“Artificial intelligence (AI) stands out as a transforma- tional technology of our digital age—and its practical application throughout the economy is growing apace. For this briefing (…), we mapped both traditional ana- lytics and newer “deep learning” techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied ex- perience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial poten- tial of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles—along with future opportu- nities as the technologies continue their advance. Ul- timately, the value of AI is not to be found in the mo- dels themselves, but in companies’ abilities to harness them.(…) We estimate that the AI techniques we cite in this briefing together have the potential to create between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries. This constitu- tes about 40 percent of the overall $9.5 trillion to $15.4 trillion annual impact that could potentially be enabled by all analytical techniques.
(…)In retail, marketing and sales is the area with the most significant potential value from AI, and within that function, pricing and promotion and customer service management are the main value areas. Our use cases show that using customer data to personalize promo- tions, for example, including tailoring individual offers every day, can lead to a one to two percent increase in incremental sales for brick-and-mortar retailers alone. In consumer goods, supply-chain management is the key function that could benefit from AI deployment. Among the examples in our use cases, we see how fore- casting based on underlying causal drivers of demand rather than prior outcomes can improve forecasting accuracy by 10 to 20 percent, which translates into a potential five percent reduction in inventory costs and revenue increases of two to three percent. In banking, particularly retail banking, AI has signi- ficant value potential in marketing and sales, much as it does in retail. However, because of the importance of assessing and managing risk in banking, for example for loan underwriting and fraud detection, AI has much higher value potential to improve performance in risk in the banking sector than in many other industries.“
Artificial intelligence‘s impact is likely to be most substantial in marketing and sales as well asl supply-chain management and manufacturing, based on our use cases.