Artificial Intelligence (AI) has been the subject of countless discussions and articles in the sphere of digital technology. With its practical applications rising rapidly, it is the next transformative technology to stand out from global research in ‘deep learning’.
AI can be described as a set of deep learning techniques that use artificial neural networks unlike traditional analytics. Techniques that address classification, estimation and clustering problems are currently most widely applicable, reflecting the problems whose solutions drive value across a range of sectors.
Recently, McKinsey Analytics conducted research on over 400 companies and organisations to assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. According to this research, greatest potential for AI, in 69 percent of cases, was found to create value in cases where more established analytical techniques such as regression and classification techniques can already be used, but where neural network techniques could provide higher performance or generate additional insights and applications. Whereas, only 16 percent of cases benefited from “greenfield” AI solution that was applicable where other analytics would not be effective.
Applicability of AI varies across sectors primarily due to varying relative importance on different drivers of value within each sector. For example, in consumer-facing industries such as retail, marketing and sales is the area with most value whereas in industries such as advanced manufacturing supply chain, logistics and manufacturing are the most valuable drivers since their operational performance drives corporate performance.
The scale of potential economic and societal impact from AI technology creates an imperative for all participants, AI-innovators, AI-customers i.e. Ai-using companies and policy makers to ensure an environment that can effectively and safely capture economic and societal benefits is fostered.