The cloud as scalable AI infrastructureAustralian organisations are forecast to spend more than $33.6 billion on public cloud services in 2026, a healthy 17.9 percent bump year on year, according to Gartner. Infrastructure-as-a-Service is growing fastest, rising 24.1 percent to $7.1 billion, driven largely by demand for GPU-intensive AI workloads.Yet infrastructure investment doesn’t automatically translate into AI success. Deloitte’s State of AI in the Enterprise report found only 12 percent of Australian leaders say generative AI is already transforming their business, compared with 25 percent globally. Australia’s AI ambition remains strong, but enterprise readiness is still uneven. Hybrid is the sweet spotIn 2026, cloud infrastructure is no longer separate from AI strategy. Organisations that once viewed cloud mainly as a cost-efficiency tool are now recognising it as the only practical way to scale AI, giving them access to graphics processing unit (GPU) capacity, elastic compute and managed services for model training, fine-tuning and inference.“There’s a shift toward inference-optimised approaches as organisations fine-tune smaller domain-specific models instead of relying on larger general-purpose LLMs,” said Gartner’s Adrian Wong. “Many are turning to hybrid cloud architectures to push this processing to the edge, which lowers cloud costs while still supporting automation at scale.”The market is moving beyond experimentation toward real-time inference and agentic AI, with organisations relying more heavily on Platform-as-a-Service (PaaS) from the likes of Salesforce, Oracle, ServiceNow and ZenDesk to manage autonomous workflows and embed AI in business applications. PaaS spending in Australia is forecast to grow 20.9 percent in 2026 to almost $10 billion, said Gartner in its May update, underscoring where AI development and orchestration are now concentrated.Hybrid infrastructure is now the default model. Rather than relying solely on public cloud, many organisations are combining on-premises systems for sensitive workloads, private cloud for compliance-heavy data and public cloud for burst AI capacity. Australia’s hybrid cloud market, valued at US$4.8 billion ($6.7 billion) in 2025, is projected to reach US$18.6 billion ($25.97 billion) by 2034, with AI and edge computing among the main growth drivers.Hybrid cloud growth is particularly strong in manufacturing, logistics, and agriculture where edge-based applications of AI are common. But companies across the board are seeing the value of taking a hybrid approach when it comes to developing and deploying AI.“We needed somewhere we could let AI agents act autonomously without data security exposure, and learn what guardrails were actually necessary before deploying agents into our cloud environment,” Ivan Wong, head of data & AI at funds manager LVP, told iTnews.“That sequencing, on-prem first to build conviction and cloud second, has worked better than starting in the cloud and retrofitting controls.” Real-time inference and agentic AI Forget AI assistants and copilots. All the hype, and much of the development, is now centred on AI agents. IDC research found 41 percent of Australian organisations are already deploying agentic AI and another 50 percent plan to do so within six months. The resulting productivity gains are now widely reported. When home loan lending platform Lendi Group implemented numerous agents to streamline loan application processes.“For customers, you can walk out of an open home, upload a contract and within 60 to 70 seconds have a readable summary and property report,” said Lendi’s chief technology officer, Devesh Maheshwari. “For brokers, the agentic funnel means richer data and far less back-and-forth. In some areas we’re seeing applications processed 60 percent faster, and in others up to 200 percent improvements in throughput.”Gartner expects 40 percent of enterprise applications to include task-specific AI agents by the end of 2026, up from less than 5 percent in 2025.At the same time, inference-optimised architectures are replacing brute-force approaches. Instead of relying on large general-purpose models for every use case, organisations are fine-tuning smaller, domain-specific models that reduce cost and latency. This is encouraging hybrid deployments in which inference runs closer to users or devices while cloud handles training and model management. The public sector’s cloud-first pushIt’s not just corporate Australia leveraging the cloud to enable AI. The public sector is also accelerating. The Australian Government’s Whole-of-Government Cloud Computing Policy takes effect on 1 July 2026 and creates a unified framework for cloud adoption across the public sector. The government has also signed a five-year Microsoft volume sourcing agreement to support AI and cloud uptake, while the APS AI Plan 2025 establishes chief AI officers in each agency. AI could add up to $142 billion a year to Australia’s GDP by 2030 if adoption accelerates, according to ChatGPT maker OpenAI, which alongside Anthropic has established an Australian presence.Investment appetite remains strong, but scrutiny is increasing. While 92 percent of Australian data leaders intend to increase GenAI investment, 98 percent say they face significant challenges proving business value, according to Informatica’s CDO Insights 2025 global research. The gap between pilot enthusiasm and production outcomes is now one of the defining issues, not just in Australia, but globally. Modernising architectures to support AI workloadsA major operational challenge in 2026 is the condition of existing enterprise architecture. Legacy systems, siloed data and rigid application frameworks were not designed for AI workloads or the high volumes of unstructured data they depend on.IDC and MongoDB research covering 200 Australian organisations found 58 percent say their architecture is too rigid, costly and slow for building new AI applications. Organisations that fail to address technical debt are likely to see AI initiative failure rates rise by 50 percent by 2027. By contrast, companies running strategic modernisation programmes are generating nearly three times more digital revenue from digital channels, 68 percent versus 24 percent for mainstream peers.That is driving demand for AI-ready data infrastructure. Data lakehouses are gaining ground as a foundation for enterprise AI, while containerisation is accelerating portability across on-premises, cloud and edge environments.Andrew Burnet, CTO at a large Australian university, said his institution had evolved its data architecture into a tiered model specially designed for AI/ML activities.“We… redesigned our AWS landing zones to support greater freedom of development whilst maintaining the required corporate security controls, and built an enterprise AI governance framework,” he said.Support for AI is now the leading reason for database and application modernisation in Australia, cited by 45 percent of organisations. But 96 percent have experienced some form of failed modernisation effort, with siloed and poor-quality data the most common obstacle. That reinforces a core lesson of 2026: architecture modernisation is a prerequisite for AI success.“I have the advantage of owning both the architecture and the implementation, so decisions get made on output quality rather than procurement cycles,” said Wong. “That's a contrast to larger enterprises I've worked in, where existing partnerships, risk committees and governance layers slow things down considerably.”