The first phase of the AI boom was a land grab for compute. Organisations scrambled for GPUs, cloud infrastructure, and specialised environments to find quick wins and explore how generative AI could transform their operations.
But experimentation isn’t deployment. As enterprises shift from pilot projects to production, a new challenge is emerging: having the infrastructure to build AI does not necessarily mean having the foundations required to run it at scale. According to Gartner, organisations will abandon 60% of AI projects through 2026 simply due to a lack of AI-ready data.
The challenge facing enterprises is changing. Compute remains essential, but infrastructure alone will not determine which organisations successfully scale AI.
The shift from AI experimentation to business transformation
Early AI adoption was about possibility. Businesses tested tools, integrated generative features, and looked for quick wins. That was the necessary first step.
But scaling AI introduces a new level of complexity. As organisations begin embedding AI into critical processes, questions around how systems are built, governed, and deployed become increasingly important.
This is already playing out as AI labs and publishers negotiate the terms under which content can be used for AI training. Internally, enterprises face a similar challenge. Without clarity on what data organisations are legally and technically allowed to use, even advanced infrastructure can struggle to deliver real value.
Data rights become a strategic issue
Up until now, businesses have treated data as an asset to collect, store, and analyse. As AI becomes embedded into more business processes, that approach is beginning to change. Data is no longer simply something organisations hold; it is something they need to understand, govern, and use effectively.
For organisations looking to scale AI, ownership, licensing, and access rights are becoming increasingly important considerations. Access to compute and advanced models is only part of the challenge. Businesses also need confidence that the data behind those systems can support the applications they are building.
As organisations operate across different markets and regulatory environments, this challenge becomes even more complex. Cross-border data restrictions, intellectual property considerations, and evolving liability frameworks will influence what businesses can build, where they can deploy AI and how quickly they can scale.
The companies that address these questions early will be best positioned to move beyond experimentation and make AI a meaningful part of their operations.
The permission problem
Historically, technology adoption has followed a familiar pattern: organisations implement new capabilities first and establish governance later.
With AI, this sequence changes.
When data is core to how systems are built and deployed, access and usage rights cannot be treated as an afterthought. They are becoming fundamental requirements for organisations looking to mobilise and scale AI.
This does not mean slowing innovation. In fact, having clarity around data access can help organisations move faster by reducing uncertainty, accelerating decision-making, and creating the confidence needed to move AI from pilot projects into production.
The businesses that succeed will view data access and usage rights as part of their AI strategy – not simply as a compliance exercise.
Building the foundations for scalable AI
The future of AI will continue to depend on infrastructure. Organisations will need powerful computing resources, flexible cloud environments, and platforms capable of supporting increasingly demanding and sophisticated workloads.
But infrastructure alone will not determine who leads the next phase of AI adoption.
The organisations that scale AI most effectively will be those that bring together robust technology with trusted data, clear governance, and well-defined access rights.
The first wave of AI was about proving what was possible. The next stage will be defined by how successfully companies build the foundations needed to make AI work at scale. Those that can establish those foundations early will be best positioned to convert AI ambition into lasting business value.
Kevin Cochrane
Kevin Cochrane is CMO at Vultr. Vultr is on a mission to make high-performance cloud infrastructure easy to use, affordable, and locally accessible for enterprises and AI innovators around the world. Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal, and Cloud Storage solutions.
