Natural language programming (NLP) is a growing field of Artificial intelligence. NLP can be applied to automate conversations, in order to determine if projects meet the R&D Tax Credit criteria as defined by Internal Revenue Code section 41. This automation includes adherence to the 4-Part Test criteria. The first step in any R&D Study is determining if a project meets the eligible criteria, and if claiming a project will result in a worthwhile financial benefit. Automating this first step allows R&D Tax Credit applicants to increase their efficiency by providing automated assistance to their technical staff. The AI application can provide opinions in determining project eligibility year after year, project by project for existing claimants as well as for new companies who wish to begin benefiting from the R&D Tax Credit program.
At first, practitioners may have difficulty visualizing how a computer program can have a human-like conversation with sufficient knowledge to make the critical decisions in determining if a project can meet the admissibility criteria. In fact, training a person how to determine eligibility is currently the standard practice and conducted at large. Training an AI system applies a similar approach.
When analyzing human conversation flows, AI systems will apply a word vector methodology to identify discussion and decision patterns (converting words into numerical values in a probability model). In the case of a conversation flow to determine R&D Tax Credit eligibility; AI can reapply the patterns to simulate the same conversational flows as they emerge from the training phase.
The big difference maker between training one person and training an AI system, simply put, is SCALABILITY: in-series vs in-parallel. Humans have conversations in-series, one at a time. AI systems have conversations in-parallel: simultaneously, all at the same time. One person can only determine eligibility one conversation at a time. Also, the resulting eligibility opinion of the conversation must be documented for further use, which requires typing, and more time.
A well-trained multi-layered AI system can have millions of conversations at the same time and apply algorithms to rank the project’s eligibility. The AI’s assessment is live and transferred to managers who can then take over (or have the next AI application take over).
As AI systems penetrate the R&D Tax Credit practice, more eligible projects that meet the criteria should result. In addition, less ineligible projects will be submitted. This efficiency will increase the integrity of the program without the high bureaucratic cost of audits and rejections.
As more knowledge about R&D Tax Credit eligibility becomes widespread and easily available through 24/7 AI systems, managers become armed with more precise information to make more informed decisions about claiming only eligible projects.
As you might expect, determining R&D Tax Credit eligibility through a conversational application is only the first step of an AI system. The R&D Tax Credit process can be continued by the AI until it’s entire R&D Study completion. This would include technical interviews, capturing time-sheets, creating support documents, training for best practices and supporting audits.
For more information on Automating R&D Tax Credits by A.I. Systems, visit SHAIN’s R&D Study
If you are an R&D Consultant interested in Partnership programs, visit SHAIN’s Partnership Programs for R&D Consultants.
If you are an Accountant interested in exploring Partnership programs, visit SHAIN’s Partnership Programs for Accountants.
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