Rethinking and Maturing AI Adoption

Many organizations are discovering, as they accelerate adoption of artificial intelligence (AI), that business and operational success with AI depends on far more than deploying AI models or experimenting with generative AI tools. Successful AI adoption occurs at the intersection of software engineering practices, the realities of system and enterprise architecture modernization, governance, cybersecurity, workforce readiness, workflow reengineering, operational integration, and enterprise strategy. Organizations must manage technological challenges that have intensified with AI adoption, including growing dependencies, vendor lock-in, and the imperative to innovate and scale quickly. Leaders must also adapt to new emerging realities, from the operational and financial demands of supporting multiple frontier models to the novel security and governance risks introduced by agentic AI approaches. Traditional approaches to technology transformation are no longer sufficient to thrive in this environment.

To address these emerging complexities and drive success, Carnegie Mellon University’s Software Engineering Institute (SEI) collaborated with Accenture to develop the AI Adoption Maturity Model—an evidence-backed, field-tested instrument that provides a structured, yet agile, pathway for scaling AI capabilities across enterprises to ensure value and return on investment (ROI). This approach is designed for today’s realities, including fast-paced technological change, limited time and resources, and the need for lightweight, actionable methods rather than burdensome documentation.

In this webcast, experts from the SEI and Accenture share technical insights and lessons learned from maturing AI adoption in complex environments. They will demonstrate how a nimble assessment instrument such as the road-tested AI Adoption Maturity Model fills critical gaps faced by organizations adopting AI.

What Will Attendees Learn?

• How AI maturity extends beyond isolated experimentation to encompass scalable, repeatable, measurable, and governed organizational capabilities.
• How common pitfalls and strengths that we observed in early adopter organizations during AI Adoption Maturity Assessments can influence AI adoption.
• How certain approaches to integrating existing risk management routines and security processes can support AI adoption.
• How an agile, lightweight maturity assessment approach can enable organizations to rapidly prioritize activities, align efforts, and make targeted progress.