AI Investment in 2026: Where Australian Businesses Are Spending and What Returns They Are Seeing

The conversation around AI investment has shifted fundamentally in 2026. After years of experimentation and exploratory spending, boards and executives are demanding clear returns. The question is no longer whether to invest in AI but how to invest wisely, where to allocate limited resources, and how to demonstrate value that justifies continued commitment.

KPMG Australia's latest research captures this shift precisely: 63% of C-Suite executives now rank artificial intelligence as their top concern for 2026, the first time AI has claimed this position. This represents a movement from inflation anxiety to AI anxiety, from debating whether AI matters to asking how to embed it before competitors do.

The stakes are substantial. Gartner forecasts that Australian IT spending will reach A$172.3 billion in 2026, an increase of 8.9% from 2025, with AI, cybersecurity, and cloud driving new investment. Server spending alone is increasing 30% to reach A$7.7 billion, driven primarily by AI-optimised infrastructure. Australian businesses are committing serious capital to AI. The critical question is whether they are investing it effectively.

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How much are Australian businesses investing in AI in 2026?

Investment patterns across Australian businesses reveal both significant commitment and notable gaps. According to KPMG research, 70% of Australian CEOs see AI as a top investment priority. Yet nearly a third commit less than 10% of their overall investment budget to AI, suggesting a disconnect between stated priority and actual resource allocation.

The global context provides perspective. Gartner forecasts worldwide AI spending will reach nearly $1.5 trillion in 2025 and exceed $2 trillion in 2026. McKinsey research shows that 92% of companies globally are actively increasing their AI investments. Australian businesses are participating in this surge but, according to some analyses, lag regional peers in adoption pace and investment intensity.

The Australian AI market reached USD 2.07 billion in 2024, with projections suggesting growth to USD 7.76 billion by 2033. This growth trajectory indicates sustained investment over the coming years, but also highlights that Australian businesses are still in relatively early stages compared to global leaders.

Investment is not distributed evenly across sectors or business sizes. Larger enterprises lead adoption, with 90% of medium-sized businesses expected to use AI by 2026 compared to just 41% of micro businesses. This disparity reflects both resource constraints and the complexity of evaluating and implementing AI without dedicated technical capabilities.

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Where are Australian companies prioritising their AI spending?

Spending patterns reveal clear priorities. According to Gartner, data centre systems spending in Australia will see the largest growth, increasing 22.5% to A$10.1 billion in 2026. Within this, server spending is increasing 30% to A$7.7 billion, driven substantially by investments in AI-optimised infrastructure to support generative AI adoption.

At the application layer, spending divides across three categories. Departmental AI, meaning tools for specific business functions like marketing or customer service, captured $7.3 billion globally in 2025. Vertical AI, addressing industry-specific needs, claimed $3.5 billion. Horizontal AI, providing broad capabilities across functions, accounted for $8.4 billion. Australian businesses follow similar patterns, with departmental applications often providing the clearest near-term value.

The practical spending priorities map to specific use cases. Customer service automation continues attracting investment as businesses seek to reduce costs while maintaining service quality. Document processing and automation addresses labour-intensive back-office functions. Sales and marketing AI supports lead generation, personalisation, and campaign optimisation. These areas offer relatively clear ROI calculations and proven implementation approaches.

Infrastructure investments also deserve attention. Many AI applications require improved data foundations, cloud capabilities, and integration frameworks. Organisations that underinvest in these enablers often find their AI initiatives constrained by inadequate underlying systems. Smart investment strategies balance application spending with necessary infrastructure improvements.

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What is the typical AI budget breakdown for mid-market businesses?

Mid-market businesses face distinct investment challenges. They need AI capabilities to remain competitive but lack the resources of large enterprises and the simplicity of small businesses. Understanding typical budget patterns helps organisations benchmark their own investment levels.

Research suggests that only about 20% of organisations qualify as true AI ROI leaders, and these outperformers share common characteristics. Ninety-five percent of AI ROI leaders allocate more than 10% of their technology budget to AI. This threshold appears to represent the minimum commitment required for meaningful capability development rather than superficial experimentation.

Budget allocation typically spans several categories. Technology costs include software subscriptions, cloud services, and infrastructure investments. Implementation costs cover consulting, integration, and customisation work. Change management encompasses training, process redesign, and adoption support. Ongoing costs address maintenance, monitoring, and continuous improvement. Organisations that budget only for technology often underestimate total investment requirements.

For mid-market businesses, starting with bounded investments in proven use cases makes sense. A customer service automation project might require $50,000 to $200,000 depending on complexity and scale. Document processing automation falls in similar ranges. These projects can demonstrate value within months, building confidence and capability for larger investments. The key is matching investment scale to organisational readiness rather than attempting transformative projects before foundations are in place.

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How do successful AI investments differ from unsuccessful ones?

The gap between successful and unsuccessful AI investments is substantial and instructive. Research suggests that 70 to 85% of AI projects fail to deliver expected returns, yet leading organisations consistently achieve significant value. Understanding what differentiates success from failure guides smarter investment decisions.

Successful investments start with business problems rather than technology solutions. Organisations that begin by identifying specific, measurable business challenges and then evaluate whether AI can address them consistently outperform those that deploy AI seeking applications. The question should be "What problem costs us money or limits growth?" rather than "Where can we use AI?"

Data readiness distinguishes successful implementations. AI systems require quality data in accessible formats. Organisations with fragmented data across disconnected systems, inconsistent data quality, or poor data governance struggle to deliver AI value regardless of technology investment. Addressing data foundations before AI deployment often determines project success.

Organisational change capability matters as much as technology capability. AI implementations change how people work, which processes operate, and which skills matter. Organisations that invest in change management, training, and cultural adaptation alongside technology see higher adoption and sustained value. Those that treat AI as purely technical projects often find expensive systems underutilised.

For detailed guidance on calculating AI returns, see our guide to conversational AI ROI.

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What returns are Australian businesses actually seeing?

Return on AI investment varies widely, but the evidence suggests that well-executed projects deliver substantial value. According to research from Menlo Ventures, 74% of executives report achieving ROI from generative AI within the first year. Each dollar invested in generative AI delivers $3.70 back on average, with top performers seeing returns of $10.30 per dollar invested.

The pressure to demonstrate these returns is intensifying. According to Kyndryl research, 61% of business leaders feel more pressure to prove ROI on AI investments compared to a year ago. Investors are particularly demanding: 53% expect positive ROI in six months or less. This compression of expected payback periods reflects both market maturation and the substantial capital already deployed.

Australian businesses report more cautious expectations. KPMG research indicates that 62% of Australian businesses expect to see returns on AI investment within one to three years, suggesting a longer-term perspective than global averages. Whether this reflects conservative Australian business culture, later adoption timing, or more realistic assessment of implementation complexity is debatable.

The most reliable returns come from proven applications with clear metrics. Customer service automation delivers measurable cost reduction and efficiency gains. Document processing reduces labour costs and error rates. Sales enablement improves conversion rates and deal velocity. These applications have established track records and proven implementation approaches, reducing the risk of failed investments.

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How should you build a business case for AI investment?

Building compelling AI business cases requires rigour that matches the scrutiny these investments now receive. Vague promises of transformation no longer satisfy boards and executives demanding evidence-based justification.

Start with specific, measurable objectives. "Improve customer service" is too vague. "Reduce average response time from 4 hours to 15 minutes while maintaining satisfaction scores above 85%" provides a testable claim. Specific objectives enable meaningful ROI calculation and post-implementation evaluation.

Document current state costs thoroughly. Many organisations underestimate what current processes actually cost because those costs are distributed across people, systems, and opportunity costs. Labour costs for manual processes, error rates and their consequences, delays and their business impact, and customer attrition from poor experiences all contribute to addressable costs that AI investment can reduce.

Model benefits conservatively. Vendor claims typically describe best-case scenarios with optimal implementations. Building business cases on median or conservative outcomes provides margin for implementation challenges. If the investment still makes sense with modest assumptions, stronger performance delivers upside rather than disappointment.

Include implementation costs realistically. Technology licensing is often the smallest component of total investment. Integration, customisation, training, change management, and ongoing operation typically exceed software costs. Underfunding implementation almost guarantees underperformance regardless of technology quality.

For understanding broader AI adoption considerations, see our guide to AI adoption for Australian SMEs.

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What mistakes should Australian businesses avoid when investing in AI?

Common mistakes account for much of the high failure rate in AI projects. Understanding these patterns helps organisations avoid repeating them.

Investing in technology before addressing data readiness wastes resources. AI systems require quality data in accessible formats. Many organisations discover after purchasing AI platforms that their data cannot support the intended applications. Assessing and improving data foundations should precede significant AI technology investment.

Pursuing transformation before demonstrating capability creates risk. Large-scale AI initiatives that span multiple functions and require extensive organisational change frequently fail. Successful organisations build capability through bounded projects with clear objectives before attempting transformative implementations. Early wins build confidence, capability, and political support for larger initiatives.

Underinvesting in change management dooms technically successful projects. AI implementations change roles, processes, and skill requirements. Without adequate attention to these human dimensions, even well-designed systems fail to deliver value because people do not adopt them. Change management investment should be proportional to the extent of workflow and role changes the AI implementation requires.

Failing to plan for ongoing improvement leaves value unrealised. AI systems require continuous monitoring, adjustment, and enhancement. Initial deployment is the beginning, not the end. Organisations that budget only for implementation without providing for ongoing improvement see diminishing returns as systems become outdated or misaligned with evolving needs.

For establishing appropriate oversight of AI investments, see our AI governance guide.

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Frequently Asked Questions

What is a reasonable AI budget for a mid-sized Australian business?

Leading organisations allocate more than 10% of their technology budget to AI. For a mid-sized business with a technology budget of $500,000 to $2 million annually, this suggests AI investment of $50,000 to $200,000 or more. However, the right budget depends on strategic priorities, organisational readiness, and available opportunities rather than generic benchmarks.

How long should we expect to wait for AI ROI?

Well-chosen AI projects typically show positive ROI within three to six months. Customer service automation and document processing often deliver returns within weeks. Larger transformation initiatives may take one to three years for full value realisation but should show early wins that validate the investment direction. If you see no positive indicators within six months, reassess the approach.

Should we build AI capabilities internally or work with external partners?

Most mid-sized businesses benefit from external expertise for initial implementations while building internal capability over time. Partners bring experience from multiple implementations and can accelerate time to value. Internal capability becomes more important for ongoing optimisation and for sensitive applications where external involvement raises concerns.

How do we evaluate AI vendors and solutions?

Prioritise solutions with demonstrated results in similar business contexts. Request references from comparable organisations and verify claimed outcomes. Assess integration requirements with your existing systems. Evaluate vendor stability and ongoing support capabilities. Consider total cost of ownership including implementation, training, and ongoing operation rather than focusing on license costs alone.

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Getting Started

AI investment in 2026 demands rigour and strategic clarity. The experimentation phase has passed; boards and executives now expect evidence-based decisions and measurable returns. Organisations that approach AI investment with discipline, starting with clear business problems, ensuring adequate foundations, and building capability through bounded projects, will capture substantial value. Those that continue treating AI as a technology initiative rather than a business transformation will continue experiencing disappointing results.

NFI specialises in helping Australian businesses make smart AI investments. We understand that successful AI requires more than technology selection: it demands strategic alignment, data readiness, implementation capability, and change management. Our team guides organisations from investment planning through implementation and ongoing optimisation, ensuring AI spending delivers measurable business returns.

Ready to ensure your AI investment delivers real value? Contact NFI for a consultation and discover how to build a compelling business case for your organisation.