Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries across the UK — from finance and healthcare to manufacturing, logistics, and beyond. Whether it’s powering recommendation engines, optimising business processes, or detecting fraud, businesses applying AI and ML are often pushing the boundaries of what’s technically achievable.
However, applying AI or ML is not enough to qualify for R&D tax relief. To be eligible, your work must go beyond simply using existing models or frameworks. You must be tackling scientific or technological uncertainties to achieve an advancement in technology — for example, by improving model accuracy, solving data processing challenges, or developing algorithms where no off-the-shelf solution is available.
Here are some examples of qualifying activities in AI and machine learning development:
Developing Novel Algorithms or Model Architectures
Engineering bespoke AI models — such as neural networks, decision trees, or natural language processing systems — to solve problems in new or improved ways where standard models are insufficient.
Improving Model Accuracy, Performance, or Reliability
Experimenting with model architecture, training data, or feature selection to improve the performance of AI systems in real-world scenarios, particularly where accuracy or consistency is a technical challenge.
Solving Data Processing or Integration Challenges
Developing methods to clean, process, or structure unlabelled, incomplete, or noisy data — such as audio, video, sensor, or customer interaction data — to make it usable for AI model training.
Engineering Real-Time or On-Device AI Systems
Creating AI solutions that operate in real-time or on resource-constrained devices like smartphones, wearables, or edge computing platforms, where optimising speed and memory usage requires technical problem-solving.
Developing AI Explainability and Transparency Tools
Building systems that make AI decision-making more interpretable and explainable to end-users or regulators, particularly in high-stakes sectors like finance, healthcare, or law.
Enhancing Data Privacy and Model Security
Developing privacy-preserving techniques such as federated learning, homomorphic encryption, or differential privacy to ensure data security during model training or deployment.
Integrating AI into Complex Systems or Workflows
Overcoming technical challenges in integrating AI tools with existing business systems, data pipelines, or customer-facing platforms, particularly when managing latency, security, or scalability.
Meeting UK Regulatory or Ethical Standards for AI
Engineering AI systems to comply with emerging UK and international guidelines on AI safety, fairness, and accountability, particularly where these require technical innovation or validation.
If your business is actively developing or improving AI and machine learning technologies, and overcoming technical challenges in the process, you could be eligible for R&D tax relief. This incentive helps UK companies recover part of their innovation costs, supporting further investment in AI-driven solutions that deliver measurable value.