Software Bills of Materials (SBOMs) and Artificial Intelligence Bills of Materials (AIBOMs) are promoted worldwide to manage the supply chain of software and Artificial Intelligence (AI) systems by providing machine-readable inventories of software and AI components. Yet, knowledge of how practitioners use SBOMs and AIBOMs—along with the challenges they encounter and the support they require—has been limited. This post-doctoral paper reports results from my PhD dissertation, which aimed to investigate the state of practice of SBOM and AIBOM and improve their tooling. The findings indicate low but growing awareness and adoption of SBOM and AIBOM, alongside substantial technical limitations in existing generators. To address these limitations, two software composition analysis tools were developed and evaluated: AIRBORNE, which enriches SBOMs by linking source code to reused Stack Overflow content, and ALOHA, which generates AIBOMs by mining information from Hugging Face. These findings provide implications for practitioners, who should integrate automated SBOM and AIBOM generation into development pipelines and procurement processes to meet standards and support risk monitoring. Future research should further improve both the generation and consumption of SBOM and AIBOM to better support their intended use cases.