AI readiness assessment for AI and ML teams

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 characterise the quality and reliability of data reaching your models in production?

No data quality checks - models consume raw upstream data Manual quality checks run periodically by the data team Automated schema validation and volume checks on critical pipelines Data contracts between producers and consumers with SLA enforcement Real-time data quality scoring with automated circuit-breakers stopping model inference on degraded data

How mature is your feature engineering and feature management practice?

Features are computed ad hoc per project, no reuse A shared feature library exists informally in notebooks or scripts A feature store is in use for online or offline serving Feature store with versioning, lineage, and point-in-time correctness Feature store with automated backfilling, freshness SLAs, and cross-team discovery

How rigorous is your experimentation and model evaluation process?

Experiments are ad hoc with no tracking or reproducibility Experiment results are logged manually in spreadsheets or documents A tracking tool (MLflow, W&B, Comet) is used but inconsistently All experiments tracked with hyperparameters, metrics, and artefacts; reproducibility enforced Automated experiment pipelines with statistical significance testing and champion/challenger rollout

How managed is the full lifecycle of your models from training to deprecation?

Models are deployed and forgotten - no lifecycle management Retraining is done manually when model performance degrades visibly A model registry tracks versions; retraining is scheduled periodically Automated drift detection triggers retraining; deprecation policies are defined Fully autonomous model lifecycle: continuous monitoring, auto-retrain, auto-validate, auto-promote

How many of your AI use cases are in governed production versus pilot or PoC stage?

None - everything is at exploration or PoC stage 1–2 use cases in production, informally managed Several in production with basic monitoring and business KPI tracking A portfolio of production models with formal governance and measured ROI 10+ production models, each with an assigned business owner, SLA, and automated health reporting

What is your team's current depth of Generative AI implementation?

No GenAI work in progress Experimenting with foundation model APIs (OpenAI, Anthropic, Gemini) in sandboxes RAG prototype built on internal data, not yet in production Production RAG or fine-tuned model deployed with enterprise data and governance Multi-model agentic architecture in production with evaluation framework and human-in-the-loop oversight

How do you evaluate model quality beyond standard accuracy metrics?

We rely on a single accuracy or loss metric Precision, recall, F1, and AUC tracked per model type Business impact metrics tracked alongside technical metrics Cohort analysis, fairness metrics, and calibration checks included in evaluation Automated evaluation suite: fairness, robustness, calibration, business KPI, and adversarial testing

How mature is your approach to AI fairness, bias, and responsible AI?

Not part of our current practice Awareness of the issue but no formal process Bias checks conducted manually at model review stage Automated bias and fairness metrics included in the deployment pipeline Ongoing post-deployment fairness monitoring with defined remediation thresholds

How effectively does your AI/ML team collaborate with business stakeholders to define use cases and success criteria?

Requests come in as technical briefs with little business context We meet with stakeholders at the start and end of projects A structured discovery process defines the business problem before modelling begins Embedded ML product managers translate business needs to model requirements AI/ML team co-owns business KPIs with product and commercial teams

How clearly can you demonstrate the business impact of your AI programme to executive stakeholders?

We report technical metrics only - no business impact reporting Anecdotal evidence of impact but no structured measurement Individual use cases have business KPIs tracked and reported A portfolio view of AI business impact is available on request AI business impact is presented at board level as part of regular reporting

AI readiness assessment and roadmap for AI and ML teams