Technical Director, Engineering Intelligent Software Systems at Carnegie Mellon Software Engineering Institute
The race to deploy generative and agentic AI has generated extraordinary momentum—and an equally extraordinary amount of confusion. Organizations are counting on breakthrough gains, yet many are instead facing mounting technical debt, inconsistent outcomes, and AI initiatives that quietly stall or fail. Despite decades of progress in software engineering, AI adoption is moving faster than most organizations can reliably engineer, govern, or sustain any of their AI initiatives. This raises a critical question: Is today’s rush toward generative AI repeating the maturity challenges that once hampered early software engineering—and can disciplined, capability-driven approaches guide us out of the chaos again?
To tackle this challenge, the Carnegie Mellon Software Engineering Institute and Accenture have partnered to develop a new AI Adoption Maturity Model. Drawing on executive interviews, survey data, background studies, and pilot engagements, we identified the core engineering and organizational practices required for predictable, repeatable, and scalable AI adoption—capabilities essential to delivering AI-enabled systems and workflows that achieve measurable value.
Today’s challenge is no longer simply embracing AI; it is keeping pace with rapid technological change while strengthening the engineering practices and tools needed to build reliable, trustworthy, and maintainable AI-enabled systems and workflows—whether created by humans, AI, or anything in between. This keynote shares insights from the development of the AI Adoption Maturity Model, outlining a path to balance continuous evolution of practice with the foundational discipline needed to achieve trustworthy, enterprise-level AI impact.
Dr. Ipek Ozkaya is the Technical Director of the Engineering Intelligent Software Systems group at the Software Engineering Institute (SEI) at Carnegie Mellon University. Her work is at the intersection of technical debt management, AI-augmented software engineering, AI-enabled systems development, and large-scale modernization. She has spent much of her career advancing how we understand and reduce technical debt in systems, including co-authoring a practitioner book titled Managing Technical Debt: Reducing Friction in Software Development. Her current work focuses on shaping the techniques, practices, and tooling that enable AI-native software engineering and enterprise-wide AI adoption. At the SEI, she puts her research into practice by helping government and industry organizations to solve their software and AI challenges.
