Accelerating cloud-enabled AI outcomes
Build the cloud infrastructure your AI solutions need to move from pilot to production.
Overview
AI tools are everywhere. Production-ready AI infrastructure is not.
Most organizations have adopted AI tools across their engineering and business teams. Developers are using coding assistants to refactor legacy applications. Business units are deploying AI-powered productivity platforms. The adoption curve is real.
But adoption is only the beginning. Those same tools are surfacing infrastructure gaps that were previously invisible: ungoverned data estates, GPU environments that collapse under production load, compliance blockers baked into pipeline architecture and agent pilots that never survive past the lab.
WWT's Cloud AI practice bridges those gaps. We work across all four major cloud hyperscalers to build the governed foundations, production compute and agentic infrastructure that turn AI experiments into measurable business outcomes.
$2.6 trillion
in worldwide AI spend in 2026, up 47% YoY (Gartner 2026)
88%
of organizations now use AI, but only 7% have fully scaled it (McKinsey 2025)
16-30%
faster timelines when AI-assisted tools are applied (McKinsey 2025)
40%
of enterprise apps will include task-specific AI agents by the end of 2026, up from less than 5% in 2025 (Gartner 2025)
Benefits of partnering with WWT
Faster path from AI pilot to production deployment
Governed data estates that satisfy compliance teams on day one
GPU and inference infrastructure right-sized for actual workloads
Reduced cloud waste through FinOps integration from the start
Agent pipelines built with production-grade security and observability
Why WWT
The only partner built for every cloud AI conversation
Most partners specialize in a single cloud. Most hyperscaler services teams serve their own platform. WWT is different. Premiere with AWS and GCP and top level partnerships with Microsoft and Oracle, giving us the ability to recommend and build on the platform that fits your data, compliance requirements and existing investments.
That neutrality is backed by infrastructure no other partner can match: an AI Proving Ground for physical validation of on-premises and hybrid GPU deployments, our ARMOR security framework mapped to NIST AI RMF and a delivery model where you own the architecture, the code and the runbooks when we're done.
Cross-cloud expertise
Premiere with AWS and GCP and top level partnerships with Microsoft and Oracle. We architect for where your data lives, not where our partnerships point.
The AI Proving Ground
A physical lab environment for validating GPU configurations, inference performance and edge AI deployments before you commit to production hardware.
ARMOR security framework
A security baseline mapped to NIST AI RMF that governs every AI workload we build. GPU node hardening, model endpoint isolation, data perimeter controls and compliance guardrails are built in from day one.
You own what we build
Every engagement delivers production-ready infrastructure as code, operational runbooks and architecture documentation. No vendor lock-in. No dependency on WWT to keep the lights on.
What you get
Comprehensive cloud-based AI solutions and expertise — all under one roof
Four integrated solutions from WWT take your AI program from governed foundations to production at scale. Each works independently. Together, they compound.
Most AI pilots look great in the lab and fall apart in production. Our model is built to prevent that: We prove every solution under real production load in the AI Proving Ground before you scale, and you walk away owning the architecture, code and runbooks, not a dependency on us.
Workforce AI
AI-powered workforce productivity
Empower every employee with AI that works the way your business does.
GenAI in the cloud
For product innovation and data platforms
Compress time to market. Personalize at scale. Govern everything.
Hybrid AI Infrastructure
Edge-to-cloud intelligence
Combining on-prem systems, hosted GPU platforms and cloud services to run AI workloads where performance, cost and data requirements best align.
Agentic AI Infrastructure
AI agents at scale
MCP servers, agent gateways, A2A protocol, orchestration frameworks — all optimized for scale and enterprise success.
Workforce AI
Solution 1: Workforce AI
Your teams spend enormous portions of their week on tasks AI can handle: drafting communications, synthesizing meeting notes, pulling data for reports and answering repetitive internal questions.
WWT deploys enterprise AI assistants embedded in the productivity tools your teams already use, grounded in your proprietary data and governed by your security policies. We build custom AI agents that automate multi-step workflows and a governed data foundation so every AI interaction is based on accurate, permissioned enterprise data.
How we engage
AI Readiness Assessment (2-3 weeks): WWT evaluates your environment, data estate and workforce use cases. Delivers a costed, prioritized build plan.
Pilot deployment (100-200 users): Configured and measured across 2-3 high-impact personas. You own the results before committing to scale.
Enterprise scale: Governed data foundation, custom agents and full workforce operationalization.
GenAI in the cloud
Solution 2: GenAI for product innovation & data platforms
Product development cycles that take months. Marketing campaigns built by hand, one market at a time. Proprietary customer data sitting in warehouses that AI models can't reach. Sound familiar?
WWT engineers a governed, AI-ready data estate on your existing cloud: unified data lakehouses, streaming ingestion pipelines, vector stores for RAG, and lineage controls your legal team can audit. On that foundation, we build custom GenAI agents for product concept generation, competitive analysis and AI-powered marketing personalization, all measured in real time and continuously improved by your customer data.
How we engage
Data estate assessment: WWT evaluates governance gaps, throughput, classification and RAG readiness. Delivers a data platform build plan.
AI data platform build: Governed lakehouse, ingestion pipelines and vector stores with ARMOR security and FinOps cost controls applied.
GenAI agent deployment: Custom innovation and marketing agents built, architecture-reviewed and deployed to production within 90 days.
Hybrid AI Infrastructure
Solution 3: Hybrid edge-to-cloud intelligence
Hybrid AI infrastructure combines on-premises infrastructure, private-hosted environments, AI as a Service (AIaaS) and GPU as a Service (GPUaaS) to support the full lifecycle of AI workloads. Through careful planning across infrastructure, data and operations, we'll design the optimal hybrid architecture for your workloads.
We're also experts at bringing edge-to-cloud intelligence to life. We pair edge AI inferences, running locally at the point of operation, with cloud-based data platforms that aggregate signals from every edge node, train improved models and push updates. Unified management and observability across thousands of distributed devices, validated in WWT's ATC before fleet deployment.
And because agent sprawl is where cloud consumption quietly compounds, we instrument every agent and gateway for cost attribution, so you can trace spend to the agent, team or workflow driving it and set guardrails before the bill surprises you.
How we engage
Hybrid AI Readiness Assessment: Edge hardware inventory, connectivity, cloud data platform and latency requirements. Delivers a validated deployment model.
ATC proof of concept: WWT simulates your operational environment. Edge AI, cloud connectivity and unified management proven under real load before fleet commitment.
Fleet deployment + TCO model: Full deployment roadmap, phased rollout plan and a FinOps-backed TCO model comparing edge infrastructure against pure cloud alternatives.
Agentic AI Infrastructure
Solution 4: Agentic AI Infrastructure
A single agent calling a single API in a notebook is easy. Running dozens of agents in production is a different problem. Once agents start invoking tools, calling each other and reaching into sensitive systems, the hard questions surface: How do you govern what each agent can touch? How do you trace a decision across a chain of handoffs? How do you keep one misfiring agent from taking down the pipeline? This is where most agent pilots stall, and it is where a lot of the cloud consumption expansion is actually happening.
We build the infrastructure layer that lets agents run in production at scale. We stand up the MCP servers that give agents governed access to your tools and data, the agent gateways that enforce authentication, rate limits and policy on every call, and the orchestration frameworks and A2A messaging that coordinate work across agents. Every layer is instrumented for observability and secured under our ARMOR framework, mapped to the NIST AI RMF, so you can see exactly what your agents are doing and prove it to your security team.
How we engage
Agentic readiness assessment: WWT maps your existing agents, tools and data sources, identifies governance and security gaps and delivers an agent infrastructure build plan.
Reference architecture build: WWT stands up MCP servers, an agent gateway and orchestration framework with the ARMOR security baseline applied, validated in the AI Proving Ground before it reaches production.
Production scale: Full agent platform with observability, policy governance and A2A coordination operationalized across teams. You own the architecture, the code and the runbooks when we are done.
Data readiness
The critical role of data readiness
AI models are only as good as the data behind them. Yet most organizations struggle with fragmented systems, inconsistent quality and governance gaps that stall AI initiatives before they gain traction. Data scientists have long been estimated to spend 50% to 80% of their time collecting and preparing data rather than building models. That's not a technical inconvenience — it's a direct drag on time to value.
WWT takes a phased approach to data readiness that meets organizations wherever they are on the maturity curve. Whether you're consolidating siloed data across cloud environments, standing up governance frameworks or building the pipelines that feed your AI models, the goal is the same: turn data from a bottleneck into a competitive advantage.
Resources
Jumpstart your cloud AI journey
Cloud Priorities for 2026
Cloud, FinOps and AI: What You Need to Know About Unit Economics, GPUs and the ROI Flywheel
The Cloud Advantage for AI
AWS Generative AI