Miles Jamison 
- Accenture and Carnegie Mellon SEI have launched a framework to help organizations scale AI adoption
- The AI Adoption Maturity Model will measure readiness across strategy, workforce, governance, data and operations
- The effort is intended address limitations in existing AI maturity models
Accenture and Carnegie Mellon University’s Software Engineering Institute, or SEI, have introduced the AI Adoption Maturity Model to help commercial and government organizations scale their artificial intelligence initiatives.
What Is the AI Adoption Maturity Model?
The framework assesses an organization’s capacity to adopt and maintain AI practices that support organizational change and AI lifecycle engineering objectives, Accenture said Monday. It evaluates maturity in eight areas, including organizational strategy, workforce and culture, workflow re-engineering, risk and governance, data, engineering, operations and ecosystem. The model also includes an assessment tool that helps organizations establish a baseline, identify capability gaps and develop a roadmap for AI adoption.
Why Did Accenture and SEI Develop the Framework?
The organizations said the model was developed to address shortcomings in existing AI maturity frameworks, many of which lack an engineering foundation or real-world validation. Development included a review of more than 100 AI maturity efforts, interviews with executives, surveys of nearly 600 practitioners and pilot programs with Fortune 500 organizations. The resulting framework draws on SEI’s four decades of maturity-modeling experience and Accenture’s work delivering more than 11,000 advanced AI projects worldwide.
The announcement comes as Accenture continues to expand its AI adoption efforts. The company previously partnered with Anthropic to establish the Accenture Anthropic Business Group, which aims to help organizations transition AI initiatives from pilot programs to enterprise-scale deployment. Accenture Federal Services also partnered with OpenAI to help federal agencies move AI adoption initiatives from experimentation to production environments.
