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.
Accenture and Carnegie Mellon SEI launch research-validated AI Adoption Maturity Model.
The model assesses AI capabilities across eight core dimensions: strategy, workforce, workflow re-engineering, risk and governance, data, engineering, operations, and ecosystem.
Development included review of 100+ existing AI maturity efforts, 25 executive interviews, and surveys of nearly 600 practitioners.
86% of C-suite leaders plan to increase AI spending in 2026, but only 21% are redesigning end-to-end processes with AI at the core.
The model includes an assessment tool for structured implementation and benchmarking.
Nearly half of executives report AI has so far delivered little impact on profit.
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. Accenture research shows that only 21 percent of organizations are redesigning end-to-end processes with AI at the core, and nearly half of executives report AI has so far delivered little impact on profit. In most cases, the barrier is not the technology itself, but mismatched expectations, misaligned applications and poorly executed implementation practices.
Against this backdrop, the SEI and Accenture set out to address a clear gap in the market. While many AI maturity models exist, most lack an engineering foundation, a clear measurement approach or validation in real-world environments.
"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."
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.
"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."
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 Kishore Durg, Lead for Accenture LearnVantage. "Developing a mature AI organization is more than learning a set of tools or techniques, it requires a cultural transformation. We are proud to support this research, helping our clients build AI-centric workflows and workforces that redefine how work gets done in the AI era. Together with the SEI, we are advancing a structured path for organizations to institutionalize AI to realize predictable and sustainable business value."
About Accenture
Accenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. Our strategy is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client-focused, AI-enabled, great place to work in the world. We bring together the talent of our approximately 786,000 people with proprietary assets and platforms, deep process and industry expertise, and leading ecosystem relationships to deliver end-to-end solutions and measurable outcomes at scale. Through our Reinvention Services, we offer broad expertise across Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology. We serve approximately 9,000 clients and generated approximately $70 billion in FY25 revenue.
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