluation harnesses, retrieval, guardrails, a model gateway and agent runtime controls; automated pipelines with adversarial tests, risk-tiered gates, human oversight, rollout and rollback; logging, drift, skew, tracing and retraining; serving objectives, fallback, incidents, unit cost and retirement; and delivery measures and improvement.
It tests whether practice is enforced by the pipeline, not only written down. Questions marked (Bridge) check the link to the model that owns a neighboring practice: AI Governance, DevOps Engineering, DevSecOps, Non-Human Identity and Secrets Management, SRE, DevOps and ITSM Convergence, AI Spend Management, Capacity Performance Management, AI Use Case Lifecycle Management, Post-Incident Review and Continual Service Improvement. Each question is scored on whether the practice is documented, implemented and effective.
Each domain is a group of related questions, scored on its own and rolled up into your overall result.
01 · 7 questions
Lifecycle standard scaled to risk tiers, platform reference architecture, platform as a product, environments, infrastructure as code, endpoint reconciliation with the AI System Inventory and accelerator quotas
02 · 6 questions
Access to ML assets, applied threat models, ML images and libraries, secrets in notebooks and pipelines, provenance of external models and safe model file formats
03 · 8 questions
Automated dataset and lineage capture, sensitive data handling in pipelines, dataset versions, data quality gates, feature management, feed freshness and labeling
04 · 8 questions
Experiment tracking, the model registry, reproducibility, evaluation with baseline comparison, responsible AI tests in the pipeline, peer review, independent validation and system card data
05 · 9 questions
Prompt and configuration versioning, evaluation harness and golden sets, RAG ingestion and indexes, permission-aware retrieval, fine-tuning controls, guardrails, model gateway, provider model changes and agent deployment
06 · 7 questions
Automated training pipelines, ML checks in the delivery pipeline, automated adversarial testing, risk-tiered promotion gates, human oversight built in, progressive rollout and rollback
07 · 8 questions
Inference logging, quality monitoring, drift detection, training-serving skew, end-to-end tracing, GenAI tracing and sampling, alert routing, retraining and feedback
08 · 7 questions
Serving objectives, scaling, fallback, ML incident response, unit cost, optimization and model retirement
09 · 5 questions
ML delivery measures, ML technical debt, platform adoption and satisfaction, learning from ML incidents and the improvement roadmap
Measures that show whether the improvements hold.
| KPI |
|---|
| Registered Production Models - Share of production models served from a registry version. Target: 100%. |
| Harness-Gated GenAI Changes - Share of production GenAI changes released only after passing the evaluation harness. Target: 100%. |
| Validated High-Risk Models - Share of high-risk models in use with a current independent validation. Target: 100%. |
| Drift Alerts Closed in Time - Share of drift alerts classified and closed within the agreed time. Target: 90% or higher. |
| Model Lead Time - Median days from the first committed model change to production. Target: 7 days or fewer (DORA 2024 high band, applied to model changes). |
Questions are written in plain language and mapped to:
Practices this assessment relies on. Assessing them too gives a fuller picture.
An overall score and a score for each of the 9 domains.
A clear view of where current practice falls short, and why it matters.
Prioritized next steps to move each area to the next level.
Results you can share with leadership and track over time.
Register, take the assessment, and get your maturity score with a clear improvement roadmap.
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