Building the Right AI Foundation: Strategy Before Spend

Enterprise leaders are preparing for artificial intelligence (AI) and receiving larger budgets to support it, but they often struggle to allocate those resources where they will have the greatest impact. Too many resources are going toward initiatives like GPU clusters without first asking whether the organization is consuming AI through hosted models or hosting it in-house.

Identifying the right strategy and the infrastructure to support it depends on addressing these questions. With a clear strategy, strong orchestration and realistic alignment between ambition and capability, technology investments can effectively solve an organization’s most immediate challenges.

Many IT solutions providers rightly emphasize high-performance computing, advanced storage and cutting-edge networking. These capabilities are important, but they can create a false sense of readiness if organizations haven’t first clarified their AI strategy.

Too often, companies invest heavily in GPU clusters and other hardware before understanding whether they need to consume AI through hosted models or host it in-house. The result is an impressive infrastructure that doesn’t immediately solve business problems, leaving teams frustrated and budgets misaligned.

The real issue isn’t the technology itself; it’s the disconnect between spending and strategic priorities. Organizations that take the time to assess their needs and align investments with their AI goals avoid wasted resources and accelerate meaningful outcomes. Once an organization understands this, the next step is to define the AI infrastructure path that aligns with its goals.

Your AI infrastructure strategy should follow one of two distinct paths, each with radically different requirements:

Most enterprises today rely on APIs and hosted services such as Azure OpenAI, AWS Bedrock or Google Gemini to power their applications. In this model, infrastructure priorities shift away from raw compute toward enabling secure, scalable application development:

●  Application infrastructure: Web servers and databases to support AI-powered applications

● Vector databases: Databases optimized for AI workloads

● Security and monitoring: Track what data users access and which models they use

● Data pipeline infrastructure: Safely prepare and route data to external AI services

Consumption strategies minimize infrastructure overhead but require robust governance and integration maturity.

Some enterprises pursue model hosting for compliance, cost control or innovation. Hosting requires a fundamentally different infrastructure stack:

●       Accelerated computing: GPUs with the right performance for training or fine-tuning

●       High-performance storage: File and object storage optimized for parallel workloads

●       Container orchestration: Kubernetes platforms to deploy and manage AI microservices

●       Facility upgrades: Power and cooling infrastructure for high-density computing

This path is expensive and complex, but for organizations with the scale and expertise, it allows for more customization and control.

A common mistake: Defaulting to model hosting and investing in GPUs before mastering consumption. For most enterprises, consumption provides faster time-to-value and lower risk. Hosting should only follow once orchestration, governance and application maturity are already in place.

While organizations focus on compute power, they often overlook the orchestration layer, which plays a key role in successful AI implementations. The real bottleneck isn’t processing power, it’s the ability to seamlessly connect AI developers to the data they need, manage model registries and orchestrate deployments at scale.

AI models and microservices are increasingly deployed as containers, which means your AI strategy now requires container orchestration at scale. This isn’t just a technical detail; it reflects a fundamental shift in how infrastructure is provisioned, managed and scaled.

Consider this reality check:

●       Can your IT team provision resources for an AI application team in days instead of months?

●       Can you monitor what corporate data is being tokenized and sent to external AI endpoints?

●       Do you have approval processes for model updates that prevent corrupted files from breaking production systems?

If you answered no to any of these questions, buying more GPUs won’t solve your problems. You need platform engineering capabilities, container orchestration maturity and governance frameworks.

Strategy must extend all the way down to the physical foundation. Modern AI systems draw more power and generate more heat than traditional data center environments are designed to handle. Leaders should confirm that their facilities can support higher-density compute, including adequate power delivery, cooling and space, which are often the hidden blockers to deployment.

Assessing facility readiness alongside orchestration and governance helps make way for smooth implementation and prevents your strategy from stalling. By planning for power, cooling and space requirements early on, organizations can align their physical infrastructure with their AI ambitions.

The most effective AI infrastructure strategies deliver new capabilities without neglecting governance, security and operational efficiency. By considering up front whether to host models in-house or access them through providers, how to manage orchestration and how to align resources with your priorities, you can ensure AI initiatives make a meaningful and lasting impact in your organization. Treating these infrastructure decisions as strategic choices rather than technical details helps ensure your efforts scale effectively and create real value over time.

Successful AI infrastructure strategies often follow this path:

1. Define your AI consumption model: Determine whether you’re primarily consuming or hosting AI models.

2. Assess orchestration maturity: Evaluate container management and orchestration capabilities honestly.

3. Audit facility readiness: Ensure your data center can support your chosen AI path.

4. Implement governance frameworks: Establish monitoring and approval processes for AI workloads.

5. Plan iterative expansion: Scale infrastructure based on actual usage patterns, not projected maximums.

For organizations navigating this journey, CDW has supported hundreds of companies in transitioning from traditional IT infrastructure to AI-ready platforms. With experience across container orchestration, facility upgrades and strategic planning for AI model hosting, CDW helps ensure infrastructure investments are aligned with your priorities and positioned to accelerate your AI initiatives rather than get derailed by misaligned strategy or execution.