Using an AI model or technique that is new to you does not, by itself, mean the activity is eligible for the program. For an AI-related activity to be eligible as a core R&D activity, it must aim to resolve a technical hurdle where a proposed solution (the hypothesis) is tested through planned experiments.
For example, while the following activities may be complex, they are unlikely to meet the requirements of core R&D activities because they can generally be addressed by experts in the field using existing knowledge and solutions:
- implementing logging, alerts, dashboards, or routine performance checks using established methods and tools to confirm expected model behaviour
- cleaning, formatting, and aligning data to meet a model’s documented input requirements using standard data preparation techniques, where the transformations required are known in advance
- running regression, acceptance, or functionality tests using established testing methods to confirm a system works as intended, where expected outcomes are already known
- adjusting or fine-tuning parameters (e.g. learning rate) using established methods when it is known that the issue can be solved by parameter changes and the effect of changing the parameter is well understood
- integrating known model outputs (such as scores, labels and recommendations) into applications or dashboards, using pre‑defined logic, rules and interfaces, where the behaviour of the model and the integration approach are established.
AI‑related activities are eligible R&D activities when they meet the requirements for core R&D activities or supporting R&D activities. These requirements do not depend on the technology used. As such, using AI in software development does not make an activity eligible.
AI-related activities may meet the requirements of a core R&D activity where a technical hurdle exists and an expert in the field considers that only experimentation will determine if a proposed solution, or the way to develop a solution, can resolve the technical hurdle.