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.

In October 2025, CyberPress reported a critical security vulnerability in the Redis Server, an open-source in-memory database that allowed authenticated attackers to achieve remote code execution through a use-after-free flaw in the Lua scripting engine. In 2024, another prominent temporal memory safety flaw was found in the Netfilter subsystem in the Linux kernel: CVE-2024-1086. Bugs related to temporal memory safety, such as use-after-free and double-free vulnerabilities, are challenging issues in C and C++ code. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Lori Flynn, a senior software security researcher in the SEI's CERT Division, and David Svoboda, a senior software engineer, also in CERT, sit down with Tim Chick, technical manager of CERT's Applied Systems Group, to discuss recent updates to the Pointer Ownership Model for C, a modeling framework designed to improve the ability of developers to statically analyze C programs for errors involving temporal memory.

On November 7, the Department of War released an acquisition transformation strategy that seeks to remove bureaucratic hurdles and streamline acquisition processes to enable even more rapid adoption of technologies, including artificial intelligence. Getting AI into the hands of warfighters requires disciplined AI Engineering. In this podcast from the Carnegie Mellon University Software Engineering Institute, Carol Smith, lead of human-centered research in the SEI's AI Division, and Brigid O'Hearn, the SEI's lead of software modernization policy for the Department of War, sit down with Eileen Wrubel, the SEI's technical director of Transforming Software Acquisition Policy and Practice, to discuss AI Engineering challenges and guidance in the defense acquisition space.