The time for artificial intelligence (AI) has finally come. Machines are performing as superhumans, real-world applications are powered by AI, and companies across different sectors are using the power of AI in their operations. Embracing AI promises substantial benefits for businesses and economies by contributions to innovations and productivity growth. Simultaneously, the impact of AI on work is likely to be intense. Some skills will decline, while others grow or change. As studies suggest, AI will create more jobs than it will lose, but the transition and skill requirements will shift significantly. Furthermore, the potential of AI to contribute to societal challenges, for example, healthcare or climate change, is huge. Given these above-mentioned impacts, businesses and economies should embrace AI. In this article, we will further explain the benefits, limitations, challenges, and impact of AI on different segments of the economy.
The progress of AI
Since the term ‘artificial intelligence’ came up, periods of significant scientific advances started. Despite these periods, AI has often failed to perform the hype that surrounded it. Researches spent decades trying to describe human intelligence. However, since the past decade, technological advancements have made progress possible. The development of machine-learning algorithms started based on deep learning and reinforcement learning techniques. At the same time, computing capacity grew exponentially making it possible to train large and complex models. After many more of these innovations and scientific breakthroughs, machine learning techniques have been developed to solve specific problems. AI has been the result of progress in the field of deep learning, a set of techniques to implement machine learning that is based on artificial neural networks. Machine learning consists of several types: supervised, unsupervised, and reinforcement learning, depending on the specific use case.
AI still faces some practical challenges. AI solutions and deep learning techniques are often seen as the “black box”, people do not exactly know how the algorithm arrived at its prediction. To solve this problem, users of AI solutions must know in advance how an algorithm arrived at its particular outcome. Explainable AI can help to overcome the “black box” complexity by showing which factors led to a certain outcome or prediction, and how.
Businesses are starting to implement AI in their operations leading to impressive results. AI can be used to enhance business performance in areas including predictive maintenance and is able to optimize the routing of delivery traffic, improving fuel efficiency and reducing delivery times. In real estate, AI has become a valuable tool to optimize asset management and maximize portfolio returns. In sales, customer demographic data and past transactions can help to provide personalized recommendations.
Such actual AI use cases and applications can be found in all segments and sectors of the economy, including all different business functions from sales to supply chain operations. According to several studies by McKinsey, deep learning techniques adds an impressive amount of value by improving traditional analytics techniques. Based on their analysis across 19 different industries and nine business functions, AI improved on traditional analytics techniques in 69 percent of potential use cases. In addition, deep learning techniques based on artificial neural networks could generate as much as 40 percent of the total potential value that all analytics techniques could provide by 2030.
Even though many businesses are starting to implement AI, the extent of adoptions has been relatively small. Only several businesses have adopted AI in certain parts of their business and a few integrated AI across their full enterprise workflows. Heavily digitalized companies are more investing in AI with all the good results. AI-adopted companies derive greater value and acquire more data creating a gap between adopters and laggards. Many companies lag in adopting AI even the clearly defined benefits. Companies face challenges with developing an AI strategy, lacking ownership and commitment to AI, lack robust data capture and digital technologies, and face the challenge to overcome the “last mile” problem, where the insights provided by AI models should be incalculated into the companies’ behavior.
Benefits for economies
Besides the benefits AI can provide for businesses, AI creates a positive impact for economies in terms of increased innovation and productivity. Productivity growth becomes critical for long-term economic growth, where AI has the power to contribute. AI’s largest economic impact is the impact on productivity through the labor market effects. AI namely automates certain tasks ensuring workers will engage in more productive and higher-value tasks. Besides, AI is able to boost innovations. Companies could use AI techniques to develop entirely new products to serve new and underserved markets effectively. According to McKinsey, these positive impacts of AI on economic growth could raise global GDP by as much as $13 trillion in 2030.
Given the challenges and benefits of AI, it is time for companies and economies to adopt. Instead of a few decades ago, AI adoption could have a serious impact on both businesses and economies.
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