How WWT is Accelerating AI Adoption Across Every Major Industry

The AI Proving Ground (AIPG) at World Wide Technology (WWT) is a learning environment, built for organizations that need to gather knowledge before they commit. 

What we consistently hear from customers is this: "AI is complex. We are not sure what products and frameworks are right for us, and I need a safe place to explore this."  

Clients have seen what happens when organizations move fast without the right guardrails.  Sensitive data gets exposed and production environments get disrupted, and a lot of money goes into the wrong architecture. The AI Proving Ground exists to prevent exactly that. 

We've moved well past generative AI and Retrieval Augmented Generation (RAG) pipelines as the only story. Organizations are now leveraging AI for AIOps, network operations and employee productivity and making better decisions from the data they already have. RAG is just one component. The real question is how to deploy AI across all of these dimensions securely and at the right cost. 

Here is a look at how that work breaks down across seven major industries. 

1. Financial services: the volume leader 

Financial services is the most active vertical in the AIPG by a wide margin. If you looked at WWT's largest customers, the majority would be global financial institutions, and that tracks with what we see in AIPG activity. These organizations are used to working with us, they move at scale, and they are holding nothing back when it comes to AI investment. 

The maturity of financial services AI has visibly evolved. Early AIPG engagements from this sector were focused on evaluating the base infrastructure for their AI center of excellence: which storage platforms and accelerators would underpin their AI workloads, and what networking framework would connect them. Global banks ran hardware validation and storage benchmarking, then moved on to GPU stress tests. They needed to get the foundation right before they could build anything on top of it. 

That foundation work has now evolved. The focus in financial services has now shifted decisively toward security. How do you secure AI systems at scale? How do you evaluate and govern large language models, including newer security-focused models, across a global organization? How do you deploy agentic AI safely in an environment where a misconfigured system can have regulatory and financial consequences? Those are the questions driving AIPG engagements now, alongside continued evaluation of the next generation of accelerators and inference infrastructure as the technology keeps moving. 

It is a constant evolution. Just as the IT bottleneck has cycled through CPU, memory, storage, and networking over the years, AI has its own version of that cycle, and financial services organizations are riding it faster than anyone. 

2. Healthcare and life sciences, from drug discovery to document ingestion 

Healthcare organizations are using the AIPG at both ends of the complexity spectrum. At one end are highly specialized drug discovery applications. At the other is foundational document processing. 

Global pharmaceutical companies have used the AIPG to develop drug discovery LLMs using NVIDIA's BioNeMo™ Blueprint, projects that sit at the leading edge of AI in life sciences. Others have evaluated high-performance storage and workload management platforms together in purpose-built sandboxes and compared enterprise storage solutions for AI workloads head-to-head. Beyond pure proof-of-concept work, WWT has also used the AIPG to enable the teams who will actually run these environments day to day. That means giving users hands-on access to prebuilt environments that mirror what their organization has deployed, so they can learn how to support and operationalize their AI workflows before they go live.  

On the enterprise side, large health systems have built custom RAG environments for PDF ingestion. It's a more straightforward use case on the surface, but in a healthcare context, the ability to reliably extract and reason over clinical documents has enormous implications for speed, accuracy and compliance. 

The common thread across all healthcare engagements: sensitivity. These organizations need secure, isolated environments where they can validate AI approaches before anything touches patient data or research pipelines. The AIPG provides exactly that. 

3. Energy and utilities: grid intelligence and agentic AI 

Energy and utilities companies are bringing AI to bear on some of the most complex physical infrastructure challenges, and the AIPG is where they are proving the technology before deploying it in the field. 

Here are some examples: 

A leading industrial technology company used AWS computer vision and visualization tools at the AIPG to build a solution for energy grid detection. It is one of the most operationally grounded AI use cases in the program.

A major investor-owned utility ran a rapid GenAI prototype at the AIPG, then returned to evaluate a cloud data platform for AI workloads.

One engagement focused specifically on AI agent security: evaluating how to safely deploy autonomous AI agents in an industrial context, where a misconfigured agent could have real physical consequences.

Another came to the AIPG specifically to evaluate AI-powered developer tooling for enterprise workflows.

Energy is early compared to financial services, but it is moving fast. Digitizing operations can give companies the agility they need to keep pace with shifting industry demands, but doing so requires navigating a complex technology landscape. Drawing on decades of experience supporting some of the world's largest energy companies and our unique blend of AI, IT and OT expertise, we put clients on a practical path to digital transformation.

4. Government: mission-critical AI under strict constraints 

Public sector organizations are testing AI for some of the most consequential use cases in the AIPG, and the requirements are unlike any other vertical: governance, security, auditability, and often offline or edge deployment. 

One of the most striking engagements built a real-time, bidirectional translation and summarization system for an allied ministry, enabling communication between allied forces across language barriers. It is AI deployed directly at the mission edge. 

Other engagements have included: hardware evaluation on enterprise GPU infrastructure for a federal law enforcement agency; AI integration testing for command intelligence management systems; autonomous vehicle perception validation on an advanced GPU cluster; video analytics solution validation for a municipal government; and a RAG AI proof of concept for social services case management at the city level. 

These engagements share a requirement that the private sector rarely faces at this level of intensity: every component must be justified and defensible, and the audit trail has to hold up. The AIPG gives these organizations the controlled environment they need to meet that bar. 

5. Technology, media and telecom: pushing hardware and platform limits 

Tech and media companies tend to bring the most demanding hardware evaluation requests to the AIPG, and they are also exploring some of the most novel data pipelines and platform configurations. 

A major professional social platform is evaluating the NVIDIA DGX Spark™. A digital advertising technology firm is testing the NVIDIA RTX Pro 4500. A large cable operator ran NVIDIA DPU testing for its OpenStack environment. These are hardware validation engagements at the cutting edge of AI infrastructure. 

One media broadcaster built a custom AI development environment that uses streaming video as a data source and integrates with NVIDIA Metropolis Blueprint for View Search and Summarization (VSS). It is one of the more creative data pipeline architectures in the AIPG portfolio, and it highlights the range of what is possible when AI development occurs in a purpose-built environment. 

A major telecommunications carrier's engagement stands out for a different reason: Evaluating Cisco Secure AI Factory with AI Defense. As telecom companies build AI-native network infrastructure, security becomes the critical design constraint. This carrier is putting the platform through its paces at the AIPG before deploying it in the live network.

6. Manufacturing and industrial: simulation, validation, and the AI factory 

Manufacturers and industrial firms are using the AIPG to validate AI-powered infrastructure and simulation environments, with requirements that range from converged infrastructure design validation to immersive digital twins. 

A global automaker ran a Cisco FlashStack AI CVD validation at the AIPG, a foundational step before committing to that architecture across its manufacturing environment. An industrial technology company built a custom NVIDIA Omniverse™ environment, using the AIPG as a development platform for immersive simulation. 

A global engineering and project services firm built a custom Charmed Kubernetes deployment on Dell AI Factory. It is a strong example of an industrial services organization building AI-native infrastructure rather than simply adopting off-the-shelf tooling.  

A major aerospace and defense contractor evaluated HPE Private Cloud AI in a sandbox before making broader commitments. 

Manufacturing has historically been slower than financial services to adopt enterprise AI, but AIPG activity suggests that gap is narrowing quickly, particularly in infrastructure validation and simulation. 

Our new WWT Industry Solutions Experience (WISE) was built to help customers understand and accelerate the options they have in the industrial verticals. This is a space to accelerate technology and apply it across industries in a vertical fashion.

7. Transportation, logistics and travel: operational AI at scale 

Transportation and logistics companies are using the AIPG to target operational efficiency and, in one case, to build something that has never existed before. 

A major US airline developed a custom GenAI "Network Engineer" application, using AI to address the operational complexity of managing one of the world's largest commercial route networks.  

A global freight brokerage is evaluating an open-source large language model for logistics reasoning, testing whether it can handle the inference demands of complex supply chain decisions. 

An entertainment and hospitality operator built an autonomous robotic gait optimization solution. It is probably the most visually striking project in the AIPG portfolio. AI-driven physical robotics in a live consumer environment, validated at the AIPG before going anywhere near a theme park floor. 

All three engagements point to the same insight: operational AI in high-complexity environments requires a proving ground. The cost of getting it wrong is too high to skip that step.

What the pattern tells us 

Across all seven verticals, a few things stand out. 

AI adoption has moved from strategy to execution. The organizations showing up at the AIPG have already made the strategic call. They are here to prove that specific architectures, models, and workflows perform at scale and within budget without giving up security. That is a fundamentally different conversation than where most organizations were two years ago. 

The use cases have broadened dramatically. GenAI and RAG pipelines were the opening act. Today's AIPG engagements span AIOps, network operations, employee productivity, edge AI, robotics, computer vision and security. Organizations are applying AI across every layer of their operations, and the infrastructure questions keep getting more complex. 

The ability to showcase technologies in an integrated way matters. Talking about the art of the possible is easy. Demonstrating it in a real environment, with the customer's actual constraints, using the vendors and platforms they are evaluating, is what moves deals forward and gives organizations the confidence to commit. The AIPG does that. 

WWT can also help customers move forward without a formal AIPG engagement. Sometimes the right answer is to bring in a partner for a specific capability. Sometimes the customer needs a guided conversation more than a lab environment. The experience and perspective WWT has built through the AIPG translates into better advice, regardless of what the engagement looks like. 

The AI Proving Ground has run more than 30 customer, partner, and internal projects in 2026 alone, across industries that look nothing alike, with use cases ranging from genomics to robotics to energy grid detection. The common thread is a commitment to proving before deploying. 

That commitment is what separates AI that works in a demo from AI that works in production.