Unsupported assumptions
AI-generated content can appear technically plausible while introducing assumptions, inaccuracies or hallucinations that have no basis in the project evidence.
Downloadable report
Can generative AI be used to create R&D claims?
AI can produce convincing technical content quickly, but a well-written narrative is not the same as an evidence-based R&D claim. This report examines where the risks arise and why specialist oversight remains essential.
Receive your copyWhy convincing language is not enough
R&D claims depend on evidence, not simply how convincing the narrative sounds. They must show what was technologically uncertain, why the uncertainty could not readily be resolved using existing knowledge, and how the team attempted to resolve it. Generative AI cannot establish that evidence, interrogate the people responsible for the work or apply the professional judgement needed to test whether the narrative accurately reflects the project.
Key concerns
The risk of AI-generated claims
Technical narrative drift
A generated explanation may gradually move away from the work undertaken and begin to reflect what the model predicts could have happened rather than what actually occurred.
Disconnect from the evidence
A generated narrative may conflict with project documentation, imply work that was not undertaken or use language the technical team would not recognise or be able to substantiate.
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Receive the full report
Complete the form to receive a copy covering the practical risks of using generative AI within the R&D claim process.
- Where AI-generated narratives can move away from the project evidence
- How subtle inaccuracies can become difficult to defend under scrutiny
- Why inconsistencies may only become visible when the claim is tested against supporting evidence
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