Beyond Use Cases: What Enterprises Need to Know About AI Infrastructure

It’s easy to get caught up in the promise of AI. With every product release and model announcement, the conversation accelerates. From automating customer service to creating personalised experiences at scale, the use cases are compelling and increasingly well understood. 

But the most valuable discussions right now are not about what AI could do, but how organisations are preparing to actually make it happen. And that comes down to infrastructure. 

Use cases are only half the story 

Many leaders are shifting their focus from surface-level applications to deeper architectural questions. More and more conversations we’re having aren’t just about the use cases. It’s about how to think about the infrastructure and architecture around agents, whether it’s A-to-A frameworks or multi-agent coordination at scale. 

The excitement around agents, copilots and intelligent assistants is well-placed, but deploying these effectively across an enterprise requires much more than plugging in a model.    You need to consider:  - How will different agents interoperate?  - Where do you need control in processes or outcomes?  - What guardrails will govern IP and data flow?    In other words: are you structurally ready for AI? 

A disconnect between vision and delivery   

There’s often a disconnect between strategic vision and operational delivery. Most people already use AI in their personal lives, planning a weekend, booking travel, so the awareness is there. But in organisations, good ideas often get stuck somewhere and never looked at. 

The solution? Rapid prototyping and live, cross-functional conversations.    Build pathways that allow your teams to experiment and iterate quickly. Create forums where data architects, security leads and business owners can shape AI implementation together. And above all, avoid building in isolation. 

Your data estate matters more than your framework   

Before choosing a model, your data needs to be in order. People worry about which framework to use, but are they spending enough time worrying about the data assets and the data estate needed to support these applications? 

If your data is fragmented, unclean or inaccessible, even the best models will under-deliver. Infrastructure decisions such as cloud architecture, data governance, security policies and API strategy, become the foundation on which effective AI systems can operate.    From GitHub to OpenAI to Meta, the velocity of innovation is unrelenting. Blink, and something fundamental has shifted. That pace can be paralysing. But the best antidote to fear is action. It’s about getting from nothing to something. Once you see a prototype in front of you, you can begin to imagine how it could work elsewhere too. 

Three questions to guide your AI infrastructure strategy   

As you move beyond use cases, here are three foundational questions to explore with your teams:   

1. Are our data and systems ready to support intelligent agents at scale? 

2. Do we have the right internal collaboration to align architecture, governance and business priorities? 

3. Can we prototype quickly enough to keep pace with emerging capabilities and market needs? 

At Endava, we help organisations build not just AI products, but the ecosystems and infrastructure that make them viable, secure and scalable. If you’re ready to move from idea to execution, we’re here to help you design the foundation. 

This article was based on a discussion between our experts, the Endava AI Pod, and then generated with the help of ChatGPT and the Endava content team, using only insights based on the discussion, as part of an experiment to better understand ways of working with AI in authentic content generation.  For more AI insights, watch this space, there’s more to come!