Accenture and the Carnegie Mellon University Software Engineering Institute Launch AI Adoption Maturity Model to Help Organizations Scale AI with Predictable Outcomes

Accenture (NYSE: ACN) and the Carnegie Mellon University Software Engineering Institute (SEI) today launched the AI Adoption Maturity Model, a research-validated framework designed to help organizations move beyond AI experimentation to scale artificial intelligence with measurable, repeatable outcomes.

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Accenture and the Carnegie Mellon University Software Engineering Institute (SEI) today launched the AI Adoption Maturity Model, a research-validated framework designed to help organizations move beyond AI experimentation to scale artificial intelligence with measurable, repeatable outcomes.

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 Manish Sharma, Chief Strategy and Services Officer at Accenture. “What we’ve built with the SEI is fundamentally different. It’s grounded in decades of maturity-modeling discipline, validated through real-world pilots with Fortune 500 companies, and designed to meet organizations where they are across eight critical dimensions of AI readiness. This practitioner-focused framework helps leaders move from AI ambition to measurable, repeatable outcomes.”

"Organizations achieve lasting AI value and return on investment through discipline, not just speed,” said Ipek Ozkaya, technical director of AI-native software engineering at the SEI. “True AI maturity is not measured by how much AI an organization deploys, but by its ability to build trustworthy and resilient capabilities, rigorous engineering practices, and governance approaches aligned with business outcomes and evolving technological realities. AI adoption success is reflected in how an organization can effectively orchestrate these practices. Our approach to developing this AI Adoption Maturity Model includes continuous refinement, real-world application, and community engagement, to both help organizations drive sustainable AI transformation and advance the state of practice.”

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