What is your level of AI Readiness?
Is your organisation truly AI‑ready? Not just experimenting, but creating real business value?
Please Note: Take this quick 12‑question assessment to discover your current maturity level and the steps to advance. For a deeper, tailored analysis, contact us directly at MAIL.
Does your organisation have one or more Data Teams?
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No in-house data expertise
Relying on external consultants or early hiring efforts/reskilling efforts
Internal and centralised data teams
Organisation-wide data fluency; cross-functional data innovation teams
Does your organisation have one or more AI Teams?
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No in-house AI expertise
Relying on external consultants or early hiring efforts/reskilling efforts
Internal and centralised AI team
Organisation-wide AI fluency; cross-functional AI innovation teams
Do you already have established/deployed AI use cases linked to business goals?
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No real AI use cases
Running pilots or POCs in isolated areas
Several deployed AI solutions with measurable impact
AI embedded into core business functions
Does your organisation have a known cross Data Strategy?
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No strategy and no data-driven decision making
Data-driven decision making
D&A strategy aligned with business goals with leadership support
D&A strategy is core to strategic planning and innovation
Does your organisation have a known cross AI Strategy?
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No strategy nor vision for AI
AI Roadmap under definition
AI Roadmap defined and ongoing
AI strategy success being measured and refined
What AI governance initiatives are undergoing?
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No ethical guidelines or governance frameworks
Some awareness of bias and compliance issues
Ethical AI principles, risk assessments, and audits in place
Regularly reviewing and updating governance policies to adapt to evolving regulations and technological advancements.
What data governance initiatives are undergoing?
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No clear data ownership and ad hoc protections
Roles are informally defined and there are basic policies in place
Formal ownership and stewardship and standardised and enforced controls
Enterprise-wide accountability with defined roles and dynamic, risk-based security and compliance
What data quality initiatives are undergoing?
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No processes in place to assess or improve data quality
Some quality checks performed manually or periodically
Formalised data quality standards and automated validation checks
Proactive, real-time quality monitoring and self-healing data pipelines
Is your data accessible?
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Data is siloed, incomplete, or inaccessible
Some centralised data efforts exist; basic analytics available
Reliable data pipelines, quality controls, and shared platforms in place
Real-time streaming, and integrated ML pipelines
Is your metadata AI-ready?
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No metadata available to be used in AI context
Some labelling and categorisation efforts
Design of metadata management framework for AI (with respect to semantics, lineage, quality, ownership and confidentiality)
Metadata management framework for AI implemented with respect to semantics, lineage, quality, ownership and confidentiality
Does your organisation have an AI Architecture in place?
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Sandbox Environment
Access to AI tools and frameworks that are industry standards
AI reference architecture defined and undergoing
Established MLOps/ ModelOps practices & AI observability system
Does your organisation have the infrastructure to manage AI Lifecycle?
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No dedicated infrastructure for AI. Experiments are local and informal.
Basic infrastructure established to support small-scale AI/ML workloads (Cloud/on-prem storage available, some ETL processes, Basic compute, Manual deployments)
Scalable infrastructure (Centralised data lakes/warehouses, Containers, CI/CD for models)
Fully AI-ready infrastructure integrated with enterprise systems, optimised for scale, speed, and compliance (Model serving APIs, Model monitoring tools, Scalable GPUs/TPUs, LLM integration)
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Your level of AI Readiness?
To know your organisation AI maturity level, please, fill the details below:
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