Digital
Maturity
Paradigm
AI-Enabled Digital Maturity Framework
Digital maturity in the age of AI measures how well organizations design, operate, and govern digital and AI capabilities in line with strategy, risk tolerance, and operational reality. The Digital Maturity Paradigm (DMP) connects assessment to evidence, governance, and outcomes so leaders can benchmark, transform, and verify progress with confidence.
A shared 1-5 language for assessing AI-enabled digital maturity.
Reactive, siloed, and ungoverned digital and AI usage.
Isolated pilots and inconsistent adoption.
Documented practices with uneven execution.
Standardized, governed, and measurable operations.
AI-enabled, continuously improving, and resilient.
Three pillars, eight core dimensions, and 184 defined capabilities across AI and digital.
Evidence-driven validation that proves maturity while protecting sensitive details.
Cryptographically secured proofs of AI and digital maturity milestones.
Immutable records of assessments, outcomes, and governance checkpoints.
Share maturity status without exposing sensitive AI or data details.
Access-controlled playbooks, artifacts, and cross-industry learnings.
Cross-industry collaboration to evolve AI and digital maturity standards.
Encrypted proof artifacts with role-based access and audit trails.
Filter by pillar, core dimension, or search for a specific practice.
Launch a focused assessment for a specific pillar.
Leadership, culture, and AI-enabled governance maturity.
Start Pillar AssessmentWorkforce optimization, intelligent experience, and engagement.
Start Pillar AssessmentContinuous learning through shared playbooks, artifacts, and cross-industry insights.
COBIT 2019, ITIL 4, ISO 27001:2022, NIST SP 800-53, CIS Controls v8.
Curated artifacts, testing procedures, and stakeholder maps for each capability.
Translate maturity gaps into targeted programs with measurable KPIs.
Built for leaders who need alignment, evidence, and measurable outcomes.
Strategic oversight, cross-functional alignment, and accountability for outcomes.
Drive AI-enabled change management and coordinate enterprise-wide initiatives.
Integrate modernization with AI governance, risk management, and resilience.
Validate progress with evidence aligned to internal and external standards.
Benchmark clients, compare outcomes, and deliver advisory value.
A continuous lifecycle to assess, transform, validate, and adapt.
Define AI and digital ambitions, constraints, and success criteria.
Establish current maturity across digital, AI, and risk dimensions.
Translate gaps into strategic initiatives and investment priorities.
Execute modernization, AI enablement, and governance improvements.
Track outcomes, risks, and maturity progression with evidence.
Continuously refine capabilities as technologies and needs evolve.
Launch an assessment and validate progress with evidence-ready outputs.
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