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 hardcoded. 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 keywords, 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.
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.
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.
These independent documents stay dormant where they are stored until an employee opens a file or runs a report. The information is collected, then analyzed, and assessed and 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 only with employees making transitions among teams difficult.
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 timesheet 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.
Every company says they have the most effective products and processes yet few can demonstrate their R&D Departments to their customer bases. High resources would be required to compile a detailed Corporate Annual R&D Report to supply their sales teams.
An intelligent automation system detects keystrokes as employees conduct their R&D work creating a knowledge center that begins compiling scientific documents in real time. This eliminates the need for employees to manually create, search and organize test results, followed by a labor intensive task of performing analysis and the critical documentation phase. Customized workflow profiles and Natural Language Processing dramatically reduce typing requirements creating detailed analytical documents beyond manual capabilities. A virtual R&D center will instantly reveal patterns among test results formulate new knowledge for the team, and create an SR&ED review agenda in real time.
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