Customers want measurable AI outcomes, but many are held back by infrastructure complexity, limited GPU compute and uncertainty around where to start. For partners, the opportunity is to help them move beyond AI hype and build practical capability with confidence.
That need is clear in the SME market. According to the National AI Centre, 43% of Australian SMEs reported some level of AI adoption across December 2025 to February 2026, with adoption rebounding to 44% in February. This signals a growing opportunity for partners to turn early AI interest into practical outcomes.
Through AI Accelerate, Dicker Data helps partners bring Dell Technologies infrastructure, NVIDIA capability and specialist ecosystem expertise together, supporting customers as they move AI from experimentation to production.
What's stopping customers from scaling AI?
Many organisations have already explored AI through proof of concepts, experiments and isolated productivity use cases. The next challenge is turning that experimentation into repeatable, scalable outcomes. That means giving technical teams the ability to build, test, refine and deploy models without being slowed down by infrastructure bottlenecks, data movement, latency, or the cost of relying on centralised compute for every iteration.
Trust remains one of the biggest barriers to AI adoption, with around 65% of non-adopting Australian SMEs citing distrust in AI decision-making or a preference to maintain human control. This is where partners can play an important role in helping customers adopt AI responsibly, with the right governance, infrastructure and support.
For customers in industries such as construction, mining, logistics, manufacturing and field services, the value of AI often depends on speed. Models need to be refined as conditions change. Operational data needs to be processed closer to where work happens. Insights need to be accurate enough to support better decisions, reduce risk and improve productivity.
As Leo Arkhipov, AI and Digital Transformation Lead at 365 Mesh, explains, “AI is creating value at an accelerating pace, and yet many organisations are still not set up to capture it effectively. Those moving ahead are shortening the path from idea to real-world results.”
Why does GPU compute matter for AI development?
One of the biggest challenges in AI engineering is not simply access to compute. It is the ability to iterate quickly. When teams are working with multimodal data, evolving models and real-world operational environments, delays in experimentation can slow innovation, increase costs and make it harder to move AI projects into production.
365 Mesh is a strong example of this in practice. Its GreenMesh platform uses multimodal AI and sensor data to help detect risks earlier in environments where safety, visibility and cost pressures are high. Behind that outcome is a need to continuously test, refine and optimise AI models across edge and cloud environments.
“The challenge isn’t just accuracy, but speed of iteration at scale,” says Sriram Puvvada, Chief Architect at 365 Mesh. “So we need to experiment fast and not just scale big.”
This is where dedicated, high-performance AI development infrastructure can change the equation. Dell Pro Max with GB10, powered by NVIDIA Grace Blackwell architecture and pre-installed with NVIDIA DGX OS, gives AI teams access to advanced AI development capability at the deskside, supporting local prototyping, fine-tuning, inference and data science workflows.
As Amir Kalil, AI Practice Lead at Dicker Data, says, “The Dell Pro Max with GB10 running on NVIDIA DGX OS puts dedicated GPU capability directly at the point of innovation, giving AI engineering teams a faster, more controlled environment to build, test and iterate without the cost and complexity of routing everything through a data centre.”
How does Dell Pro Max with GB10 support AI engineering teams?
For customers, the value is not the hardware alone. The value is what the right infrastructure enables: faster model development, lower iteration friction, stronger control over data and a clearer pathway from development to deployment. For partners, that creates a powerful conversation around helping customers unlock AI value without overcomplicating their environment or delaying outcomes.
For active AI engineering workflows, the commercial case can become clear quickly. According to Amir Kalil, AI Practice Lead at Dicker Data, “The ROI is very simple: for active engineering AI workflows, the Dell Pro Max with GB10 can deliver full ROI in under six months.”
Through Dicker Data, partners can access an ecosystem designed to support the full AI journey. That includes Dell Technologies infrastructure, NVIDIA capability, local technical expertise, and AI Accelerate pathways that help partners identify opportunities, validate use cases and bring trusted AI solutions to market.
For 365 Mesh, Dicker Data’s AI ecosystem brings together the right technology and support to help accelerate customer outcomes. As Arkhipov notes, “Our main advantage as a Dicker Data partner is gaining access to the latest AI technologies, from NVIDIA and Dell Technologies, to create value for our customers at accelerated pace.”
How can Dicker Data help partners capitalise on the AI opportunity?
The opportunity for partners is to move the customer conversation beyond AI hype and into practical business outcomes. That could mean helping a customer assess where AI can reduce operational risk, improve visibility, automate manual processes, enhance productivity, or create new services and revenue streams.
Dicker Data helps partners have those conversations with confidence. By combining infrastructure, vendor relationships, enablement and specialist ecosystem capability, partners can better support customers at every stage of the AI lifecycle, from readiness and use case discovery through to proof of value, deployment and optimisation.
“This is the kind of real-world acceleration Dicker Data is focused on through AI Accelerate, bringing together NVIDIA capability, Dell Technologies systems and partner expertise like 365 Mesh to help organisations move faster from experimentation to production,” says Kalil.
For customers ready to build, test and scale AI more effectively, the combination of Dell Technologies, NVIDIA and Dicker Data provides a practical way to bring GPU capability closer to innovation. For partners, it creates a pathway to help customers capture AI value faster, while strengthening their own role as trusted advisors in a rapidly evolving market.
How can partners start an AI conversation with customers?
Relevance is another key barrier, with 54% of non-adopting Australian SMEs saying AI is not relevant to their business. For partners, that creates a practical starting point: helping customers identify where AI can improve efficiency, reduce risk, support better decision-making or unlock new value.
AI adoption is accelerating, but customers still need help turning ambition into action. Dicker Data can help partners identify the right opportunities, access the right technology and build a stronger pathway to measurable AI outcomes.
To explore how Dicker Data can support your AI conversations, speak with your Dicker Data representative.
