Fortune 500 companies are already using the model to establish a baseline for adopting artificial intelligence technology and plan for future AI investments
PITTSBURGH, June 8, 2026 /PRNewswire/ — The Carnegie Mellon University Software Engineering Institute (SEI) and Accenture today released the Artificial Intelligence (AI) Adoption Maturity Model, an empirically validated framework designed to help organizations move beyond AI experimentation and scale artificial intelligence with measurable, repeatable outcomes.
The model, available to download from the SEI Digital Library, provides a field-tested, structured approach for government and commercial enterprises to assess their current AI capabilities, identify gaps and build a clear roadmap for responsible, value-driven AI adoption.
Bridging the Gap from Strategy to Practice
AI investments, optimism and accessibility are rising, but 95 percent of organizations are realizing no returns. Only 8 percent of companies are scaling AI at an enterprise level and embedding the technology into their core business strategy to maximize value.
The technology is not always the culprit. Mismatched expectations, misaligned applications and poorly executed or untested implementation often keep organizations from realizing immediate value from a technology investment. Organizations need to shift their focus from hype-driven experimentation to establishing foundational capabilities and practical, measurable outcomes.
The majority of businesses and government agencies lack an adaptable, measurement-based approach for consistently assessing AI adoption maturity. Such an approach would enable predictable outcomes and the building of roadmaps to strengthen the practices required to meet AI goals.
“Many AI maturity models in the market now focus on high-level strategy without considering the engineering rigor that organizations need to actually scale,” said Manish Sharman, Chief Strategy and Services Officer for 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.”
An Engineering Approach to Enterprise-Scale AI
The AI Adoption Maturity Model is a framework for assessing the ability of an organization or unit to perform and sustain specific technical practices to achieve 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.
Achievement of the model’s capability areas across each dimension will indicate one of five levels of AI adoption maturity:
- Exploratory AI: Learning about AI grounds adoption in the organization’s context, culture and objectives.
- Implemented AI: Exemplar AI-enabled systems and workflows show potential positive impacts.
- Aligned AI: Integrated, consistently managed AI demonstrates return on investment and value.
- Scaled AI: Operations-integrated AI has predictable performance across the enterprise. Successes can be repeated.
- Future-Ready AI: Consistently replicable and scalable AI initiatives drive successful, predictable innovations.
“Our industry often assumes discipline can be automated away. But sustainable AI success still depends on disciplined engineering, governance and operational practices. The ongoing struggles with ROI, value realization and fragmented adoption reinforce this reality,” said Ipek Ozkaya, technical director of the SEI’s AI-Native Software Engineering directorate and leader of the model’s development. “In this environment, measurable and adaptive approaches to maturity matter more than ever.”
With assessments against the model, organizations can establish their baseline readiness to incorporate AI into workflows and tech ecosystems. The baseline enables organizations to identify use cases, institutionalize practices, focus on the value of investments and create a structured roadmap for adoption.
Deeply Researched, Industry Validated
When developing the AI Adoption Maturity Model, Ozkaya and her team leaned on the SEI’s history in software measurement and analysis, software architecture, cybersecurity, risk management and AI Engineering. The SEI’s expertise in organizational maturity modeling, gained through its creation of the pioneering Capability Maturity Model (CMM) and CMM Integration (CMMI), the influential CERT Resilience Maturity Model (CERT-RMM) and, more recently, the co-developed Cybersecurity Maturity Model Certification (CMMC), enabled the team to balance core elements of successful maturity modeling with the demands of AI adoption.
The SEI team, in collaboration with Accenture, also interviewed more than two dozen executives and surveyed nearly 600 practitioners. The developers reviewed more than 100 existing AI maturity efforts worldwide and deeply analyzed three dozen models. They tuned the new model to fill key gaps in the AI maturity landscape: the lack of measurable criteria, limited adaptability to rapid AI advances and inconsistent practice definitions. Finally, they piloted the model with several Fortune 500 organizations.
Bosch Global Software Technologies Private Limited (BGSW), a subsidiary of Robert Bosch GmbH and a leading global supplier of technology and services, performed one of the pilots. “The SEI AI adoption maturity assessment provided far more than a point-in-time evaluation—it gave us a structured, actionable understanding of where we are succeeding, where more attention may be needed and how to prioritize future investments for maximum ROI,” said Srinivasulu Nasam, BGSW’s head of Enterprise AI Transformation. “The process reinforced that our teams have been proactively integrating AI into engineering and operational practices with intention and measurable business value. While the assessment validated that we were progressing in the right direction, it also helped us create a baseline and calibrate our future roadmap for continuous improvement.”
The SEI’s empirically based development and real-world testing, informed by Accenture’s experience delivering thousands of advanced AI projects, resulted in a new maturity model fitted to today’s biggest technology advancements and flexible enough for the fast-changing AI future.
AI Presents Technology Adoption at a New Scale
“Successful AI adoption goes beyond improving automation or augmenting existing processes. It means rethinking workflows and innovating ways to bolster them with AI,” said Ozkaya. “Amid the pressure to innovate with AI, organizations must ask what AI should do for the enterprise, not only what AI can do.”
This innovation pressure spans all industries, including highly regulated ones such as health, automotive and defense. Government agencies especially need a rigorous approach, as signaled by the DoW’s recently announced critical technology area of Applied AI. The AI Adoption Maturity Model combines the technical depth, operational realism and security-conscious implementation guidance that support the unique needs of defense and industry organizations.
“The SEI’s maturity models have given industries a structured way to measure readiness, reduce risk and continuously improve,” said Anita Carleton, director of the SEI’s Software Solutions Division. “Today, as AI moves from experimentation into mission‑critical environments, organizations need similar clarity to understand where they are, where they need to go and how to get there responsibly. This AI Adoption Maturity Model reflects the SEI’s deep experience helping organizations adopt emerging technologies safely and effectively, and it incorporates the latest insights from our ongoing research in trustworthy, secure and engineering‑grade AI.”
Download the AI Adoption Maturity Model v1.0 from the SEI Digital Library. The SEI invites model users to share implementation insights. Join SEI and Accenture experts in the live webcast “Rethinking and Maturing AI Adoption,” June 9 at 1:30 p.m. EDT.
Learn more about the model in the SEI Blog post Managing the Complexities of AI Adoption and on the AI Adoption Maturity Model project site.
About the Carnegie Mellon University Software Engineering Institute
The Software Engineering Institute (SEI) advances software as a strategic advantage for national security through research, development, and deployment of tools, technologies, and practices in software engineering, artificial intelligence (AI), cyber and acquisition transformation. We serve the nation as a federally funded research and development center (FFRDC) sponsored by the U.S. Department of War (DoW). For more information, visit the SEI website at https://www.sei.cmu.edu.
SOURCE Carnegie Mellon University Software Engineering Institute
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