AI Inferencing Without the Infrastructure Overhaul

For many enterprise organizations today, the easiest part of the artificial intelligence (AI) puzzle is identifying the opportunity. The hard part is infrastructure: finding a path to production that does not require specialized hardware, lengthy GPU procurement cycles, or data center facilities your team has never seen before. 

The NetApp AIPod Mini with Intel was built with these challenges in mind. It is designed to run on modern, right-sized server infrastructure rather than the 5- to 7-year-old hardware still running in many data centers. It keeps sensitive data on-premises and scales with the organization as AI demand grows.

"We have our use cases. We got the data and the team. What we don't have is a path to production that doesn't require us to rearchitect our data center, wait eight months for GPUs, or spend our way into a cloud dependency we're going to regret." 

This tension, a clear business case with no clear infrastructure path, is a common AI story among enterprise organizations today. The ambition is real. So are the roadblocks. 

The research backs this up. According to McKinsey's 2025 State of AI survey, the share of organizations using generative AI in at least one business function more than doubled between 2023 and 2024, from 33% to 71%. The global AI inference market, the segment focused on serving real users in production, not training models in research labs, is projected to reach $255 billion by 2030, growing at a compound annual rate of nearly 19%, according to MarketsandMarkets. 

Demand is not the constraint. The constraint is the infrastructure—specifically, where right-sizing it lands on the strategic priority list. Most enterprise organizations have not made GPU infrastructure a strategic priority—and that is a deliberate choice. They don't have liquid-cooled data centers, dedicated AI operations teams, or the capital budget to acquire and maintain GPU clusters. What they have is existing server infrastructure, years of accumulated institutional knowledge locked in internal documents and databases, and a boardroom full of people asking why AI is not deployed yet. 

This is precisely the problem NetApp and Intel solved for: they built a validated reference architecture that delivers AI inferencing for use on right-sized server infrastructure organizations can deploy today, with no special power, no liquid cooling, and no GPU procurement required. WWT's role is that of a solutions integrator, advisor, and validator. We help our clients determine whether a solution fits their environments, and work alongside them in deployment phases.

Is This the Right Solution for You?

The NetApp AIPod Mini with Intel is purpose-built for organizations that want to make their internal, proprietary knowledge accessible through AI, securely, on-premises, without GPU infrastructure. It is not designed for model training, large-scale video or image processing, or workloads requiring the largest frontier models.

In business terms: if the goal is to give employees faster, smarter access to information the organization already has (such as policy documents, technical manuals, case histories, and research archives), this is the right conversation. If the goal is to build and train a custom AI model from scratch, that is a different infrastructure conversation, and WWT can help navigate that one, too. 

Fit Assessment

The criteria below are the same framework WWT's advisory team uses at the start of an engagement to determine whether this solution belongs in the conversation.

Strong fit indicators:

• Your primary AI use case is knowledge retrieval or Q&A on internal, proprietary documents
• You need to keep sensitive data on-premises and fully air-gapped from public cloud environments
• GPU procurement timelines or costs are slowing your AI roadmap
• You want to deploy at department scale first and expand gradually without re-architecting later, without a technology replacement cycle (compute and storage scale independently, so growth is incremental, not a heavy-lift overhaul
• You are working with smaller, focused language models rather than the largest frontier models
• Your data center operates on standard air cooling and power, with no specialized infrastructure required
• Power efficiency is an active advantage, not just a default: the built-in AI acceleration inside 5th and 6th Gen Intel Xeon PCOR processors delivers up to 100kW in power efficiency gains compared to GPU-based alternatives
• Your IT team already manages standard server infrastructure, and you want a familiar operational model

Equally important is knowing if the solution is not the right fit:

• Your workload requires large-scale model training -- building and training AI models from scratch
• You are processing compute-intensive video, image recognition, simulation, or large-scale multi-modal workloads
• You need response times measured in milliseconds for very high volumes of simultaneous users
• You are already operating a GPU-based AI environment and optimizing within it
• Your use cases require the very largest publicly available AI models

WWT's advisory team works with organizations on both sides of this assessment. Some come in expecting a clear fit and discover that a different architecture is a better match for their workload. That outcome is just as valuable. The goal of the WWT engagement is the right solution for the client organization's environment. For organizations where the fit is clear, the next question is equally practical: what does this solution actually do, and where does it create value?

Use Cases: Where This Solution Creates Value Across the Business

Here is what a strong fit looks like in practice. The technology that makes it possible is called retrieval-augmented generation (RAG), and the business translation is straightforward: employees ask questions in plain language and get accurate, sourced answers drawn from your organization's own internal documents and data, not from the public internet, not from a general-purpose AI system's best guess.

For a chief financial officer, this means less time spent searching for information and more time acting on it. For a chief information officer, it means AI capability deployed on existing infrastructure, governed by existing security controls, managed by the existing IT team. For a chief risk officer or general counsel, it means proprietary data stays where it belongs.

Here is what that looks like across specific use cases.

Use Case: Human Resources and Compliance

HR organizations field the same questions repeatedly: what does the policy say about this, how does this benefit work, and what does the process require? Each of these interactions consumes staff time that could be redirected to higher-value work. A RAG-based assistant built on your actual HR documentation gives employees instant, accurate answers grounded in your real policies rather than a general AI system's approximation. The information is current because it draws from the documents your team actually maintains. HR staff shift from answering routine questions to addressing situations that genuinely require human judgment. The productivity gain accrues on both sides of that interaction.

Use Case: IT Operations and Help Desk

IT support organizations are, in many ways, the ideal first application for this technology. They already operate a structured knowledge base: troubleshooting guides, system documentation, past ticket histories, and vendor procedures. And the value of making that knowledge instantly accessible is easy to measure, in ticket deflection rates, mean time to resolution, and the reduced burden on senior technical staff for routine escalations. A technician or end user describes a problem in plain language. The system returns the relevant procedure, the related ticket history, and the correct escalation path. What previously required 20 minutes, and a senior engineer's attention becomes a self-service interaction. 

Use Case: Legal and Contract Management

Contract review, due diligence, precedent research, legal work is information-intensive and time-sensitive. RAG systems can automate the retrieval of relevant clauses, surface past agreements with similar terms, and accelerate the review cycle, while keeping every document within the organization's security perimeter. For legal teams navigating strict data confidentiality requirements, the on-premises, air-gapped architecture is not a technical feature. It is the foundation of the entire business case.

Use Case: Sales Enablement

Sales organizations lose time – and sometimes deals – to information gaps. A salesperson who cannot immediately surface the right case study, the precise product specification, or the current pricing guidance for a specific situation is working at a disadvantage. RAG applications close that gap in real time, drawing from internal repositories to put the right information in front of the right person at the right moment.

Use Case: Customer Experience and Service

Customer-facing teams carry the same information burden as internal support organizations, but the stakes are higher. When a service representative cannot immediately locate the right answer, the customer feels it. RAG systems built on product documentation, FAQ databases, and support histories give customer-facing teams instant access to accurate, current information -- reducing the time it takes to resolve an issue and the volume of escalations to senior staff. Response quality improves. Handle times drop. And customers interact with representatives who sound like they know the product, because the information they need is right in front of them.

Use Case: Manufacturing and Operations

Operations environments carry enormous bodies of institutional knowledge -- maintenance procedures, equipment histories, quality specifications, supplier records -- much of it accessible only to experienced personnel who have been in the job long enough to know where to look. AI inferencing on that knowledge base puts it within reach of any frontline worker with a question, independent of tenure or seniority. The experienced engineer's knowledge becomes a shared organizational resource.

Use Case: Research and Knowledge Management

In research-intensive settings, the volume and specificity of internal knowledge create a retrieval challenge that general search tools cannot adequately address. RAG systems can navigate large, heterogeneous knowledge repositories -- spanning documents, code, images, and in specialized domains such as life sciences, information as specific as therapeutic protein structures -- and return precise, contextually grounded answers. The organization's accumulated knowledge becomes an active, queryable resource rather than an archive that requires expertise to navigate.

Across all of these use cases, the common denominators are: the value lives in your internal data, the sensitivity of that data makes on-premises deployment a requirement, and the scale of deployment begins at the department level and expands from there. That profile maps directly to the architecture of this solution.

Next Steps: Start a Conversation with WWT

If these use cases map to your environment, the right next step is a conversation with WWT's advisory team rather than a purchasing decision. We help organizations pressure-test fit against their actual data structure, governance requirements, and infrastructure before any commitment is made