AI Infrastructure Checklist

Is Your AI Infrastructure Ready for What Comes Next?

A quick readiness checklist for enterprise AI environments.

AI infrastructure is more than GPUs.

AI demand is putting pressure on infrastructure plans that were designed for more centralized, predictable workloads.

Compute may be available in one location. Data may live somewhere else. Storage, cloud, and security services may span multiple providers. Inference workloads may need to run closer to users, applications, or data sources. The network becomes the layer that makes those choices workable.

This checklist helps infrastructure, cloud, storage, and data center teams identify where their current environment is ready to scale for AI, where it needs attention, and which questions should be answered before AI workloads move closer to production.

How to use this checklist

For each question, select the answer that best describes your current environment.

Scoring

0 Not yet planned, unclear, or handled manually.
1 Partially planned or supported in limited environments.
2 Defined, repeatable, and ready to support production AI requirements.

Maximum score: 20 points

AI infrastructure readiness checks

Where will AI workloads actually run?

AI workloads may not run where you first expected. GPU availability, power capacity, latency, cost, security, and data gravity can all influence where training, tuning, and inference workloads are deployed.

Which best describes your business?

0 pts Workload locations have not been defined. 1 pts Workload locations are planned for one primary environment, but alternatives are not defined. 2 pts Workload placement is mapped across cloud, data center, storage, and compute locations, with clear criteria for each workload type.

Please select an option

What to look for

A clear view of where AI workloads will run today, where they may need to move later, and what connectivity is required between those environments.

Where does the data live?

AI infrastructure depends on access to the right data, in the right location, with the right controls. If data is spread across clouds, data centers, SaaS platforms, object storage, and backup environments, access patterns need to be understood before workloads scale.

Which best describes your business?

0 pts Data locations and access requirements are not fully documented. 1 pts Key data locations are known, but access, movement, or governance requirements are incomplete. 2 pts Data sources, storage locations, access paths, and governance requirements are documented for AI use cases.

Please select an option

What to look for

A data map that shows where training, inference, operational, and sensitive data resides, and how AI workloads will access it.

How will data move between environments?

AI workloads can create new traffic patterns between storage, compute, cloud, and data center environments. Moving large volumes of data over the wrong path can lead to performance, cost, security, and reliability issues.

Which best describes your business?

0 pts Data movement is expected to use existing paths without further assessment. 1 pts Some data movement paths are defined, but capacity, cost, or performance impact is not fully modeled. 2 pts Data movement paths are designed for AI traffic patterns, with capacity, security, and operational requirements considered.

Please select an option

What to look for

Defined paths for moving data between storage, compute, cloud, and data center environments without relying on ad hoc routing or manual workarounds.

How are cloud, data center, storage, and GPU environments connected?

AI infrastructure often spans multiple environments. If those environments are connected through fragmented, inconsistent, or public internet-based paths, production readiness becomes harder to manage.

Which best describes your business?

0 pts Connectivity between environments is inconsistent or not yet designed. 1 pts Connectivity exists for some environments, but designs vary by team, provider, or location. 2 pts Cloud, data center, storage, and compute environments are connected through a defined architecture with clear ownership and repeatable deployment patterns.

Please select an option

What to look for

A connectivity model that supports distributed infrastructure without forcing every workload through one location, provider, or manual provisioning process.

Do you have enough bandwidth and predictable performance?

AI workloads can increase east-west traffic, data replication, model distribution, and inference-related traffic. Bandwidth planning needs to account for current and future requirements.

Which best describes your business?

0 pts Bandwidth requirements are unknown or based on existing network assumptions. 1 pts Bandwidth estimates exist, but scaling, burst capacity, or performance predictability is not fully addressed. 2 pts Bandwidth, latency, performance, and scaling requirements are defined for priority AI workloads.

Please select an option

What to look for

Bandwidth, latency, and burst capacity requirements modeled against real AI traffic patterns, with headroom to scale as data volumes and inference demand grow.

What happens if something fails?

AI infrastructure should not depend on a single connection, provider, cloud region, site, or operational process. Resilience needs to be designed into the environment before AI workloads become business-critical.

Which best describes your business?

0 pts Failover paths are not defined or have not been tested. 1 pts Some failover options exist, but they are not consistent across all critical AI environments. 2 pts Resilience, failover, and recovery paths are documented, tested, and aligned to workload criticality.

Please select an option

What to look for

Documented failure scenarios across compute, network, storage, and cloud dependencies, with clear recovery expectations.

Can you scale without redesigning the architecture?

AI infrastructure requirements can change quickly as models, data volumes, users, and regions change. The environment should be able to scale without relying on long procurement cycles or major redesigns.

Which best describes your business?

0 pts Scaling requires manual provisioning, long lead times, or architecture changes. 1 pts Some parts of the environment can scale, but processes vary across locations or providers. 2 pts Capacity, connectivity, and environment changes can be deployed through repeatable, on-demand processes.

Please select an option

What to look for

A way to add or change capacity without turning every AI expansion into a bespoke infrastructure project.

Can workloads move between providers or locations?

AI teams may need to shift workloads based on cost, performance, availability, security, or capacity. If the architecture assumes one provider or one location will always be available, flexibility may be limited.

Which best describes your business?

0 pts Workloads are tightly coupled to one provider, location, or platform. 1 pts Some portability exists, but connectivity, data access, or operational processes limit movement. 2 pts Our environment supports workload placement across multiple providers or locations, with connectivity and data access planned accordingly.

Please select an option

What to look for

Provider and location flexibility that gives teams options without creating major networking, security, or data movement issues.

Are security and exposure risks understood?

AI changes how teams need to approach infrastructure. More distributed environments can increase the number of connections, access paths, identities, and public exposure points that need to be managed.

Which best describes your business?

0 pts Security requirements for AI infrastructure have not been assessed. 1 pts Security controls exist, but AI-specific access, traffic, data, or exposure risks are still being defined. 2 pts Security, segmentation, access, and exposure controls are mapped to AI workload and data requirements.

Please select an option

What to look for

A design that considers private connectivity, segmentation, identity, access control, data sensitivity, and operational visibility.

Are you locking yourself into one infrastructure model?

AI requirements are still changing. Infrastructure choices made today should not prevent the business from using different clouds, GPU providers, data center locations, or storage models later.

Which best describes your business?

0 pts The current design depends on one primary cloud, platform, or infrastructure model. 1 pts Alternative providers or locations are possible, but would require significant redesign or manual migration work. 2 pts The architecture is designed to support provider choice, distributed deployment, and future workload movement.

Please select an option

What to look for

An infrastructure strategy that keeps cloud, compute, network, and storage decisions flexible as AI requirements evolve.

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Is Your AI Infrastructure Ready for What Comes Next?

AI infrastructure is more than GPUs.

How to use this checklist

AI infrastructure readiness checks