Maturing AI Adoption: From Chaos to Consistency

While Stanford University found that AI investments, optimism, and accessibility are rising, a recent MIT report suggests that 95 percent of organizations are realizing no returns on their generative AI investments. Research from Accenture found that only 8 percent of companies are scaling AI at an enterprise level and embedding the technology into core business strategy to maximize value. Mismatched expectations, misaligned applications, and poorly executed or untested implementation practices—not the technology itself—often keep organizations from realizing immediate value from an AI investment. For AI to increase efficiency, productivity, and value while conserving resources and lowering overall costs, organizations need to shift their focus from hype-driven experimentation to foundational capabilities and practical, measurable outcomes. In our latest podcast from the Carnegie Mellon University Software Engineering Institute, Dr. Ipek Ozkaya, technical director of AI-Native Software Engineering, sits down with Matthew Butkovic, technical director of Risk and Resilience in the SEI's CERT Division, to discuss their work on an AI Adoption Maturity Model that organizations can use to create a roadmap for predictable AI adoption and realization of AI benefits.