From Proof of Concept to Production: Advancing AI Adoption

Everything you read these days gives the impression that every company, except perhaps yours, is advancing their AI initiatives quickly and successfully. The reality is that companies are at different stages of AI adoption and experiencing varying degrees of success. According to Lenovo’s CIO Playbook 2025 with research insights from IDC, 46% of organizations surveyed globally are either in the early stages of development or implementation, or supporting various pilot projects/use cases.[1]

The most successful companies are progressing by running AI proofs of concept (POCs). It’s a lower-risk approach compared to moving straight into live production. It allows companies to identify gaps, such as a lack of sufficient AI-ready data, in-house AI expertise and AI infrastructure, and address challenges related to complexity, security and scalability.

Given the scope of new requirements for AI initiatives, it’s not surprising that many POCs don’t succeed. The CIO Playbook 2025 reported that 88% of POCs monitored didn’t advance: Surveyed companies launched 33 AI POCs, of which only four made it into production.[2] This indicates a low level of organizational readiness in terms of data, processes and IT infrastructure. But again, the purpose of an AI POC is to identify these kinds of gaps, so a POC that doesn’t make it to production can still be valuable.

The most successful organizations I’ve worked with start small but think big. All signs point to quick wins that build momentum.

Maturity levels vary, but not ambition to succeed

The enthusiasm for AI is universal across business units, but readiness differs. In some companies, sales teams may already be deploying AI-driven personalization, while operations teams are still in the early stages of workshops, just trying to understand the basics. The maturity gap doesn’t signal a lack of ambition; every team wants to leverage AI, but each is moving at its own pace. It’s another example of shadow IT, which can introduce data privacy and sovereignty issues, unpredictable costs and compromised performance.

Similar to the early days of cloud, when IT teams took back control and a centralized cloud function became the norm, enterprises are adopting the concept of the AI Center of Excellence. It’s an AI factory solution combined with a centralized governance model overseen by IT teams.

Like other technological disruptions–think digital transformation–AI comes down to people, processes and technology, plus data. With AI, a significant shift in mindset is necessary, and in many cases, it’s as substantial as the investment in technology. As we learned from the digital transformation era, early adoption leads to competitive advantage.

What’s critical is for companies to be intentional about advancing from one stage to another, whether they’re considering or evaluating AI, planning to start soon, in development or implementation, running pilot projects or achieving full adoption across all business units. Success at one stage builds momentum that propels companies forward in their AI maturity.

Building momentum through small wins

Starting with use cases that have a high potential for success, such as IT-related use cases, can help build momentum with other business units.  Small, measurable AI wins build confidence, momentum and organizational readiness for larger and riskier initiatives. It’s essential not to go big too fast.

Organizations achieve success when they start with smaller, practical AI use cases that deliver a clear, visible and measurable impact quickly. A well-defined use case like automating email classification might feel too basic, but when it saves employees hours each week and wins trust, it lowers the barrier to entry and sets the stage for bolder, higher-stakes AI projects. When companies implement AI use cases in IT Ops, software development and marketing, they often exceed expectations.

Building momentum also means moving forward without having all the answers and a perfect plan. Implementing AI use cases means you’ll be entering uncharted territory, which is why testing proofs of concept is a less risky approach to starting your AI journey.

Closing the gap between AI proof of concept and production

Many organizations get stuck as they transition from proof of concept to scalable, secure, compliant production systems. A demo may look brilliant in controlled conditions, but as soon as real-world demands kick in—think security, integration, compliance, data accessibility, readiness and volume—that may change quickly.

Scaling introduces another layer of complexity, often amplifying these challenges significantly. Production-grade AI requires greater resources and alignment, such as ensuring your IT infrastructure can accommodate significantly increased data throughput and compute power demands. You need the right foundation of AI infrastructure, with the necessary compute power, cooling and networking.

An AI Center of Excellence can also be useful at this stage. A centralized governance structure is essential to deploying production-grade AI solutions and helping various business units share best practices.

Recognizing AI agents as a new workforce of the future

Agentic AI tools are advancing fast; the technology is solid and ready to execute. Many businesses are just getting up to speed on how  AI agents can be productive, ethical, sustainable, and complementary to GenAI.

Production-grade agentic AI is not just technology; it behaves like a workforce. AI agents in customer service, for instance, directly influence customer satisfaction and revenue, so they must be trained, monitored and scaled like employees.

A shift in mindset is required to understand how AI agents can transform your business. Interacting with an AI agent is an experience that can help facilitate this shift. Recently, I received a sales call from an agentic AI agent. While I recognized that the caller wasn’t human, I was impressed with the seamless delivery of product information and benefits.

The use of AI agents redefines infrastructure investments: they’re no longer optional IT spend, but a core business capability. Agentic AI requires distributed AI infrastructure to meet the needs of the various workflows. It consumes a significant amount of power and requires specialized cooling, which is available in AI-ready data centers.

Deploying agile AI infrastructure

AI adoption requires infrastructure that can flex as quickly as the business environment changes. Your company might start with a cloud-first approach for convenience. However, over time, evolving regulatory pressures and cost control, data locality or resilience strategies can all influence decisions on whether you lease or acquire GPU assets. Getting architecture right from the start and ensuring it can adapt is critical to long-term AI success.

Distributed AI infrastructure is becoming the go-to choice for many businesses as they build production-grade AI solutions. They need a distributed AI infrastructure located everywhere their data lives to connect that data with compute and decision-making tools securely, quickly and flexibly.

With Equinix Distributed AI, you can deploy the infrastructure you need to accelerate AI innovation at scale wherever opportunities exist. Our vendor-neutral AI ecosystem comprises more than 2,000 partners with whom you can connect to scale quickly and securely, without requiring custom builds. You can also validate your AI architecture in a secure, ready-to-use environment at one of our 20 Equinix Solution Validation Centers, located across 10 countries.

Learn why the future of AI infrastructure is distributed: View videos with Equinix executives for an introduction to our Distributed AI solution.

You may also be interested to read our Distributed AI solution brief.

[1] Lenovo, CIO Playbook 2025: It’s Time for AI-nomics, Lenovo US, with research insights by IDC, February 2025.

[2]Lenovo, CIO Playbook 2025: It’s Time for AI-nomics, Lenovo US, with research insights by IDC, February 2025.