As part of eInfochips’ AI consulting services, we have developed an AI maturity assessment framework across three phases. The framework enables organizations to understand their current AI maturity level, identify capability gaps across key dimensions including strategy, data, technology, and governance, and define a clear roadmap for scalable, sustainable, and responsible AI adoption.
The first phase of the framework focuses on discovering the organization’s current AI state. This requires conducting stakeholder interviews and documentation review to understand business goals, strategic priorities, KPIs, and challenges. This phase defines engagement scope, ROI expectations, and success metrics, introduces eInfochips’ proprietary AI maturity model across strategy, data, technology, and governance, captures self-assessment inputs, and identifies and prioritizes initial AI use cases. The outcome is an AI discovery report outlining the current maturity state, prioritized use cases, and high-level gap recommendations.
The second phase evaluates the organization’s AI maturity by analyzing assessment results and visualizing them on an AI maturity radar. It includes a detailed gap analysis across strategy, data, technology, and governance, along with dimension-specific recommendations to address maturity gaps. ROI modeling is conducted for prioritized AI use cases to quantify business value.
The third phase focuses on translating assessment insights into an actionable execution roadmap. This phase defines workstream plans with clear time horizons, estimates impact, cost, and effort, evaluates risks and dependencies, and establishes KPIs to track progress. The roadmap is aligned with business and technology owners, followed by an executive walkthrough of recommendations, Q&A, and agreement on next steps such as follow-up analysis or PoC initiatives.
The organization lacks a formal AI strategy, roadmap, and governance. AI awareness, skills, and cultural readiness are limited, with no structured change management. Data and infrastructure are fragmented and low quality, analytics capabilities are minimal, risks are managed reactively, and AI tools and models are siloed.
The organization has an early AI vision and pilot roadmap with limited leadership sponsorship. Initial data integration, governance, and basic security practices are in place, along with draft AI policies and growing risk awareness. AI pilots leverage vendor or open-source tools, supported by increasing AI literacy and upskilling efforts.
At this level, the organization has a documented AI strategy and roadmap aligned with business goals and leadership commitment, with AI value tracked through defined KPIs. Data pipelines and MLOps are standardized, AI literacy is widespread through a formal CoE, governance and responsible AI frameworks are well established, regulatory compliance is in place, and scalable AI infrastructure supports enterprise adoption.
AI is embedded in enterprise strategy with strong leadership support, high-quality governed data, federated governance, proactive ethics and risk management, mature platforms and infrastructure, seamless integration, and organization-wide AI adoption programs.
AI is fully integrated into business strategy and core operations, supported by real-time, trusted data enabling predictive and prescriptive insights, autonomous self-learning systems, embedded ethics and automated governance, and an AI-first culture driving continuous value, innovation, and societal impact.
Schedule a 30-minute consultation with our Automotive Solution Experts
Schedule a 30-minute consultation with our Battery Management Solutions Expert
Schedule a 30-minute consultation with our Industrial & Energy Solutions Experts
Schedule a 30-minute consultation with our experts
