AI Proving Ground

The AI Proving Ground is designed for enterprise organizations at any stage of their AI journey, from early exploration to large‑scale production deployment. It supports a broad cross‑functional audience, including:

• Business leaders and executives who want to prioritize AI use cases tied to measurable business outcomes and ROI.

• Data scientists and AI practitioners evaluating large language models (LLMs), generative AI, NLP and computer vision models using real infrastructure and real data.

• IT and infrastructure leaders responsible for AI architecture, performance, security, power and cooling, and hybrid cloud design.

• Security, risk and compliance teams validating AI security tools, governance controls and responsible AI practices before production.

Unlike many AI labs, the AI Proving Ground is business‑outcome focused, helping teams align AI experimentation directly to operational and strategic goals rather than isolated technical demos.

AI delivers the greatest value when applied to complex, data‑intensive problems where speed, scale or human limitations constrain outcomes. Common business challenges include:

• Improving productivity and automation (e.g., coding assistants, digital agents, IT operations automation)

• Enhancing decision‑making with predictive analytics, intelligent search and real‑time insights

• Accelerating innovation and time to market, especially in R&D, healthcare, manufacturing and customer experience

• Reducing risk and cost by validating AI solutions before investing in production infrastructure

The AI Proving Ground helps by providing a secure, multi‑vendor environment where organizations can test, compare and validate AI solutions end‑to‑end—from data pipelines and models to infrastructure and security—before committing to production.

Organizations can engage the AI Proving Ground at any point in their AI journey, whether they are just getting started or already running pilots. Common starting points include:

• Identifying and prioritizing high‑value AI use cases aligned to business outcomes

• Evaluating and comparing AI hardware, infrastructure and AI/ML operations platforms before committing to production investments

• Hands‑on experimentation with AI models, reference architectures and pre‑built labs

• Guided workshops and proofs of concept (POCs) supported by WWT AI, data and infrastructure experts

• Assessing organizational readiness across data, infrastructure, security and operating models

By removing barriers such as hardware availability, skills gaps and architectural complexity, the AI Proving Ground enables organizations to move from idea to validated solution faster and with less risk.

In many engagements, organizations develop, test and validate AI solutions in the AI Proving Ground while production hardware is being procured, significantly accelerating time to deployment once infrastructure is ready.

The AI Proving Ground is purpose‑built for risk‑free AI testing and validation. Organizations can:

• Compare AI models, frameworks and platforms using real workloads and data

• Validate performance, scalability and reliability across different architectures

• Test security, governance and compliance controls in a safe environment

• Recreate production issues and validate fixes before deployment

This approach allows teams to prove feasibility, performance and business value without exposing production systems or customers to risk.

The AI Proving Ground enables side‑by‑side evaluation of AI models, platforms and infrastructure across a multi‑OEM ecosystem, including on‑prem, hybrid and cloud environments. Organizations can assess:

• LLMs, generative AI models and traditional ML models

• Infrastructure options such as GPUs, networking and storage

• MLOps tools, orchestration platforms and deployment frameworks

• Cost, performance, supportability and vendor interoperability

Because components can be swapped in and out easily, teams gain data‑driven insights into which combinations best meet their technical and business requirements.

Scaling AI demands repeatable architectures, operational discipline and governance. The AI Proving Ground supports this transition by helping organizations:

• Design production‑ready reference architectures validated in the lab

• Establish scalable AI foundations, including data pipelines, MLOps and security controls

• Plan infrastructure for growth, accounting for performance, power, cooling and cost

• Move from POCs to deployable solutions with confidence and speed

By validating the full AI stack upfront, organizations reduce rework and accelerate their path from experimentation to enterprise‑scale AI adoption.