Accenture and the Carnegie Mellon University Software Engineering Institute Launch AI Adoption Maturity Model to Help Organizations Scale AI with Predictable Outcomes
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The model provides a structured approach for commercial enterprises and government organizations to assess their current AI capabilities, identify gaps and build a clear roadmap for responsible, value-driven AI adoption.
“Many AI maturity models in the market now focus on high-level strategy without considering the engineering rigor that organizations actually need to scale,” said
"Organizations achieve lasting AI value and return on investment through discipline, not just speed,” said
A Proven Model for More Predictable Outcomes
The launch comes at a critical inflection point for enterprise AI. Investment is surging, with 86 percent of C-suite leaders planning to increase AI spending in 2026. Yet, execution is not keeping pace.
Against this backdrop, the SEI and
To develop the AI Adoption Maturity Model, the teams systematically reviewed more than 100 existing AI maturity efforts, conducted approximately 25 executive interviews, surveyed nearly 600 practitioners and completed intensive pilots with Fortune 500 organizations. Insights from this research were continuously folded back into the model, resulting in a framework grounded in the SEI’s four decades of leadership in maturity modeling and Accenture’s experience delivering more than 11,000 advanced AI projects worldwide.
An Engineering Approach to Enterprise-Scale AI
The AI Adoption Maturity Model is a framework for assessing the ability of an organization to perform and sustain specific technical practices to achieve two key, high-level goals: organizational change and AI lifecycle engineering.
The model divides AI-relevant capability areas into eight core dimensions: organizational strategy, workforce and culture, workflow re-engineering, risk and governance, data, engineering, operations and ecosystem. An organization’s maturity is measured by how well key practices across these dimensions are implemented, governed and sustained—providing a clear baseline and a roadmap for improvement. The model is accompanied by an assessment tool that enables structured implementation and benchmarking of client outcomes across industries.
With an assessment against the model, organizations can establish their baseline readiness to incorporate AI into workflows and tech ecosystems—enabling organizations to identify use cases, institutionalize practices, focus on the value of investments and create a structured roadmap for adoption. With rigorous reassessments, organizations can check their AI maturity and realign the roadmap to changes in the AI landscape.
“As organizations move from experimentation to enterprise-scale AI, a mature and scalable approach is essential to compete and thrive in the AI economy," said
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Dallin Hatch
Accenture
+1 719 244 6537
dallin.p.hatch@accenture.com
Source: Accenture