Engineering R&D Strategy with AI: A Deep Dive into a Hypothetical Machine Learning Case Study

If you’re building a predictive AI model, understanding how the R&D Tax Incentive applies in practice is crucial. The business.gov.au hypothetical machine learning (ML) case study offers a clear, technical example of what a compliant AI claim can look like.

Let’s unpack it — and show how you can apply those lessons to your own AI projects.

🚜 The Scenario: Precision Irrigation with ML

In this case, a company builds an Irrigation Decision Support System (IDSS). Existing systems were inaccurate because they relied on soil moisture sensors and standard weather data. The company decided to incorporate satellite hyperspectral imagery and real-time weather inputs to improve predictive accuracy.

They identified real technical uncertainty:

No public research showed clear correlations between satellite variables and moisture outcomes
Off-the-shelf ML models had limitations in sparse, heterogeneous data environments

🧪 Core R&D: Hypothesis → Experimentation → New Knowledge

The project followed a structured R&D process:

Conducted literature and benchmark reviews to confirm no existing solution worked in this context
Formulated a hypothesis: that a variable relevance framework would better select predictive features
Developed and tuned random forest regression models using satellite + weather data
Tuned hyperparameters and compared feature importance across iterations
Evaluated results to draw technical conclusions on model efficacy — not business outcomes

🧩 Supporting R&D: Linkage Matters

The case also documents supporting activities required to deliver the core experimentation:

Data research and acquisition
Dataset curation and validation
Infrastructure setup (hardware/software)
Training/debugging pipelines
Documentation for test protocols and iteration outcomes

These were directly tied to the core experiment and conducted with the dominant purpose of supporting it — a necessary condition for supporting activity eligibility.

📋 Self‑Assessment & Registration: A Practical Workflow

Following the R&DTI self-assessment steps at year-end, the company:

Sorted eligible activities by core and supporting type
Confirmed evidence of technical uncertainty, systematic approach, and new knowledge
Registered both sets of activities through the DISR portal
Nested cost attribution for salaries, data acquisition, compute services, and external consultancy
Maintained contemporaneous records to support each activity

✅ Key Takeaways for Your AI Project

Define your ML hypothesis upfront:

Frame your R&D claim around a technical question, not a business goal (e.g. prediction thresholds, model architecture limits).

Structure your experimental design:

Use DoE-like approaches: benchmarks, ablation studies, hyperparameter sweeps, and iteration logs.

Document all reasoning and process:

Board notes, code notebooks, email exchanges, literature reviews, and failed tuning runs are often more valuable than polished model summaries.

Tag your costs to specific experiments:

Use project-level cost tracking — ideally in your finance system and time-tracking tools — to map spend directly to R&D activities.

Identify core from supporting R&D:

Only claim support costs when they truly enable the core work and when the dominant purpose test is met.

🧭 Why This Matters

Machine learning is appealing, but many AI projects don’t satisfy the legislative thresholds for R&D funding because they lack documented uncertainty, hypothesis framing, or structured experimentation.

By aligning your AI project with the core and supporting requirements — as illustrated in the ML case study — you can demonstrate your eligibility and technical sophistication.

🤝 How Noah Connect Can Support Your AI R&D Strategy

We help data-led companies and ML teams to: