APPLYING ARTIFICIAL INTELLIGENCE TO R&D AND SR&ED DEPARTMENTS
Intelligent Automation Systems Are Dramatically Reducing the R&D Tax Credit Compliance Burden
Artificial intelligence represents a growing field of science that has been applied to industry since the 1960s. Up until recently artificial intelligence software programs have been unable to penetrate business operations and deliver higher degrees of efficiency. Although computer programs have dramatically improved business operations, their capabilities have been impeded by hard coding software programming requirements. This means that a computer program can only execute a programmable task, up until now. With the rise of computing power combined with the development of neural network algorithm architectures, new AI systems emerging are not hard-coded. They evolve on their own and can make their own decisions based on training data, creating the capability to automate many tasks never thought automatable before.
AI-based technologies now offer exciting possibilities for businesses to improve process efficiencies, boost control of information, and save money. Legal Departments Implementing AI Systems: One example is how legal departments are complementing the countless manual hours of analyzing documents for legal purposes, now replacing these white-collar tasks with intelligent automation systems. These systems can read through documents and identify complex keyword combinations, categorize content, and perform the preliminary legal work in a mere fraction of the time when compared to current methods creating a higher efficient process.
Human Resources Implementing AI Systems:
Finished are the days where employers sift through hundreds of resumes to screen candidates looking for the candidate with the right qualifications. Candidates no longer have to spend hours searching for job posts as AI systems can conduct this work, sifting through thousands of job posts worldwide in fractions of seconds.
Virtual R&D Departments And AI Systems:
Heavy Manual Time-Loads:
Up until the introduction of intelligent automation systems, tracking the workflow of a company during a product development cycle relies heavily on the employee team to document every phase of its work. This means that test conditions for each design concept must be captured and documented and when employee teams get busy, this is the first task to get sidelined. This information is typically typed by employees in word processors, spreadsheets, ERP or project management systems.
Lost Corporate Knowledge:
These independent documents stay dormant where they are stored until an employee opens a file or runs a report. The information must be manually collected, analyzed, and assessed forming the next round of decisions in the product development cycle. Only the apparent information is most likely to be processed, in many cases leaving the knowledge behind, making transitions among teams difficult.
SR&ED Tax Credits:
Now add the extra layer of complexity of conducting an annual review of scattered documents to pinpoint experimental activities to resolve technological uncertainties, organize and explain the content to accountants and consultants. Furthermore, add independent employee time sheet tracking and linking these hours to the target experimental activities, the risk of audit selection and preparation of support documents, all translating to a significantly high level of manual SR&ED labor. In the current environment of reduced SR&ED budgets and tougher compliance regulations, companies are more apprehensive about claiming SR&ED as they view the work load demands combined with an elevated rejection risk just not worth the long winded journey.
How A.I. is Becoming a Game Changer for SR&ED :
Simply put, A.I systems can significantly reduce the labor involved and increase compliance shifting back the risk – reward ratio to a much more favorable position. An intelligent automation system can screen for eligibility requirements and create an opinion for managers. A.I. will detect keystrokes and document voice transcriptions as employees text or converse with A.I. systems about their R&D work creating a knowledge center that begins compiling scientific support documents in real time. If intelligent enough, an A.I. system can read the work records and write technical narratives in seconds. This eliminates the need for mangers and technical staff to manually read, type, create, search and organize documents.
The labor intensive task of analyzing the critical documentation phase becomes automated. Customized workflow profiles and Natural Language Processing dramatically reduce typing requirements creating detailed analytical documents beyond manual capabilities. A.I can instantly reveal patterns among test results, formulate new knowledge for the team, and create an SR&ED recap, concerns alert, and review agenda all in real time. No more waiting months for people to do all of this.
To Learn more about applying Artificial Intelligence to R&D workflows, visit www.shain.ca