Artificial intelligence is changing how companies manage research and development (R&D) tax credits. AI can make R&D work faster and smarter, but it still relies on the experts guiding it. The companies that get this balance right are the ones seeing real benefits.
The Power of Good Documentation
Before diving into AI, it is worth revisiting the backbone of every successful R&D claim: documentation. The IRS focuses heavily on showing that projects meet the four-part test:
- a defined business goal,
- a technological foundation,
- genuine uncertainty, and
- a process of experimentation.
That final element, experimentation, is where most claims rise or fall. It is also where the story of innovation lives. Detailed records of testing, failed attempts, and adjustments reveal how teams worked through uncertainty to reach a solution.
Documentation can take many forms. Beyond test plans or design documents, even chat messages between engineers can demonstrate technical uncertainty. Digital project management tools have become especially valuable, capturing iterations and conversations automatically. When a project takes twice as long as expected or shifts directions midstream, those records can make a strong case that experimentation occurred.
Good documentation is not about quantity; it is about clarity. The goal is to make it easy to show what happened and why.
Where AI Fits In
AI has quickly become one of the most talked-about tools in the tax and technology world. It can help organize project data, flag missing documentation, and even sort potential qualifying activities. For companies with large volumes of technical projects, that efficiency can be transformative.
But AI in it’s current form isn’t much more than a probabilistic language model. It can’t ‘think’ for itself, so it only performs as well as the data and direction it receives. Without expert oversight, it can misinterpret context or produce misleading results. One example involves an AI model that flagged three types of screws as proof of experimentation when all three were simply part of one design.
At the same time, AI can help filter out clearly non-qualified activities and identify areas worth deeper review. The key is to treat it as an enhancement, not a replacement. Think of it as a skilled assistant that works quickly and tirelessly but still needs supervision from someone who understands the bigger picture.
The Importance of Human Oversight
AI models are constantly evolving, which means companies cannot “set it and forget it.” As data shifts and models update, results can degrade over time. Teams must adjust prompts, retrain models, and periodically review performance to maintain accuracy.
This ongoing attention separates success from disappointment. Many organizations that expected major efficiency gains have found that their AI projects failed because they lacked flexibility and human involvement. The most effective analyses pair AI’s speed with expert review at every step.
When AI Work Qualifies as R&D
As businesses explore AI, many ask whether building or integrating these tools counts as R&D. The answer is: sometimes.
Integrating AI into existing systems often qualifies when it involves overcoming technical uncertainty, as opposed to a “plug and play” implementation of an existing technology. Meanwhile, work such as prompt engineering—teaching AI to produce better results through language and structure—remains a gray area. The IRS has compared it to early search engine optimization, which it did not view as technological in nature.
The best approach is to document every phase of AI-related development: the challenges, tests, iterations, and technical hurdles. If a project involves real uncertainty and systematic problem solving, it may qualify under existing R&D standards.
AI and the Future of R&D Credits
When used effectively, AI can give R&D teams the time and insight to focus on what matters most: complex, uncertain projects that drive real innovation. It can improve consistency, uncover inefficiencies, and accelerate reviews. What it cannot do is replace the nuanced judgment of tax and technical professionals.
The next evolution of R&D credit work will not be purely digital or purely human. It will be both—where AI handles the data and humans handle the decisions.
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