From AI Pilots to Production Results with Governed Execution

Rackspace Technology and Palantir Technologies Inc. have entered into a partnership aimed at helping enterprises operationalize AI in production environments where performance, governance and measurable outcomes matter. Together, the two organizations bring platforms and execution capabilities designed to help companies translate AI strategy into real business impact. The collaboration reflects a broader shift taking place across the enterprise landscape.

In conversations I have with enterprise leaders, the focus has shifted to execution. Leaders are asking where AI is delivering measurable value, how quickly initiatives can scale and what it takes to operationalize results across complex environments. Many are still working to close the gap between experimentation and sustained impact.

That gap is usually created by the realities of deploying inside complex enterprise systems.

Why optimized AI components don’t translate into outcomes

Most AI ecosystems are engineered around highly optimized components. Hardware vendors push performance limits. Systems are designed for efficiency. Software frequently arrives as copilots or point solutions.

This progress is real, but enterprises do not operate as collections of optimized parts. They run as interconnected systems built over decades, shaped by fragmented data, legacy processes, regulatory constraints and accountability for measurable results. In that environment, even powerful AI platforms do not automatically translate into business value. The technology can perform as designed. The enterprise environment determines whether it delivers impact.

Based on conversations with customers, this is where initiatives most often stall. Production environments surface constraints, dependencies and operating realities that pilots rarely reveal, and those realities ultimately determine whether AI succeeds or stalls.

What determines whether AI succeeds at enterprise scale

Within large organizations, the difference between a promising pilot and production results rarely comes down to model performance. Instead, it reflects how well the surrounding environment is prepared to support AI in operation.

Enterprise data environments are typically distributed across systems, teams and governance structures. Ownership varies. Standards differ. Security and compliance requirements shape what can be deployed and how. Production AI must operate within those realities.

At enterprise scale, outcomes are driven by data readiness, architectural clarity and operational alignment. When those elements are in place, AI can scale. When they are not, even strong models struggle to deliver sustained value.

A familiar pattern from the cloud era

Enterprise leaders will recognize this pattern because you’ve seen it before. When cloud first entered the market, virtualization alone did not create business value. Moving workloads was relatively straightforward. Delivering measurable outcomes required something far more deliberate: operating models, governance frameworks and modernization strategies built specifically for cloud environments. That transition established a lasting principle: enterprise value comes from disciplined execution.

That same pattern is playing out again today. Enterprise AI is following a similar trajectory. Early tools can improve efficiency, but efficiency alone rarely justifies enterprise investment. Real value shows up when AI supports better decisions, enables new capabilities and strengthens operational performance across the business. That level of impact only materializes when AI is integrated across systems, data and workflows.

What it takes to make AI work

For AI to deliver sustained impact, execution has to be designed into the initiative from the beginning. In my experience, the initiatives that produce measurable results start with clear business objectives, defined ownership and success criteria tied directly to outcomes.

Those initiatives also rely on a trusted knowledge foundation. AI cannot operate reliably on fragmented or inconsistent data, which is why unified, governed data environments are critical. I believe the enterprises positioned to deliver measurable AI outcomes are those that can connect data, logic and workflows into a shared operational model leaders can rely on for decision-making.

Operational realities ultimately determine whether AI succeeds beyond the pilot phase. Security, compliance, uptime, cost controls and change management shape what can run in production and what cannot. In practice, production environments are the real proving ground. Measurement has to be built in from the outset so leaders can evaluate value, manage risk and track impact over time.

Why execution is the differentiator

This is the context behind the collaboration between Rackspace Technology and Palantir Technologies Inc. Palantir delivers platforms built to help organizations operationalize AI through structured, governed environments that connect data, logic and decision-making. Rackspace brings deep experience helping enterprises deploy complex technologies and prepare their environments for production adoption at scale.

In my role, I spend a significant amount of time examining what separates AI initiatives that remain experimental from those that deliver measurable impact. The difference consistently comes down to execution discipline.

That is the problem this partnership is designed to solve. Together, we help organizations move beyond experimentation toward AI that can be trusted, scaled and measured in real operating environments.

Enterprise platforms create potential. Execution turns that potential into results. Partnerships like this matter because they close the gap between innovation and operational impact.

Learn more about how this partnership helps you operationalize AI in production environments.