From pilot to production: why AI needs next-generation data organization

As AI technologies continue to mature, businesses are moving from experimentation to real-world implementation. Organizations have advanced projects from small-scale pilots to full production deployments, using technologies like digital twins, AI agents, and multi-agent orchestration frameworks. These deployments vary widely in scope and scale, but they all depend on one common foundation: data.

As AI projects move from pilot to production, data architecture becomes a strategic decision. Where data resides, how quickly it can be accessed, and how securely it can be governed often determine whether AI delivers meaningful business value at scale.

With so much riding on these projects, organizations must look beyond the promise of the new tools and pay close attention to the underlying architecture. Hardware matters, but so does how data is stored and accessed as AI pilots scale, mature, and begin supporting critical business needs. Organizations must decide whether to deploy data and AI workloads in the public cloud, on-premises, or at the edge.

From pilots to production

The standard and maturity of generative AI technology have progressed since ChatGPT burst onto the scene in 2022. Since then, businesses across sectors have been experimenting and seeing results. For instance, close to 70% of organizations have used some form of AI and would be considered early adopters, according to KPMG's global tech report, with nearly three-quarters (74%) reporting that their AI use cases deliver meaningful business value.

Many organizations have launched successful production-ready AI systems. One example is energy tech company SLB, which launched an AI tool at the end of 2025 called Tela to automate processes and workflows for other energy companies.

Tela makes use of agentic AI for automation to transform workflows and deliver better results for businesses using SLB products. It uses an AI loop comprising 5 key tenets: observe, plan, generate, act, and learn.

“Technology like Tela marks a paradigm shift in how AI supports the energy industry, from subsurface to operations,” Rakesh Jaggi, president, digital and integration, at SLB, said in the company’s press release announcing the launch of Tela.

“Today, the industry faces a dual challenge: a leaner workforce and increased technical complexity, and Tela can address both. Tela doesn’t just automate tasks — it can understand goals, make decisions, and take action. It’s the convergence of 100 years of domain science and cutting-edge digital technology, amplifying human ingenuity and redefining how work gets done.”

Pegatron, a Taiwan-based electronics design and manufacturing company, is another example of an organization that has harnessed digital twins and virtual factory models to improve its production facilities. As a result, the company has reduced construction times by 40% and has slashed defect rates.

Finally, a scientific study published in December 2025 found numerous examples of AI agents in production across hundreds of organizations globally. The main reasons practitioners build and deploy AI agents are to increase productivity (80.3%) and reduce human hours (72.7%).

As with incorporating any new technology, however, adopting AI and making a success of it are two different things. Digging beneath the surface, Databricks’ research shows that the overwhelming majority of organizations (97%) reporting increasing return on investment (ROI) have unified data architectures. The implication is clear: fragmented data environments can limit AI performance, slow deployment, and reduce business impact.

AI data residency

When it comes to running AI workloads, much of the focus falls on compute infrastructure, but data organization, management, and residency are also essential to whether a project stands up or falls flat. McKinsey reports that up to 40% of potential impact is lost, partially due to fragmented systems and insufficient operating model design.

Depending on the nature of the deployment, AI demands huge volumes of up-to-date data for training, fine-tuning, and inference. Considerations extend to where the data is located and how easy it is for AI models to access sometimes petabytes of information at a time. This may demand moving the compute elements closer to where the data resides (such as in edge computing), and attempting unification across environments with shared metadata, common management policies, and strong data pipelines.

Public cloud environments are often well-suited to early-stage or experimental AI workloads. They let teams provision resources and test models quickly, and take advantage of managed services without committing to new hardware upfront. Hyperscalers may also offer certain software or orchestration advantages as part of a wider package. Finally, data residency is a major part of the equation, too, in the sense that public cloud-facing businesses will host their data close to cloud-centric AI workloads.

That model becomes less attractive as AI workloads become larger, more continuous, and more data-intensive. At that point, organizations must weigh rising data movement costs, stricter governance requirements, and the latency of moving large datasets between environments. For many production workloads, on-premises or edge deployments can offer greater control, lower latency, and more predictable economics.

Modern data infrastructure

Data platforms were initially designed and engineered for the world of big data, analytics, and real-time insights, but the foundations of that era aren't necessarily suited to the modern AI-first environments that many organizations are shifting toward. For sophisticated and multi-disciplinary AI projects to succeed, a company's data infrastructure must be built to handle massive amounts of data flowing up and down pipelines in real-time. It must also be able to handle agents perceiving, reasoning, and acting across different tools and applications, and orchestration elements coalescing the entire operation coherently and effectively.

In the AI era, reducing latency to the absolute minimum is vital, as is high-performance storage and unified metadata that can serve different tools and applications across various systems and environments. This isn't to mention the massive GPU throughput required. Modern challenges, therefore, require modern solutions. Systems like Dell AI Data Platform offer enterprises a streamlined infrastructure engineered to help navigate choppy waters when transitioning AI projects from pilot to production.

The system, powered by NVIDIA, unifies Dell's storage systems and modular data engines with its AI-accelerated computing platform, networking capabilities, and AI enterprise software to offer a single and fully integrated platform for large-scale AI workloads. It’s a powerful enabler for organizations as they further their AI efforts.

As AI projects scale, the challenge is building a data foundation that can support performance, governance, and cost control across environments. That is where modern AI data infrastructure becomes a competitive advantage. The Dell AI Data Platform delivers 6x better SQL query performance with NVIDIA Blackwell GPUs vs. x86 CPUs. Furthermore, customers see up to 12X faster vector indexing.

The platform delivers enterprise-grade security and data protection, modular building blocks that let organizations start small and scale as requirements evolve, and an open architecture that supports the integration of new technologies over time.

To learn more about how Dell AI Data Platform can power the next phase of AI projects –from pilots to fully-fledged deployments – please visit the website.