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The true potential of AI lies not just in building impressive models, but in deploying them effectively for real-world inference. Experts like Craig Partridge from HPE emphasize that the most significant return on AI investment comes from scaling trusted AI inference in production environments. Currently, many organizations are grappling with the transition from experimental projects to widespread implementation.

Successfully scaling AI inference requires a holistic approach encompassing trust, data-centric strategies, and strong IT leadership. Establishing trust hinges on reliable and high-quality data. The industry is witnessing a shift from a model-centric perspective to a data-centric one, fueling the emergence of the ‘AI factory’ concept – a continuous intelligence generation process powered by data pipelines. This shift necessitates careful consideration of data and model ownership.

HPE proposes a four-quadrant AI factory implication matrix encompassing various approaches: ‘Run’ (utilizing external models), ‘RAG’ (combining external models with proprietary data), ‘Riches’ (training custom models on internal data), and ‘Regulate’ (training custom models on external data, with a focus on regulatory compliance). IT departments play a vital role in scaling AI by leveraging their expertise in infrastructure standardization, data protection, and brand safeguarding, all while maintaining the agility required for AI applications. The key to success lies in aligning technological aspirations with robust governance and clear value creation, ultimately transforming AI from a pilot project into a fully operational system.