R&D Tax Incentive for AI Development: Funding Your AI Projects in Australia

Note: The following content is for informational purposes, whilst we make efforts to keep our content up-to-date exact figures and requirements may change from time to time.

Building AI is expensive. Whether you’re training custom machine learning models, developing intelligent automation systems, or architecting AI-driven SaaS platforms, the costs add up fast, and for many Australian businesses, that’s where genuine innovation stalls.

Here’s what a lot of AI founders and technical teams don’t fully appreciate: a significant portion of those development costs may be recoverable through the Australian Government’s R&D Tax Incentive (RDTI). When structured correctly, eligible AI projects can attract a tax offset of up to 43.5 cents on the dollar, money that flows back into your business and accelerates the next phase of development.

At AGR Technology, we work with Australian businesses as their technical implementation partner, helping them design, build, and document AI and software projects in a way that holds up to scrutiny. We’re not tax advisors or registered grant consultants, but we know how to structure technically complex innovation projects and collaborate with the specialists who handle the compliance side.

This guide breaks down how the R&D Tax Incentive applies to AI development, which activities qualify, what costs are claimable, and how to combine the RDTI with AI development business grants to maximise your funding position.

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What the R&D Tax Incentive Means for AI Companies in Australia

The R&D Tax Incentive is one of the most significant funding mechanisms available to Australian technology companies. Administered jointly by AusIndustry (now part of the Department of Industry, Science and Resources) and the ATO, it’s designed to encourage businesses to invest in genuine innovation, and AI development sits squarely in the program’s sweet spot.

For AI companies, the RDTI isn’t a grant in the traditional sense. It’s a tax offset, meaning you claim it after incurring eligible expenditure, either reducing your tax liability or, for smaller companies, receiving a cash refund. The distinction matters, but the outcome is the same: real capital returned to your business to fund continued development.

How the RDTI Works: Offsets, Rates, and Thresholds

The RDTI operates on a tiered offset structure. Companies with an aggregated annual turnover of less than $20 million are eligible for a refundable tax offset of 43.5% on eligible R&D expenditure, meaning even pre-revenue AI startups can receive a cash refund at tax time. For companies with turnover above that threshold, a non-refundable offset of 38.5% applies, which reduces the company’s tax payable.

To access the program, companies must register their R&D activities with AusIndustry each year within 10 months of the end of their income year. The minimum eligible expenditure threshold is $20,000, though companies spending less may still be eligible if they use a Research Service Provider. There’s also a $150 million cap on R&D expenditure eligible for the offset, a ceiling that won’t concern most early-stage AI companies but is worth knowing.

One nuance that catches teams off guard: the offset applies to net eligible expenditure, so any government grants or recoupments received for the same activity reduce the amount you can claim. This is where coordination between your grant strategy and your R&D claim becomes critical.

(Source: ATO & Business.gov.au)

Why AI Development Is a Strong Fit for the RDTI

AI development is inherently experimental. Training a machine learning model doesn’t come with a guaranteed outcome, you form a hypothesis, design experiments, iterate on architectures, and test results against benchmarks. That process of systematic investigation and refinement is exactly what the RDTI was built to reward.

Unlike off-the-shelf software implementation, genuine AI development involves unknown outcomes at the outset. You don’t know whether a particular model architecture will achieve target accuracy. You don’t know if your training data pipeline will produce reliable results at scale. Those unknowns are the technical core of an R&D claim.

Activities like developing novel algorithms, training and evaluating ML models, building proprietary AI infrastructure, and researching the application of AI to domain-specific problems all have strong alignment with the RDTI’s core eligibility criteria. When structured and documented correctly, these activities can form the backbone of a compliant and defensible claim.

Which AI Activities Qualify for the R&D Tax Incentive

Not every line of code qualifies, and not every AI project will have an eligible R&D component. The RDTI has specific criteria that activities must meet, and understanding those criteria upfront shapes how you design, document, and execute your development work.

Core R&D Activities: Novelty, Hypothesis, and Systematic Experimentation

Core R&D activities must satisfy four key criteria under the Income Tax Assessment Act 1997:

1. New knowledge, the activity must be aimed at generating knowledge that doesn’t currently exist in the public domain.
2. Hypothesis, there must be a clear, testable hypothesis underpinning the experimental work.
3. Experimentation, the activity must involve systematic, scientific-style experimentation.
4. Unknown outcome, the result cannot be knowable in advance by a competent professional in the field.

For AI companies, qualifying core activities typically include:

• Developing novel machine learning architectures for specific use cases
• Researching and testing approaches to training data curation and augmentation
• Designing proprietary natural language processing (NLP) or computer vision pipelines
• Investigating reinforcement learning approaches for complex decision-making problems
• Building and evaluating AI models where performance thresholds are not guaranteed

The key phrase that trips up many claimants is “unknown outcome.” If a competent AI engineer could reliably predict the result using existing knowledge and techniques, the activity may not qualify as core R&D. But if you’re pushing into territory where standard methods fail or haven’t been applied to your specific domain, that’s where genuine core R&D lives.

Supporting R&D Activities Tied to AI Projects

Supporting R&D activities are those that directly enable the core R&D work, they don’t need to be experimental in themselves, but they must have a necessary relationship to the core activity. For AI projects, this can include a meaningful range of development work:

• Data collection and preprocessing infrastructure built specifically to support model training
• Development of evaluation frameworks and testing environments for ML experiments
• Software engineering work that creates the environment in which core AI research is conducted
• Annotation and labelling workflows designed to feed experimental models

Supporting activities are governed by a dominant purpose test, the work must be undertaken for the dominant purpose of supporting core R&D. If the infrastructure or tooling would have been built anyway for production use, that weakens the supporting activity case. The framing and timing of the work matters.

AI Activities That Do Not Qualify

It’s equally important to understand what’s excluded. The ATO has been increasingly specific in its guidance around software and AI claims, and the following activities are generally not eligible:

• Integrating existing third-party AI APIs (e.g., calling OpenAI, Google Vertex, or AWS SageMaker without novel research)
• Fine-tuning pre-trained models using standard techniques for commercial deployment
• Software development that follows established methods with predictable outcomes
• Market research, customer discovery, or business case development
• Production deployment, maintenance, or user acceptance testing
• Administrative and compliance activities

None of this means you can’t build commercially valuable AI products, it just means the commercial parts of the build are separated from the experimental parts for RDTI purposes. A well-structured project keeps those activities distinct and documented accordingly.

Eligible Costs You Can Claim for AI R&D Projects

Once eligible activities are established, the next question is what expenditure can be claimed against them. The RDTI covers a wider range of costs than many businesses initially expect, and for AI development, the claimable cost base can be substantial.

Staff, Contractors, and Overseas Expenditure

Labour is typically the largest cost category in any AI development project, and the RDTI allows you to claim:

• Salaries and wages for employees who directly conduct eligible R&D activities, apportioned to the percentage of their time spent on qualifying work
• Superannuation contributions associated with those salary costs
• Contractor fees paid to Australian entities for eligible R&D work, where a written agreement exists and the contractor doesn’t also lodge their own R&D claim for the same work

Overseas expenditure is a more nuanced area. By default, expenditure incurred overseas doesn’t qualify. But, there is a mechanism, the Overseas Finding, that allows foreign expenditure to be included if the R&D activity cannot be conducted in Australia due to genuine lack of facilities, expertise, or regulatory requirements, and the work isn’t conducted for more than a certain period. This is particularly relevant for AI companies accessing international specialist expertise or proprietary datasets.

For any labour costs, the critical administrative requirement is timesheets or time tracking records that substantiate the proportion of time spent on R&D versus non-R&D activities. Estimates and reconstructed records after the fact are a significant audit risk.

Cloud Computing, Data, and Infrastructure Costs

This is an area of growing importance for AI development claims, and the ATO has issued specific guidance on how cloud costs are treated.

Eligible cloud and infrastructure costs include:

• Compute costs (GPU/CPU instances) used directly for model training and experimentation
• Storage costs for datasets, model checkpoints, and experimental outputs
• Data acquisition or licensing costs, where the data is used specifically for eligible R&D activities
• Software tools or platforms used exclusively in the conduct of R&D experiments

The key distinction is direct use in eligible R&D activities. Cloud costs associated with production infrastructure, staging environments, or general business operations don’t qualify. For AI teams running experiments in the same cloud environment as production workloads, cost allocation and tagging becomes a critical compliance practice, not just good engineering hygiene.

Depreciation on dedicated R&D equipment can also be claimed in some circumstances, and costs paid to a Research Service Provider (RSP) for contracted R&D work are treated differently but may unlock access to the program even below the $20,000 self-expenditure threshold.

AI Development Business Grants and Other Funding for AI Development

The RDTI is powerful, but it’s retrospective, you spend first, then claim. For early-stage AI companies or those undertaking large-scale innovation projects, combining the RDTI with upfront AI development business grants creates a far more sustainable funding model.

Government Grants Available to Australian AI Founders

Several Australian Government grant programs are relevant to AI founders and businesses developing with AI technology:

Entrepreneurs’ Programme (Accelerating Commercialisation): Provides matched funding of up to $1 million for businesses commercialising novel products or services. AI-driven platforms and proprietary technology solutions are common applicants.

Export Market Development Grants (EMDG): Relevant for AI companies with international ambitions, generally provides reimbursement of up to 50% of eligible export promotion expenses.

Industry Growth Program: The successor to the Entrepreneurs’ Programme Business Programs, supporting businesses investing in advanced manufacturing, critical technologies, and deep tech, including AI and machine learning applications.

State-based grants: Programs such as the Victorian Government’s Digital Future Now initiative, NSW’s AI Strategy-linked funding rounds, and Queensland’s various tech sector initiatives regularly open funding for AI development projects. These are often faster-moving and less competitive than federal programs.

ARC Linkage Grants: For businesses partnering with universities on AI research, the Australian Research Council’s Linkage Program can fund collaborative R&D projects with co-investment from industry partners.

The grant landscape shifts regularly, programs open, close, and evolve. Working with a registered grants specialist alongside your technical implementation team keeps you positioned to move quickly when relevant programs open.

Stacking Grants With the R&D Tax Incentive

It’s possible, and often strategic, to access both government grants and the RDTI for the same project. But, the interaction rules between them need careful management.

The core rule: any government grant or recoupment received for R&D activities reduces the eligible expenditure you can claim under the RDTI by the same dollar amount. So if you receive a $200,000 grant for an eligible AI project and spend $500,000 on that project, your net eligible R&D expenditure is reduced to $300,000.

That still represents a meaningful RDTI claim, and the net funding position (grant + offset) can be significantly better than pursuing either mechanism in isolation. The key is structuring grant applications and R&D registrations in a way that maximises the combined return without inadvertently reducing your RDTI base more than necessary.

This is why early coordination between your technical team, your grant specialist, and your tax advisor matters. At AGR Technology, we work alongside those specialists to ensure the technical scope and documentation of your AI project supports both the grant application and the subsequent R&D claim.

Proving Your Claim: Documentation and Compliance for AI R&D

The quality of an R&D claim is only as strong as the documentation that supports it. The ATO’s compliance activity in the R&D space has intensified in recent years, particularly for software and AI-related claims, and underdocumented claims are increasingly subject to reviews, audits, and in some cases, reassessment.

Records You Need to Keep for AI and Machine Learning Projects

For AI and ML projects, robust contemporaneous documentation should include:

• Research plans and experimental design documents, written before work begins, articulating the hypothesis, the unknowns, and the proposed experimental approach
• Experiment logs and iteration records, records showing what was tested, what the results were, and how findings informed subsequent decisions
• Model performance benchmarks and evaluation reports, demonstrating that outcomes were uncertain and measured systematically
• Time tracking records, apportioning staff time to specific eligible activities
• Cloud cost allocation reports, linking compute and storage costs to specific R&D workloads
• Project meeting notes and technical decision logs, contemporaneous evidence of the systematic process
• Version control history, git logs and commit histories that support the timeline of development activity

This documentation doesn’t need to be created specifically for the ATO, good engineering practice naturally produces much of it. But it does need to be preserved and organised in a way that can be produced if the claim is reviewed.

Common Mistakes AI Companies Make When Claiming the RDTI

Several patterns consistently undermine AI R&D claims:

Claiming routine development as R&D. Integrating a third-party AI API, configuring an existing ML framework, or deploying a known algorithm to a new dataset, these are common activities but rarely constitute core R&D. The experimental component needs to be genuinely novel and uncertain.

Not separating R&D from production work. If your team is simultaneously building the experimental AI model and the production application that will use it, the R&D activities need to be clearly delineated in documentation and cost allocation. Blended records are a compliance risk.

Reconstructing records after the fact. Documentation created retrospectively, even if accurate, carries significantly less evidentiary weight than contemporaneous records. Compliance frameworks need to be built into the project process from day one.

Missing the registration deadline. R&D activities must be registered with AusIndustry within 10 months of the end of the income year. Missing this window means losing the claim for that year entirely, no exceptions.

Overclaiming supporting activities. The dominant purpose test for supporting activities is applied strictly. Activities that have a dual purpose (both R&D and commercial production) generally don’t qualify in full.

Working with an experienced technical partner who understands how to structure AI projects for both compliance and performance makes a material difference to the strength and defensibility of your claim.

Turning R&D Tax Compliance Into a Competitive Advantage

Most businesses treat the R&D Tax Incentive as a compliance exercise, something to tidy up at tax time with their accountant. The companies extracting the most value from it treat it as a strategic instrument.

When you design your AI development projects with R&D eligibility in mind from the beginning, several things happen. Documentation improves, and better documentation produces better engineering. Experimental scope is clearly defined, which reduces scope creep and improves technical focus. Cost allocation becomes disciplined, which gives leadership better visibility into where innovation spend is actually going.

And the capital returned through the offset gets recycled into the next development cycle faster, compounding the impact of each investment in innovation.

For AI companies competing in markets where the pace of model improvement and feature development is a differentiator, that reinvestment cycle matters. A 43.5% refund on eligible expenditure isn’t a minor accounting adjustment, on a $500,000 AI development budget, that’s over $200,000 returned to reinvest in your next innovation sprint.

The businesses we work with at AGR Technology often find that a well-structured AI project, one built with technical rigour, strong documentation practices, and clear separation between experimental and commercial work, produces both a better product and a stronger R&D claim. Those goals aren’t in tension. They reinforce each other.

Beyond the financial return, there’s a credibility dimension. Companies that can demonstrate a disciplined, hypothesis-driven approach to AI development are better positioned when raising capital, responding to enterprise procurement requirements, or entering regulated markets where technical governance matters. R&D documentation isn’t just for the ATO, it’s evidence of how seriously you take your own innovation process.

If you’re building AI products and haven’t yet mapped your development roadmap against RDTI eligibility criteria, that’s a conversation worth having. The funding that’s available, through the tax offset, through AI development business grants, and through the combination of both, represents a real competitive lever for Australian AI companies willing to structure their projects intentionally.

Conclusion

The R&D Tax Incentive for AI development is one of the most accessible and substantial sources of innovation funding available to Australian businesses, but it rewards companies that approach it with clarity and rigour. Eligibility isn’t automatic. It’s earned through genuine experimental work, disciplined documentation, and accurate cost allocation.

The opportunity is significant. Between the RDTI’s 43.5% refundable offset for eligible small companies, the range of government grants available to AI founders, and the ability to strategically stack both funding mechanisms, Australian AI businesses have more tools to fund deep technical work than many realise.

At AGR Technology, we act as the technical implementation partner for AI and software projects funded through these mechanisms, helping businesses design and execute innovation projects that are technically robust, commercially focused, and built with compliance in mind. We work alongside registered tax advisors and grant specialists, not in place of them, ensuring that your project is supported by the right expertise at every stage.

Frequently Asked Questions

What AI development activities qualify for the R&D Tax Incentive in Australia?

Qualifying AI activities include developing novel machine learning architectures, building proprietary NLP or computer vision pipelines, researching training data curation, and investigating reinforcement learning approaches. The activity must involve a testable hypothesis, systematic experimentation, and an outcome that isn’t knowable in advance by a competent professional in the field.

How much can Australian AI companies claim under the R&D Tax Incentive?

AI companies with aggregated annual turnover under $20 million can access a refundable tax offset of 43.5% on eligible R&D expenditure — meaning even pre-revenue startups can receive a cash refund. Companies above that threshold receive a non-refundable 38.5% offset. On a $500,000 AI development budget, that’s potentially over $200,000 returned.

Can AI development costs like cloud computing and GPU usage be claimed under the RDTI?

Yes. Eligible cloud and infrastructure costs include GPU/CPU compute instances used for model training, storage for datasets and model checkpoints, and data licensing costs tied to R&D activities. However, costs linked to production infrastructure or general business operations don’t qualify — accurate cost allocation and cloud resource tagging are essential for compliance.

Can you stack government grants with the R&D Tax Incentive for AI projects?

Yes, combining grants and the RDTI is a common and strategic approach for AI companies. However, any grant received for an eligible R&D activity reduces your claimable RDTI expenditure by the same dollar amount. Careful coordination between your technical team, grant specialist, and tax advisor is critical to maximize the combined funding return without unnecessarily eroding your R&D claim base.

What documentation is required to support an AI R&D Tax Incentive claim?

Strong documentation for AI R&D claims includes pre-work research plans articulating hypotheses, experiment logs showing iteration and results, model performance benchmarks, staff time-tracking records, cloud cost allocation reports, and version control history. Contemporaneous records carry far greater evidentiary weight than records reconstructed after the fact.

Does fine-tuning a pre-trained AI model qualify as R&D for tax incentive purposes?

Generally, no. Fine-tuning pre-trained models using standard techniques for commercial deployment is typically excluded from R&D Tax Incentive eligibility, as is integrating third-party AI APIs like OpenAI or Google Vertex without novel research. Eligibility requires genuine technical unknowns and experimental investigation beyond established methods.

Source(s) cited:

[Online]. Business.Gov.Au. Available at: https://business.gov.au/grants-and-programs/research-and-development-tax-incentive/overview-of-rd-tax-incentive (Accessed: 19 February 2026).

[Online]. Available at: https://www.ato.gov.au/businesses-and-organisations/income-deductions-and-concessions/incentives-and-concessions/research-and-development-tax-incentive-and-concessions/research-and-development-tax-incentive/rates-of-r-d-tax-incentive-offset (Accessed: 19 February 2026).

[Online]. Business.Gov.Au. Available at: https://business.gov.au/grants-and-programs/research-and-development-tax-incentive/check-if-you-are-eligible-for-the-randd-tax-incentive (Accessed: 19 February 2026).

Attention Required! [Online]. Cloudflare. Available at: https://www.bentleys.com.au/resources/what-is-an-rd-tax-incentive-grant-and-how-do-australian-businesses-qualify/ (Accessed: 19 February 2026).

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