From Pilot to Production: How Australian organisations can successfully progress AI

Following strong audience engagement at the recent ADAPT Cloud & Infrastructure Edge event, Sharon AI and World Wide Technology answer the questions we were unable to get to during the panel session and explore what it takes to turn AI investment into measurable business outcomes.

At the recent ADAPT Cloud & Infrastructure Edge event in Sydney, Sharon AI joined World Wide Technology (WWT) to discuss one of the most important challenges facing Australian organisations: how to move AI beyond experimentation and into secure, scalable production.

During the panel session, Sharon AI Chief Operating Officer and Co-founder Andrew Leece, WWT Chief Technology Advisor Pratik Jhaveri and Sharon AI Chief Revenue Officer Linton Burling explored the infrastructure, architecture and execution decisions organisations need to make as they scale AI.

The panel session concluded with some key learnings, the core being summarised by Pratik: “Stop starting [new pilots], instead focus on moving your existing pilots towards production. You’ll learn more and begin seeing results sooner.”

The discussion generated more questions than time allowed us to answer. Many focused on a common concern: organisations are not short of AI ideas or pilots, but too few initiatives are progressing into production and delivering measurable value.

Below, we respond to the questions raised by the audience and examine how organisations can close that gap.

In summary, the common questions were:

1. There is an abundance of AI agents, and often several solving the same problem. How can organisations identify the most efficient and avoid unnecessary duplication?
2. Which organisations should invest in their own AI infrastructure, and which should use in-country hyperscaler platforms?
3. What prevents Australian organisations from progressing beyond the AI pilot stage?
4. What does “execution risk” mean in an AI deployment?
5. How should organisations categorise production AI workloads when deciding where to host them?

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Question One:
There is an abundance of AI agents, and often several solving the same problem. How can organisations identify the most efficient and avoid unnecessary duplication?

Silo’s – where pilots go to die

The rapid growth of generative AI has made it relatively easy for teams to build agents. That accessibility is positive, but it can also lead to disconnected experimentation, with different business units developing similar agents using different platforms, data and governance standards.

Simply put, remove the silo’s and create an observability framework to open up progress.

As Pratik concluded: the answer is not to stop experimentation. It is to introduce enough coordination to ensure experimentation contributes to a coherent enterprise capability.

Organisations should begin by creating visibility across their AI portfolio: what agents are being developed, which business problem each one addresses, what data it uses, who owns it and what measurable result it is expected to deliver.

From there, proposed agents can be assessed against several questions:

• Does this solve a defined business or customer problem?
• Does a similar capability already exist elsewhere in the organisation?
• Can an existing agent, workflow or shared service be extended?
• Is an autonomous agent genuinely required, or would a simpler automation deliver the same result more reliably?
• What is the expected value relative to its compute, integration and maintenance costs?
• Who will own its performance, security and governance once it enters production?

Create, reuse, remodel

The most effective approach is often to develop reusable components, such as approved models, data connectors, identity controls, orchestration tools and monitoring capabilities, rather than building every agent independently from the ground up.

The objective should not be to deploy the greatest number of agents. It should be to create the smallest effective portfolio of AI capabilities that produces measurable value.

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Question Two:

Which organisations should invest in their own AI infrastructure, and which should use in-country hyperscaler platforms?

It’s never black or white – success lives in shades of grey

The answer should not be treated as a binary choice between owning infrastructure and using the public cloud. For many organisations, the right answer will be a hybrid model based on the requirements of individual workloads.

Hyperscaler platforms can be well suited to early experimentation, variable demand and organisations that want access to managed services without making long-term infrastructure commitments. They can provide speed and flexibility, particularly when workloads do not have highly specialised performance, sovereignty or cost requirements.

Dedicated sovereign AI infrastructure becomes more compelling when an organisation has:

• Sustained or predictable demand for accelerated compute
• Sensitive, regulated or strategically important data
• Data residency or sovereignty requirements
• High-performance training or inference workloads
• A need for greater control over security, architecture and performance
• Sufficient scale for dedicated capacity to offer more predictable economics
• A requirement to avoid capacity constraints or competition for scarce GPU resources

To Edge or Not to Edge?

Running AI at the edge may also be appropriate when decisions must be made with very low latency, connectivity is constrained, or data needs to remain close to the location where it is generated.

However, edge infrastructure introduces additional hardware, operational and maintenance requirements, so it should be driven by a genuine workload need rather than infrastructure preference.

Neoclouds – freedom to write your own rules

A neocloud provides another option. Unlike a general-purpose cloud, a neocloud is designed specifically for accelerated computing and AI workloads. It gives organisations access to high-density GPU infrastructure without requiring them to build and operate the entire physical environment themselves.

Sharon AI’s model is designed to provide access to trusted, secure and sovereign AI infrastructure through an ecosystem of established data centre and technology partners. WWT then helps organisations integrate that infrastructure with their applications, data environments and operating models.

This allows customers to make infrastructure decisions based on workload requirements without assuming all of the complexity and execution risk themselves.

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Question Three:

What prevents Australian organisations from progressing beyond the AI pilot stage?

Answering the business need with proof

Leadership incentives and short-term decision-making can play a role, but the problem is usually broader than any single barrier such as renumeration.

Many pilots are designed to demonstrate that a technology works, rather than prove that it can deliver sustainable value in a production environment. As a result, the pilot may succeed technically but still lack the foundations needed to scale.

Common barriers include:

• No clearly accountable business owner
• An undefined business problem or value case
• Limited access to trusted, production-quality data
• Security and governance being considered too late
• Difficulty integrating AI with existing systems and workflows
• Uncertain infrastructure and token costs at scale
• Insufficient access to accelerated compute
• A shortage of operational skills and support
• Procurement processes that move more slowly than the technology
• No plan for monitoring, maintaining and improving the system after launch

Workload over development

The strongest AI programs combine long-term leadership commitment with short, measurable delivery cycles. Organisations should select an initial workload linked to a real operational or financial outcome, assign an accountable owner and define what success looks like before development begins.

That could be; reducing the time required to resolve a customer enquiry, improving equipment uptime, accelerating a research process or lowering the cost of a repeatable internal workflow.

Starting with one meaningful workload creates a clearer path to production than launching a large number of loosely connected pilots. It also gives the organisation an opportunity to develop reusable infrastructure, governance and operating practices for the workloads that follow.

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Question Four:

What does “execution risk” mean in an AI deployment?

Execution risk is the combined risk that an AI initiative will fail to become a secure, reliable and economically sustainable production capability.

It includes elements such as model hallucinations and guardrails but can extend to any number of factors.

Execution risk can arise across the full AI stack:

• Business risk: The project does not solve a sufficiently valuable problem, lacks an accountable owner or cannot demonstrate a justifiable return.
• Model risk: Outputs may be inaccurate, inconsistent, biased or unsuitable for the intended use without appropriate evaluation and human oversight.
• Data risk: The organisation may not have the quality, permissions, lineage or controls needed to use its data safely and effectively.
• Infrastructure risk: Compute may be unavailable, poorly sized or too costly when the workload scales. The facility may also be unable to support the power density, cooling and networking required by modern accelerated computing.
• Integration risk: The AI capability may work in isolation but fail to integrate with core applications, data sources, identity systems and business processes.
• Operational risk: No team is responsible for monitoring performance, managing updates, controlling costs and maintaining the system over time.
• Security and compliance risk: Sensitive information may be exposed, processed in an unsuitable jurisdiction or used without the controls required by the organisation or its regulators.

These risks are interdependent. A model can perform well in testing and still fail in production
because of integration, latency, infrastructure or cost constraints.

Risk mitigation end to end

This is why Sharon AI and WWT advocate an end-to-end approach. Sharon AI provides the specialised AI infrastructure and sovereign compute foundation, while WWT brings the architecture, integration, testing and deployment expertise needed to connect that infrastructure to real enterprise environments.

The infrastructure, platform and workload cannot be planned in isolation. Execution depends on all three working together in a symbiotic partnership.

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Question Five:

How should organisations categorise production AI workloads when deciding where to host them?

Start at the beginning

The starting point should be the workload, not the infrastructure provider or preferred technology.

A useful first distinction is between several common workload types:

• Model training: Building a new model or undertaking substantial training using large datasets. These workloads typically require intensive, high-performance GPU capacity and high-speed networking.
• Fine-tuning and adaptation: Customising an existing model using organisational or industry-specific data. Requirements vary depending on the size of the model, data sensitivity and frequency of retraining.
• Batch inference: Processing large volumes of information without an immediate response requirement. These workloads may offer greater flexibility around location and scheduling.
• Real-time inference: Producing responses or decisions immediately, such as customer interactions, fraud detection or operational systems. Latency, availability and predictable performance are critical.
• Retrieval-augmented generation: Connecting a model to proprietary organisational information. Data access, security, permissions and integration are often more important than raw compute alone.
• Agentic workflows: Using AI to initiate or coordinate actions across business systems. These require strong identity controls, monitoring, auditability and clearly defined limits on autonomy.
• Edge AI: Running models close to where data is generated or decisions are required. This can be appropriate where latency, connectivity or data locality is critical.

Once the workload has been defined, organisations should assess it against a consistent set of criteria:

1. 1. Data sensitivity and sovereignty: Where can the data legally and appropriately be stored and processed?
   2. Performance and latency: How quickly must the system respond, and how consistent must that performance be?
   3. Scale and utilisation: Is demand steady, variable or likely to increase significantly?
   4. Economics: What will the workload cost at production volume – not merely during the pilot?
   5. Integration: Which applications, data sources and operational systems must it connect to?
   6. Resilience: What level of availability and recovery capability is required?
   7. Governance: How will outputs, access, security and model behaviour be monitored?
   8. Operating model: Who will maintain the workload and remain accountable for its performance?

This assessment may lead to the public cloud, a sovereign neocloud, dedicated infrastructure, the edge or a combination of environments. The aim is not to force every workload onto a single platform. It is to place each workload in the environment that best meets its business, technical, security and economic requirements

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From AI ambition to AI outcomes

The transition from pilot to production is not simply a technology challenge. It requires organisations to align the business case, workload, data, infrastructure, integration and operating model from the beginning.

Australia has a significant opportunity to build sovereign AI capability and translate it into productivity, innovation and economic value. But access to compute alone will not deliver that outcome. Organisations also need the expertise and discipline to turn infrastructure into working, production-ready solutions.

That is the role of the Sharon AI and WWT partnership: bringing together trusted, secure and sovereign AI infrastructure with the architecture, integration and deployment capabilities organisations need to move confidently from experimentation to production.

The central question is no longer whether organisations will invest in AI. It is how quickly they can turn that investment into secure, scalable and measurable results.