Developing a robust AI model portfolio—often visualized these days as a diverse ‘garden’ of different AI models, large and small—requires capability development, enthusiastic experimentation, thoughtful planning and strategic foresight. AI models not only have to be able to provide good answers, they must also be selected to be cost-effective, as measured by metrics like cost per kilo-inference, as well as technologically sound to integrate seamlessly into the broader IT infrastructure. Additionally, these models must align with strict corporate governance standards, including adherence to centralized IT, AI, and cybersecurity policies, safeguarding of personally identifiable information (PII), and compliance with regulatory requirements. This task involves a delicate balance of technical acumen and strategic management, ensuring each model performs efficiently and ethically within the corporate framework.
