R&D TAX CREDIT – HOW ARTIFICIAL INTELLIGENCE INCREASES COMPLIANCE – WHILE REDUCING INTERNAL COSTS
INCREASING COMPLIANCE RELIES COMPLETELY ON EMPLOYEES CONDUCTING THE WORK – UNTIL A.I.
Creating nexus or support documents during the evolution of work is a critical part of an R&D claim. Without it, auditors are more likely than not to reject claims since the claimants cannot prove that their activities or time are directly related to QRAs (Qualified Research Activities). Compliance does not stop at QREs (Qualified Research Expenses) or a properly created R&D Study, rather companies must be able to fully support and prove their activities as the work progresses through documented work. This requires additional resources that many companies simply do not have or are unwilling to apply. The result is claim submissions with a lower compliance and a higher risk of rejection.
A.I. Assists Managers, Tracks QRAs, and Creates Nexus Support Documents
Rather than have a manager saddled with the responsibility to track all work and ensure employees are segregating and tracking their activities, an A.I. system can manage this task and reduce the load on internal employees. SHAIN is an example of an A.I. system that can communicate in natural language with teams and document their work for them at a fraction of the cost.
Pinpointing Technical Uncertainties and their Start Date
One important requirement is to identify an eligible project as it begins and initiate tracking the workflow from the beginning. In order to do this, a manager or consultant must be able to recognize the eligible characteristics and initiate a tracking procedure. Otherwise, qualified project expenses (QREs) can be significantly reduced or can be missed all together. SHAIN is designed to track the team at a fraction of the cost of a human manager.
Tracking Technical Uncertainties by A.I.
Once a project starts, the ideas to resolve technical uncertainties must be tracked and tested and the results must be measured and documented. This renders a project compliant with QRAs. Failing to chronologically track the work to resolve technical uncertainties with test records can render projects inadmissible.
Tracking QREs & Timesheets by A.I.
The bulk of most QREs is based on labor. A proper documentation of the team’s activities is the best method to capture labor expenses. However, this relies on the employees’ initiative and usually gets sidelined when other priorities emerge. SHAIN can assist employees by communicating with them and managing their hours.
R&D Process by A.I.
The end result is that A.I. platforms can increase compliance among R&D teams by ensuring a systematic investigation process is followed, meeting the 4 part test criteria. Accomplishing these tasks by an A.I. manager reduces the resulting load on and cost of human managers.