Why this matters:

AI models require data. If data is siloed, inaccessible, or non-compliant for AI use, initiatives can't progress.

Output:

Clear understanding of data readiness and recommendations for making data AI-accessible.

Why this matters:

Most organisations should use managed AI services (Azure OpenAI, AWS Bedrock) rather than building custom GPU infrastructure. We help you plan the right approach.

See our public cloud page for details on Azure OpenAI, AWS Bedrock, and other managed AI services.

Output:

Compute capacity plan and cloud AI service recommendations tailored to your AI initiatives.

Why this matters:

AI infrastructure can be expensive. CFOs need cost confidence before approving initiatives.

Output:

Cost model for AI infrastructure spend with budget planning guidance.

Assessment Phase:

• Data accessibility review
• Current infrastructure capacity assessment
• AI initiative requirements gathering
• Gap identification
• Readiness scoring

Planning Phase:

• Data strategy for AI (where data needs to be)
• Compute capacity planning (cloud AI services vs. custom)
• Cost modelling (forecast before commitment)
• Infrastructure recommendations
• Roadmap for infrastructure preparation

What you get:

• Clear understanding of current readiness
• Documented gaps and recommendations
• Cost models for AI infrastructure
• Plan for making infrastructure AI-ready
• Ongoing support as AI initiatives launch

What makes infrastructure "AI-ready"?

Three foundations:

Data accessibility:

AI models need data. Infrastructure must make data available where AI services can use it, while maintaining governance and compliance.

Compute capacity planning:

Understanding what compute you need (usually managed cloud AI services, rarely custom GPU infrastructure).

Cost understanding:

Knowing what AI infrastructure will cost before committing budget.

Synapse assesses all three before AI initiatives launch.

Do we need to build custom AI infrastructure?

Most organisations don't.

Use managed AI services (recommended for most):

• Azure OpenAI, Azure AI services
• AWS Bedrock, SageMaker
• Managed GPU compute when needed
• Fully managed, pay-per-use
• Latest models and capabilities

Build custom infrastructure (rarely needed):

• Only if extreme scale or unique requirements
• Significant capital investment required
• Specialised operational expertise needed

Synapse recommendation: Start with cloud AI services. Only consider custom infrastructure if cloud services prove insufficient for your specific needs.

Can we use public cloud for AI?

Yes. And it's the recommended starting point for most organisations.

See our public cloud page for details on Azure and AWS AI services.

Advantages:

• Access to latest AI models and services (GPT-4, Claude, etc.)
• Pay-per-use pricing
• Managed infrastructure (no GPU management)
• Global scale available
• Regular service updates

Considerations:

• Data sovereignty requirements (UK regions available)
• Cost management at scale (need monitoring)
• Vendor relationships

We help you use public cloud AI effectively while managing costs and sovereignty requirements.

Storage considerations:

AI workloads require high-throughput storage for training data and low-latency storage for inference. We help you plan storage for AI across cloud and on-premise.

What about data sovereignty for AI?

Critical consideration that affects AI operations.

The challenge:

AI models train on data. If data must stay in UK for sovereignty, where can AI operate?

Options:

1. UK Cloud Regions: Azure OpenAI and AWS services available in UK regions
2. Data Residency Controls: Keep training data in UK while using UK-region AI services
3. On-Premise Inference: Train in cloud, deploy inference on-premise if needed

We help navigate sovereignty requirements for AI workloads as part of Sovereign Infrastructure considerations.

How much does AI infrastructure cost?

Highly variable based on:

• AI service used (OpenAI, custom models, etc.)
• Usage volume (API calls, tokens processed)
• Training requirements (fine-tuning vs. using pre-trained)
• Infrastructure choice (managed services vs. custom)

Example costs (managed services):

• Small-scale API usage: £500-2,000/month
• Medium-scale with fine-tuning: £5,000-20,000/month
• Large-scale production AI: £50,000+/month

We model costs before commitment using actual pricing and your expected usage.

How does this integrate with Adaptive Cloud?

AI Infrastructure Readiness is one of four outcomes Adaptive Cloud manages:

1. Recovery Confidence
2. Cost Control
3. Sovereign Infrastructure
4. AI Infrastructure Readiness (you're here)

AI readiness builds on other outcomes:

• Cost Control: AI can be expensive. cost management essential before scaling
• Recovery: AI models and training data need protection
• Sovereignty: Data location affects AI operations

Adaptive Cloud provides the foundation for AI initiatives through managed infrastructure.