When AI Earns Its Keep: Inference at Scale in Production

Why inference is where AI delivers value

Training an AI model to predict failures or automate tasks is an important engineering milestone, but real business impact happens when those predictions trigger action. Inference is the operational layer that applies trained models to live workflows, turning analysis into measurable outcomes. HPE's Craig Partridge puts it plainly: "the true value of AI lies in inference." When inference is trusted and running at scale, organizations see the biggest returns on their AI investments.

Trust is the foundation

Trusted inferencing means users can rely on system outputs. That reliability matters for low-risk use cases like marketing copy or chatbots, and it is mission-critical for high-stakes scenarios such as surgical assistance or autonomous driving. Building trust starts with data quality: "Bad data in equals bad inferencing out," Partridge warns.

Poor data quality produces unreliable outputs, hallucinations, and content that clogs workflows and forces manual fact-checking. Christian Reichenbach highlights survey findings showing many organizations remain stuck in experimentation rather than operationalizing AI. When outcomes are unreliable, trust drops, productivity gains evaporate, and expected business value is not realized.

Conversely, when trust is engineered into inference systems, teams gain dependable copilots that improve speed and accuracy. A network operations team, for example, can gain a 24/7 member that provides faster, tailored recommendations for troubleshooting.

From model-centric to data-centric: the AI factory

The initial wave of AI focused heavily on model size and hiring data scientists. As organizations push pilots toward production, priorities shift to data engineering, architecture, and removing silos so data streams can be accessed and monetized. Reichenbach describes this as the rise of the "AI factory": an always-on production line where data flows through pipelines and feedback loops to produce continuous intelligence.

This evolution reframes strategic questions: how much of the model is proprietary, and how unique is your input data from customers, operations, or market signals? Answers to these questions shape platform strategy, operating models, engineering roles, and trust/security measures.

HPE's four-quadrant AI factory framework

Partridge outlines four quadrants that describe relationships between models and data:

• Run: Accessing external, pretrained models via APIs. Organizations don't own the model or the data and must focus on security, governance, and a center of excellence to guide usage.
• RAG (retrieval augmented generation): Combining external pretrained models with proprietary data to generate unique insights. Implementation centers on connecting data streams to inferencing capabilities for quick, integrated access.
• Riches: Training custom models on enterprise-resident data for differentiated insights. This requires scalable, energy-efficient environments and often high-performance systems.
• Regulate: Training custom models on external data with added emphasis on legal and regulatory compliance when handling sensitive, non-owned data.

These quadrants are not mutually exclusive. Organizations commonly operate across multiple quadrants, building models internally to inform products, while customers may consume intelligence via the "Run" quadrant without owning the underlying data or models.

IT must lead the scale-up

Moving from pilots to enterprise-wide AI highlights a core tension: solutions that work for a few use cases often fail when applied broadly. Partridge argues that IT teams are best positioned to scale AI because their core competence is implementing discipline and operating systems at scale.

The cloud migration story is a cautionary example: business units deployed cloud services independently when IT abstained, leading to fragmentation, redundant costs, and security gaps. The same risk exists for AI as 'shadow AI' proliferates—teams using models and tools without governance.

IT's role should be to bring structure rather than shut down experimentation. That means architecting data platforms with guardrails, governance frameworks, and accessible data streams to feed AI. Standardizing infrastructure, protecting data integrity, and preserving brand trust are essential while preserving the speed and flexibility AI demands.

Practical guidance for operationalizing inference

Success requires clarity about where to play across the quadrants: when to "Run" external models, when to apply RAG to enrich them, where to invest in proprietary "Riches," and when to "Regulate" sensitive data. Organizations that align technology ambition with governance, clear ownership, and value creation will lead.

Operationalizing inference at scale is a people, process, and technology challenge: build trust through data quality, design data-centric pipelines that support continuous intelligence, and empower IT to orchestrate and govern AI across the enterprise.

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