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Artificial intelligence (AI) and machine learning have become industry buzzwords. This article discusses the state of AI and machine learning as they relate to accounting, and the challenges the profession may face moving forward as these technologies start to spread.
AI and Machine Learning
AI, sometimes referred to as cognitive computing, is currently considered a broad category of technologies that can imitate or simulate human behavior. Some AI aim at mimicking human behavior, some aim at surpassing human performance. In a recent article, InfoWorld contributor Kevin Gidney, cofounder of Seal Software, refers to AI as a broad description of any device that mimics human or intellectual functions, such as mechanical movement, reasoning, or problem-solving.1 He defines machine learning as a subset of AI consisting of a statistical and data-driven approach to creating AI, such as when a computer program learns from data to improve its performance. Information technology research firm Gartner defines AI as systems that change behaviors without being explicitly programmed, based on data collected, usage analysis, and other observations.2 Regarding machine learning, Gartner views it as a technical discipline to solve business problems using mathematical models that can extract knowledge from data (as opposed to traditional software engineering, which aims to solve business problems by explicitly defining the software logic). For Gartner, AI employs machine learning, deep neural networks (a variant of machine learning),3 and other technologies to analyze huge amounts of data beyond simple algorithms to achieve new lev- els of performance and insight. Examples of other technologies employed by AI include natural language processing (ability of a system to understand written or spoken human speech), planning systems (ability to find optimal path), and agents and bots (ability to perform complex, repetitive tasks involving interaction among multiple data sources or systems).
In the context of accounting, AI and machine learning technologies - embedded in existing applications or combined with other technologies such as robotic process automation (RPA)4 - are expected to automate a significant part of the mundane tasks performed by CPAs today, such as document and data collection from clients and third parties; document recognition and classification; data extraction from documents and entry into accounting, auditing, tax, or other systems; approvals (such as invoice or expense approvals), confirmations, and reconciliations; computations...