An AI-ready enterprise needs room to experiment, safeguards for sensitive workloads and the freedom to adopt new models without starting again. Building one means clear choices about infrastructure, data, costs and responsibility.
When an AI project moves from a pilot into everyday use, the questions change. Leaders need to know whether it will work reliably, what it will cost and who will be responsible for it. A successful demonstration is only the beginning.
Boards want to understand the benefits and risks before approving an investment. Technology teams need time to test what AI can deliver. Enterprises need a way to do both: run small, controlled experiments and agree on what must be proven before scaling them.
Enterprises must decide where each AI application should run, what information it can use and what it is allowed to do. These decisions should reflect the risk involved. An assistant summarising public documents needs different safeguards from an AI agent that can change customer records.
Enterprise must also prepare for change. A better model may become available, a provider may change its prices, or an application may need new features. The aim is not to adopt every new model release. It is to make useful changes without having to rebuild the whole system.
According to Adobe’s 2026 State of AI Readiness Report, around 59% of organisations in the region are stuck in the ‘Frozen Middle’, where they are piloting or deploying AI but lack the coordination needed to scale it. For agentic AI, the figure stands at 40%.
The challenge is therefore bigger than choosing a model or securing computing capacity. Enterprises need a clear path from testing an idea to running it reliably. That means agreeing on who approves it, how success will be measured and what happens when something goes wrong.
Keeping control does not mean building every model or owning every server. It means knowing where data goes, who can access it, what AI system may do and who is responsible for its use. An enterprise can work with outside providers while retaining control over these decisions.
“Enterprise leaders are increasingly comfortable adopting AI. They remain significantly less comfortable relinquishing control. The dominant preference is Enterprise-controlled AI. Not vendor-controlled AI,” neo-cloud provider Neysa observes.
Neysa proposed a shared AI Platform that helps enterprises move from experiments to everyday use. The idea is to give teams a common foundation for running applications, monitoring performance and tracking costs. They should still be able to choose different models and tools for different tasks.
From Isolated AI Pilot Projects
Many organisations begin their AI journey with individual projects. One team might test a model, another might build a chatbot, and a third might explore AI agents. These projects help identify useful applications. The next step is deciding which ones deserve futher investment and who will run them.
Different teams may need different tools. The problem begins when no one has a clear view of what they are using, what data they can access or how much they are spending. Shared rules can address these gaps without forcing every team to use the same technology.
Before an AI application moves into everyday use, it needs a named business owner, approved data access, measurable quality targets, a support plan and an operating budget. It also needs enough computing capacity and someone to support it. Access to GPUs alone does not answer these questions; the enterprise needs to know how the application will keep running when demand rises or problems occur.
Enterprises should also understand what might make future changes difficult. An AI application may depend heavily on one provider’s tools or on the way a particular model responds. Keeping these dependencies clear can make changes easier, although a replacement will still require testing. If their AI compute infrastructure is tightly tied to a particular provider or technology, any will mean substantial rework too.
Teams should not have to build the same basic setup for every new AI application. Reusing approved ways to deploy applications, manage access and monitor performance can save effort and reduce risk. A shared platform and technical support can help, while business teams remain responsible for deciding whether the application works well enough.
Building a Controlled Environment
At the centre of this approach is what Neysa describes as a walled garden for AI. In simple terms, it is a controlled environment in which an enterprise manages the infrastructure supporting its AI applications, including the models, tools and frameworks it uses.
The objective is to give organisations greater control over their AI systems without restricting their ability to experiment with new technologies.
Neysa’s proposed architecture divides this environment into three broad areas based on how AI workloads need to be managed.
The first is an experimentation environment for innovation teams. It would allow them to test models, fine-tune existing ones, build applications and run POCs. Teams could also evaluate solutions from external AI software providers before deciding whether to deploy them more widely.
The second is a tightly controlled environment for workloads with strict requirements around data privacy, security and compliance. Enterprises could place applications that handle sensitive information or operate under regulatory restrictions in this environment, subject to the appropriate access controls and policies.
The third is a general deployment environment for AI applications that do not require the same level of regulatory oversight or data protection as more sensitive workloads.
These environments would operate within the same broader platform but serve different purposes. An enterprise could allow teams to experiment freely within defined boundaries while maintaining tighter controls over applications that handle sensitive data.
The separation would also help organisations decide where each workload should run, based on its requirements rather than applying the same infrastructure and security settings to every AI application.
Making AI Infrastructure Modular
A central part of Neysa’s proposal is the ability to replace individual components without rebuilding the entire infrastructure.
Enterprises may use different models for different tasks, depending on their performance, cost and other requirements. They may also change the tools and frameworks used to develop applications or introduce new technologies as their needs evolve.
Neysa argues that the infrastructure should support these changes from the outset. Models, tools and frameworks should be interchangeable within the platform, subject to compatibility, security and performance requirements.
For example, Adopt AI uses TrueFoundry’s AI Gateway to connect applications to models from providers including OpenAI, Anthropic, Google Vertex AI, AWS Bedrock and Groq.
Adopt AI’s co-founder and CTO Rahul Bhattacharya explains that applications no longer communicate directly with individual model providers. Instead, requests pass through a central gateway, which allows the company to switch models and manage traffic without changing application code.
This approach reduces dependence on a single model provider or technology stack. It also gives enterprises more flexibility when a new model becomes available, or an existing solution no longer meets their needs.
AI agents make this flexibility particularly important. Unlike applications that only generate responses to individual prompts, agents can perform multiple steps, use tools and interact with other applications to complete tasks.
A modular platform would give organisations a common environment in which to manage these components, rather than requiring them to create a new infrastructure setup for every application or agent.
However, replacing one component with another would not always be straightforward. Enterprises would still need to test compatibility, evaluate performance and ensure that changes meet their security and governance requirements.
Why Hybrid Cloud Could Play a Role
Building a controlled AI environment does not necessarily mean moving all enterprise applications and workloads to a single infrastructure provider.
Many companies already have substantial investments in public cloud platforms. Their applications, databases, development tools and business workflows may be built around services from providers such as Microsoft Azure. Moving these systems elsewhere could be difficult and expensive.
Neysa’s approach is to allow enterprises to retain these existing investments while running selected AI workloads in a separate, controlled environment.
For example, a company could continue using Azure AI Foundry to select and develop an AI solution while directing the computing workloads required to run that solution to infrastructure provided by Neysa. The AI system would process requests in the designated environment and return the results to the application running on the existing cloud platform.
This is a form of hybrid cloud architecture, in which workloads operate across different infrastructure environments according to their requirements.
The arrangement could allow enterprises to continue using their preferred cloud services while exercising greater control over where selected AI workloads run. It could also help them avoid having to move their entire application stack simply to change the infrastructure supporting AI.
The exact implementation would depend on the services involved, the integration available and the enterprise’s technical requirements. Data movement, latency, security responsibilities and the cost of operating across multiple environments would also need to be considered.
Governance, Interoperability and Cost Remain Key Challenges
The architecture an enterprise chooses must address more than the technical requirements of running AI models. It must also account for governance, interoperability and cost economics.
Governance determines how AI systems can be used across the organisation. Enterprises need to establish which teams can access particular models and data, what restrictions apply to sensitive workloads and how security and compliance policies are enforced.
A common platform could manage these requirements across environments, although organisations would still need to implement and monitor the policies and controls.
Interoperability matters when organisations use models, applications, and infrastructure from multiple providers. If these components cannot work together, enterprises may find themselves tied to particular technologies or forced to rebuild applications when their requirements change. A modular architecture could help, provided the underlying systems support the necessary integrations.
Cost economics is another consideration. The cost of running AI depends on factors such as the models selected, computing capacity, workload volumes and the infrastructure required to support applications in production. Organisations must also account for the cost of operating and maintaining the environment.
