In the current digital age, Artificial Intelligence (AI) emerges as a cutting-edge and transformational technology. The technologies underpinning AI and its application continue to move forward across various business types including financial services, healthcare, psychology, politics, law, and ethics. Business leaders need to decide when, how, and where they can employ AI technologies.

McKinsey report [1] highlights a number of business use cases, where AI technologies can help logistics and transportation industries to reduce costs and delivery times through real-time forecasting and behavioral coaching. As an example, a European trucking company put sensors to monitor vehicle performance and driver behavior, enabling the drivers to receive real-time coaching in the final step. Airlines are using AI to predict weather-related problems to avoid costly cancellations [2]. The report presents the practical applications and economic benefits of advanced AI techniques by mapping such techniques to the type of problem they address [1].

The report provides insight into use cases to assess where AI advanced techniques can demonstrate significant value when compared to traditional analysis [1]. Their assessment confirmed that 69% of use cases that apply advanced AI techniques improve their performance. According to McKinsey Global institute analysis across nine business functions in 19 industries, the AI advanced techniques can approximately create between $3.5 trillion and $5.8 trillion in value annually [3]. However, only 20 percent of AI-aware companies actively employ one or more AI-advanced techniques in a core business process due to their challenges around technology, processes, and people.


[1]. “Notes from the AI frontier insights from hundreds of use cases,” McKinsey Global Institute (MGI), 2018.

[2]. “What AI can and can’t do (yet) for your business,” McKinsey Quarterly, January 2018.

[3]. Ibid. McKinsey Global institute, Artificial intelligence, June 2017.


Elham Taghizadeh received her bachelor’s and master’s degrees in industrial engineering from K. N. Toosi University of Technology in Tehran, Iran, in 2010 and 2012, respectively. She is currently working toward her Ph.D. in industrial and systems engineering at Wayne State University. Elham’s research interests include supply chain management, optimization, network resilience, big data-enabled analytics and data-driven decision making.

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