AI Maturity Model
Evaluate your organization's AI readiness and maturity across key dimensions including strategy, infrastructure, talent, and implementation capabilities.
The Synozur AI Maturity Model is a five‑stage framework—Explore, Build, Scale, Transform, Frontier—that gives business and technology leaders a common language to assess readiness, align investments, and sequence AI initiatives with confidence. It’s designed to move you from isolated experimentation to an enterprise growth engine where AI is orchestrated across people, process, data, and platforms.
How it works
A conversational, guided assessment produces an AI maturity score on a 100–500 scale, then maps targeted “next‑level” actions by dimension. The experience was specified for executives and IT leaders—clear, empathetic, and outcome‑focused—so teams can act immediately, not just admire the problem.
What you receive
Instant results—no login required: complete the assessment and immediately see your 100–500 maturity score.
Downloadable PDF report: personalized recommendations by stage and dimension, plus a shareable summary for stakeholders.
Follow‑on resources: links to best‑practice guides, workshops, and enablement to move from score to impact.
Model Information
The five stages (at a glance)
Explore – isolated pilots validate potential; strategy and governance are emerging.
Build – foundations form: core platform, data plumbing, and initial use cases with guardrails.
Scale – programs expand across functions, with standard processes, training, and support.
Transform – AI reshapes operating models and customer experiences; benefits appear in growth and efficiency. AI for Enterprise ITDMs Super Deck
Frontier – best‑in‑class firms orchestrate human‑AI teaming and continuous innovation for durable advantage.
Dimensions we score (with what “good” looks like)
Strategy & Leadership – Executive sponsorship, funding, and roadmaps tie AI to strategic outcomes; governance committees make fast, informed decisions.
Talent & Skills – Role‑based upskilling (execs, managers, makers), hands‑on enablement, and coaching loops elevate adoption quality.
Data & Infrastructure – Secure, well‑governed data and cloud services support safe experimentation and reliable production workloads.
Use‑Case Integration & Value Focus – A balanced portfolio moves from quick‑win pilots to embedded, measurable use cases with clear ROI and owner accountability.
Governance & Responsible AI – Policy, risk, privacy, and ethics are operationalized with intake forms, review cadences, and auditability.
Culture & Change Management – Adoption is sustained through communication, training, incentives, and communities of practice.