AI R&D Credit

Many companies mistakenly believe the R&D tax credit is only for "groundbreaking" or revolutionary AI breakthroughs. The reality is the incentive is designed to reward the day-to-day process of experimentation used to build, train, and refine machine learning models and AI-driven systems.

Eligibility is built directly into the AI development lifecycle. It rewards the systematic process of designing, training, and testing to resolve technical uncertainty—whether that involves selecting the optimal model architecture, developing a complex algorithm to improve accuracy, or ensuring the AI can scale to meet performance requirements.

Qualifying Activities Throughout the AI Lifecycle:

Core Principles of AI R&D
This focus on process, rather than breakthroughs, is defined by these key concepts.

The credit rewards the day-to-day process of experimentation used to build, train, and refine machine learning models, not just "groundbreaking" breakthroughs.
Eligibility is built directly into the AI development lifecycle and focuses on resolving technical uncertainty—such as finding the right architecture, improving algorithm accuracy, or ensuring the system can scale.
The incentive applies to the systematic process of designing, training, and testing to overcome technical challenges, regardless of the final product's market success.

Design & Data Engineering Activities
Qualifying work is found in the foundational planning, design, and data preparation stages.

Analyzing technical requirements for new predictive features and designing the structural architecture of the necessary data pipelines.
Evaluating technical alternatives for model selection, such as comparing the suitability of neural networks versus random forest models for a specific task.
Developing and testing new methods for data collection, cleansing, and feature engineering to resolve data-related technical challenges.

Model Training & Validation Activities
This process of experimentation carries through the core implementation and iterative testing phases.

Iteratively training models and systematically tuning different hyperparameters (e.g., learning rates, layer sizes) to resolve uncertainty and improve model performance.
Compiling and testing the underlying software code that supports the AI system, including APIs and integration points.
Conducting rigorous validation, performance, and load testing to check for issues like model drift and ensure the system meets its technical requirements for speed and accuracy.

Ultimately, these common, iterative development activities represent the core of qualifying R&D and can translate into significant tax savings.