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Introduction
Once confined to the realm of science fiction, Artificial Intelligence (AI) has seen significant gains in development and practical application in the previous couple of decades. The recent fame of OpenAI’s ChatGPT and DALL-E 2 has demonstrated AI’s increasing sophistication. As the technology matures, the projected economic impact of AI cannot be understated. Like steam power, electricity, and computers before it, the advent of AI is expected to become a new general-purpose technology (GPT) – a technology with a wide scope for application, growth, and economic impact – forming the basis of the fourth industrial revolution (ParkerSoftware, 2022; Howard, 2019). Even now, businesses and institutions are learning to harness AI to automate tasks previously only performed by human workers. For example, AI is being used to automate data entry and processing, analyze medical images, and automate customer service (Vu, 2023). The benefits of integrating AI into business and industry is expected to be significant as AI is used to cut costs, enable a more creative workforce, gain insight into hidden trends, and manage tasks and workflows. It is this concern for the integration of AI and business that is the focus of this article. This article seeks to illuminate AI’s strengths and weaknesses, outline where AI is useful in business, and explore the technology’s disruptive impact in the future of work and leadership.

Background
Artificial intelligence, also known as machine intelligence, is a broad term used to describe a group of technologies capable of performing tasks that approximate human intelligence. AI technologies comprise certain types of hardware and software that include computer vision, machine learning, speech recognition and synthesis, and natural language understanding and processing. Together, these technologies synthesize information, recognize patterns, learn from experience, solve problems, and make decisions (Vu, 2023; Dordevic, 2022).

Artificial intelligence works by processing large datasets and adapting algorithms to find new patterns to solve a problem (SAS, 2017; CSU Global, 2021). This means that AI is iterative, it continually improves by running simulations of a problem, measuring its fitness in solving the problem, and exploiting new patterns to improve efficiency in subsequent generations (SAS, 2017; CSU Global, 2021).

Strengths
In general, AI is very good at performing and automating routine tasks that have clearly defined objectives. Such tasks include the reproduction and synthesis of information using existing inputs, computing data to find new patterns, and formulating recommendations (Benhamou, 2020a). These tasks can be grouped into four broad categories: detection and classification, pattern recognition, prediction, and decision making (Lockridge, 2020).

Weaknesses
While AI performs exceptionally at some tasks, it is constrained by some significant limitations. First, AI cannot produce novel outcomes. Because AI performs tasks within the bounds of predetermined rules and standardized procedures, AI cannot produce any outputs that do not originate from inputs of existing data. Second, AI has trouble guaranteeing quality. Artificial intelligence outputs are dependent on the amount and variety of information fed to it during the training process. However, even large volumes of information do not guarantee optimal or accurate outcomes. This is due to AI’s weakness with generating complexity. Third, AI is limited by significant resource costs. AI requires significant computational processing power, data storage space, large volumes of curated and codified data, and financial investment (Sako, 2020; Benhamou, 2020b).

Implications for Leaders
With us being in the center of an AI craze, we are now questioning whether or not we should adopt AI ourselves. There is no certainty for a positive outcome due to increased investments to AI (Davenport & Mittal 2023). Much of this can be attributed to two factors: time and money. The cost of implementation of AI into a business projects costs ranging from hundreds to million of dollars and significant time for its implementation to even begin for companies. From there the path for a positive return of investment may take years (Sako 2020; Abelmasov 2021). Despite this, we must shift our focus into the best practices of leveraging AI into your business. Many have a misconception of AI as a singular omnipotent entity to solve all issues, but this is simply not true. You must be able leverage AI in your business, not just any business. With that in mind, here are a few tips when to help manage AI’s implementation: