What it takes to move enterprise AI from experimentation into production

The hard part of enterprise AI starts after the prototype.

Once AI is expected to deliver real work at scale, the pressure shifts to inference economics, full stack design, and sovereign deployment choices that can survive production.

That is the gap Angelo Joseph, Head of ANZ Cloud Engineering at Oracle, and Johann Kruse, Strategic AI Partnerships Lead at NVIDIA, addressed at the 5th Data & AI Edge.

Key takeaways:

• AI creates more value when organisations redesign work around new capacity, rather than treating it as another tool rollout.
• Inference is becoming the point where enterprise AI starts generating measurable business value, with speed, throughput, and deployment economics shaping outcomes directly.
• Production scale depends on full stack readiness, including usable data, infrastructure choices, and sovereign deployment models that support secure execution.

Enterprise AI changes the business before it changes the technology stack

Enterprise AI has moved beyond incremental uplift.

The bigger change comes when organisations redesign how work is executed, how capacity is used, and how performance is measured.

Angelo makes that case directly.

He argues that the pace of change is already outstripping organisations’ ability to absorb it, while the consumerisation of AI has changed workforce expectations across the enterprise.

He also points to a different order of ambition, where the target is no longer marginal gains, but 10x and 35x improvements in efficiency and throughput.

That shifts AI from a side initiative into a structural business capability.

Inference is where enterprise AI starts doing real work

The centre of gravity is shifting away from model training alone and towards inference, where AI is actually producing intelligence that can be used in operations, products, and decision making.

Johann explains that shift through the idea of an AI factory.

He is clear that this is a capability, not simply a room full of servers.

Good usable data goes in, intelligence comes out, and that intelligence is then used to run the business.

He also pushes back on the idea that enterprise AI is just a hardware story.

In his framing, AI is full stack by nature, spanning software, infrastructure, models, and the delivery ecosystem that makes those capabilities usable at scale.

Deployment speed now depends on architecture, sovereignty, and readiness

Production AI will increasingly be constrained by deployment readiness, not by access to the next model release.

That is why infrastructure and sovereignty are moving closer to the centre of enterprise AI strategy.

Johann describes GTC 2026 as a deployment focused moment, with attention on capabilities that will reach enterprise environments over the next six to eighteen months rather than on research demos alone.

Angelo connects that to the practical decisions organisations already face around sovereign AI, deployment options, and faster time to market.

Enterprise AI moves faster when architectures are designed to reduce data movement, keep intelligence close to trusted environments, and build control into the stack from the beginning rather than retrofitting it later.