The ascent of AI has transformed the accounting profession into an incubator for opportunity, innovation and creativity. While firms once favoured humans for repetitive tasks like data-processing, they now look for multi-dimensional talent that can interact with and contribute to technology that will accelerate workflow.
CPAs are present throughout Ontario’s workforce: from the music and movie industries to sports management, and from small tech start-ups and big tech giants. And thanks to the applications of AI that can now take on repetitive tasks, CPAs have more time to work with the engineers and data scientists to design and implement technology that will further the profession.
One exciting recent example of CPAs using AI is Erin Kelly, CPA, CMA, and CEO of Advanced Symbolics Inc., whose AI technology correctly predicted the Brexit vote. As an educator, CPA Ontario recognizes the need for the accounting curriculum to evolve to support the desire of emerging CPAs to understand how new technology is accelerating the profession, and the demands of employers to hire talent that can contribute to the growth of the business. In fact, many of our own professional development offerings are evolving to infuse technology and AI components.
As for the application of AI on accounting-specific tasks, Natural Language Processing (NLP) and Machine Learning (ML) are the two most dominant technologies used, because of their ability to quickly analyze and categorize large amounts of data.
Further information about these applications, and other disciplines of AI can be found below:
Natural Language Processing (NLP):
NLP gives computers the ability to read, listen to and classify human words and phrases. NLP can extract and analyze language from within massive data sets of both structured (financial statements) and unstructured (emails, phone calls, social media) data. It’s what powers virtual assistants like Siri and Alexa.
Machine Learning (ML):
ML algorithms develop and improve themselves through observation and trial-and-error, similar to the way humans learn. After being trained, ML can learn on its own by recognizing patterns in data. It can make decisions based on the outcomes of similar situations it has observed before.
Robotics is the science and engineering that makes machines autonomously perform specific manual tasks. In industrial and commercial settings, robots are involved in assembly lines and, in Amazon’s warehouses, for transporting products from shelves to checkout. At home, robots like the Roomba can vacuum.
Knowledge Representation and Reasoning (KRR):
KRR is a way for machines to represent and understand concepts, the relationships between concepts, and the rules in which concepts interact and behave. KRR is not about computers storing mass data, but about actually understanding what data means and what to do with it to draw conclusions.
Vision gives machines the ability to process, understand and categorize images and alphanumeric characters. Computer vision is used in facial recognition, in document analysis and fraud detection, in the identification and censoring of explicit or illegal content, for tracking objects, and in autonomous vehicles and robots.
Planning is the science of devising a hierarchical sequence of events to complete a predefined task, including also re-planning a sequence to account for unexpected variables. Planning currently has applications in production lines, construction, military and air campaigns, and robotics.
AI allows CPAs to focus more of their time developing richer insights, and less time on routine tasks. It means CPAs can do less of the repetitive work and spend more time developing insights and analyses.
LEARN MORE ABOUT HOW AI IS TRANSFORMING THE PROFESSION.