AI Landing Zone

Most enterprises are investing heavily in generative AI and machine learning, but few are translating that investment into production-grade business outcomes. Across industries we see a recurring pattern: AI initiatives stall not because of the models themselves, but because of the foundation they sit on. Security, compliance and ethics teams hold back deployments, data is difficult to connect safely, costs spiral as workloads scale, and engineering teams spend months building “plumbing” rather than delivering value. The result is slow adoption, duplicated effort, unsafe experimentation in ad-hoc environments, and a loss of executive confidence in AI as a strategic capability. The specific blockers fall into six recurring themes: ▪ Security and compliance alignment — meeting regulatory and data-handling obligations whilst adopting fast-moving AI services introduces significant risk and slows time-to-deployment. ▪ Data integration and connectivity — enterprise data sits across on-premise systems and multiple clouds; safely connecting it to AI services without compromising governance is complex and costly. ▪ Cost visibility and FinOps — AI workloads are GPU- and inference-intensive; without robust FinOps practices, organisations cannot forecast, allocate or optimise spend, and production economics quickly become unsustainable. ▪ Platform abstraction for developers — long provisioning cycles and bespoke infrastructure setup delay innovation; teams need self-service environments that hide cloud complexity. ▪ Productionising AI workloads — the path from prototype to a reliable, monitored, lifecycle-managed production environment exposes gaps in MLOps maturity, CI/CD integration and governance. ▪ Governance, ethics and guardrails — risk and ethics committees increasingly block deployments where guardrails, audit trails and explainability are not demonstrably in place.

How Computacenter Delivers Computacenter brings together cloud platform engineering, AI/ML specialism, security and FinOps under a single delivery team. The accelerator is not a slideware concept — it is a working, opinionated reference architecture, packaged as Infrastructure-as-Code (Terraform) and deployable directly into customer cloud environments. It is built on the same composable patterns we already use to deliver enterprise landing zones at scale across AWS and Azure. What our skilled resources bring ▪ Cloud platform engineers with deep AWS experience, applying GitOps, policy-as-code and ephemeral environment patterns. ▪ AI/ML specialists configuring Amazon Bedrock, SageMaker, vector data services, RAG patterns and agentic workflows under enterprise guardrails. ▪ Security architects embedding identity, networking, data protection, audit and Bedrock Guardrails as first-class concerns from day one. ▪ FinOps practitioners aligning AI spend to business value through right-sizing, model usage tracking and governed consumption controls.

Greenfield or brownfield — we meet customers where they are Where AI investment is held back by an existing landing zone, AILZ is delivered as a brownfield augmentation — reusing established identity, networking and governance whilst adding the AI-specific extensions needed for production workloads. Where speed matters more than reuse, a greenfield deployment provides a clean, opinionated foundation with the latest GPU SKUs, managed AI services and a modern platform-engineering baseline.

The outcomes we commit to ▪ A secure, production-ready AI environment deployable into the customer’s cloud accounts via Terraform, with private networking, encryption and centralised audit. ▪ Demonstrable guardrails across content, topic, PII and model usage — making AI risk controls explicit and auditable to security, risk and ethics committees. ▪ Golden-path reference use cases (LLM playground, retrieval-augmented generation, safe agentic workflows, ML pipelines) that move teams from concept to production in weeks, not months. ▪ FinOps-aligned cost visibility and optimisation that brings AI spend back into business-value conversations. ▪ A platform abstracted from the developer — self-service environments that let engineering teams build AI capability rather than infrastructure