Applied artificial intelligence (AI) is a shift from theoretical AI research to practical application in commercial environments. Unlike traditional AI research, which focuses on mimicking human capabilities, applied AI focuses on tangible outputs and solutions that immediately impact business.
Applied AI has revolutionized the business world with unprecedented growth. In 2021 alone, personal investment in AI more than doubled to $93.5 billion, powering the development of commercially sound solutions.
What differentiates applied AI from other AI initiatives is that it focuses on five key drivers of success: it offers immediate impacts, fosters beneficial human connections, is ready to scale, is a trusted solution, and has commercial relevance. These drivers bridge what experts call the "application gap" - the inability of so many AI solutions to practically and ethically deliver commercially viable solutions impacting wider communities.
Most current AI technologies lack one or more of the above categories. Some, like autonomous vehicles, have enormous potential but face scalability and regulatory challenges. Others, like language processing software, are everywhere but not efficiently utilized. Some AI models perform well out of sight but face consumer data challenges, and others, like DeepMind's AlphaGo, possess high technological innovation but low practicality.
Applied AI has diverse business benefits. Firms that employ applied AI reduce their operation costs, with over 85 percent reporting cost savings in process efficiency, supply chain operations, and labor productivity. These gains typically come in the form of reduced environmental impact as well.
Revenue growths are on the rise. AI companies achieve around six percentage points in flat revenue growth from AI. For businesses that use virtual agent technology, 99 percent report lower cost per contact and improved customer and agent satisfaction.
Applied AI enhances decision-making by using real-time big data analysis to detect patterns and provide profound insights. It enables personalized customer experiences through recommendation systems and autonomous support. Predictive maintenance in manufacturing enables companies to anticipate machinery failure beforehand, circumventing downtime and associated costs.
Retailers use AI to personalize customer experiences and enhance inventory management. Healthcare organizations use AI to drive advanced diagnostics and personalized therapies. Banks and financial institutions use AI to combat fraud and optimize risk management. Manufacturers simplify processes and predict maintenance needs. Thanks to AI solutions, farms monitor their crops more efficiently.
However, applied AI has dangers, such as hallucinations, where the AI produces imaginative information that has nothing to do with reality, and algorithmic bias, which leads to unequal and discriminatory treatment. These problems stem from AI's limitations: AI learns from specified datasets, which may have biases. Further, AI does not have the reasoning skills to distinguish right from wrong.
Despite such challenges, applied AI has a promising future. It offers unprecedented strategic understanding and analysis potential, allowing companies to react to emerging challenges, drive growth, and speed up work processes. It will enable organizations to liberate their human employees and create value elsewhere, such as in customer service and strategic consulting.
Furthermore, applied AI facilitates a greater focus on sustainability and corporate social responsibility. With consumers increasingly choosing to buy or not buy products based on the environment, with 84 percent stating they may no longer purchase from brands with a negative environmental record, applied AI solutions can enhance business functions while reducing environmental footprint.
