How to Take Your AI Projects from Experiments to Engines

Across every industry, AI adoption is accelerating. Enthusiasm is incredibly high: according to the 2025 CDW AI Report, over 98% of organizations have initiated at least one AI project, and 48% have started three to five projects.

Yet, with all these pilots and proof-of-concepts floating around, only a fraction of organizations are able to successfully scale AI into production environments where it delivers measurable value.

Models end up living in slide decks instead of applications, data remains fragmented, governance stalls innovation and infrastructure buckles under real-world workloads.

The same report cites that nearly two-thirds of organizations have seen a 50% return or less on their AI initiatives. Many of these organizations agree they have good ideas for AI, but they have trouble executing. Why?

The answer is perhaps simpler and more complex than you think. It’s not a technology problem – it’s an operational one.

To move beyond isolated wins, enterprises need more than algorithms. They need a system for continuous creation, deployment and evolution. They need an AI Factory.

An AI Factory is not a single platform or product. It’s an operational model that standardizes how AI is built, deployed and managed across the enterprise.

Instead of one-off projects, AI becomes an assembly line of repeatable, governable processes powered by:

• Modernized data supply chains that are cleansed, catalogued and accessible in real time
• Industry-grade infrastructure that utilizes GPU-accelerated compute built on platforms like NVIDIA
• Shared development frameworks and orchestration tools that enable reuse instead of reinvention
• Deployment pipelines that span edge, data center and cloud environments
• Continuous monitoring and governance to uphold security, compliance and performance

With this foundation in place, AI stops being a series of disconnected efforts and becomes a capability the business can rely on.

Many organizations discover the same hurdle: building a model is easy. Operationalizing it is hard.

Successful AI initiatives require coordination across data engineering, infrastructure, application development, cybersecurity and line-of-business stakeholders.

Without clear ownership models and repeatable pipelines, even the most promising use cases stall in the “last mile” – where models need to be connected to real systems, users and processes.

The AI Factory approach aligns these functions into one cohesive lifecycle. It answers critical questions upfront such as:

• How will models be deployed (APIs, edge devices, applications, chat interfaces)?
• How will usage be governed and audited?
• How will performance be monitored and improved over time?
• How will new workloads scale without re-architecting each time?

Consider three common use case patterns that AI engines enable:

• Intelligent resource allocation – Using video-based analytics to detect asset usage and optimize deployment
• Automated inventory or asset tracking – Applying computer vision to replace manual reconciliation
• GPU orchestration across departments – Ensuring high-value computer resources are fairly scheduled and efficiently utilized

Each of these use cases requires not just a model, but a system to deploy, run and scale that model.

AI transformation doesn’t happen when just a single use case succeeds, either. It happens when every use case gets easier to deliver.

That’s the promise of an AI factory: faster iteration, higher reliability and a governed path from idea to production.

Enterprises that can embrace this model will outpace their competitors, who will stay stuck in pilot purgatory, rebuilding the same foundation from scratch, model after model.

Most organizations already have the ingredients for AI success. The only thing they need help executing is orchestration. CDW’s AI Factory team helps enterprises design and assemble the full operational stack required to move from experimentation to execution.

From NVIDIA-powered infrastructure and workload orchestration to data governance, deployment frameworks and last-mile integration, we bring the strategy and engineering needed to turn AI into a repeatable capability, not just a one-off project.