The advancement of artificial intelligence (AI) is often celebrated for its ability to train models that can predict machinery failures, marking a significant engineering milestone. However, the real transformation for businesses occurs when these predictions lead to actionable insights—when AI successfully identifies a malfunctioning machine. Craig Partridge, Senior Director of Digital Next Advisory at HPE, emphasizes that the true worth of AI lies in its inference capabilities. He describes inference as the operational layer that translates extensive training into practical applications within real-world workflows. Partridge notes, “Trusted AI inferencing at scale and in production” is where organizations can expect to see the most substantial returns on their AI investments.
Despite the promise of AI, many companies are still in the experimental phase. Christian Reichenbach, HPE’s Global Digital Advisor, points to survey findings indicating that while approximately 25% of organizations have operationalized AI, a significant number remain in the exploratory stage. To break through this barrier, Reichenbach advocates for a three-pronged strategy: establishing trust as a foundational principle, ensuring data-centric execution, and fostering IT leadership capable of scaling AI initiatives. Trust is crucial, especially in high-stakes environments—be it in automated surgical robots or self-driving vehicles. The quality of the data feeding these systems is paramount; poor data can lead to unreliable outputs, resulting in decreased trust and ineffective productivity.
As businesses pivot from model-centric to data-centric approaches, the importance of data engineering and architecture becomes increasingly clear. Reichenbach observes that organizations are now focused on dismantling data silos and accessing data streams to unlock value rapidly. This shift coincides with the emergence of the ‘AI factory’—a continuous production line where data flows through pipelines to generate real-time intelligence. HPE has developed an AI factory implication matrix to help clients navigate their AI strategies, highlighting four quadrants of implementation: Run, RAG (Retrieval Augmented Generation), Riches, and Regulate. Each quadrant offers distinct paths for leveraging AI, from using external models to creating proprietary insights. Partridge notes that most organizations, including HPE, operate across several quadrants to maximize their AI capabilities. As the IT sector takes the lead in scaling AI across various use cases, it’s crucial for organizations to engage fully in this transformative journey, ensuring that AI benefits are realized on a broader scale.
Source: Realizing value with AI inference at scale and in production via MIT Technology Review