Updated Value Assessments from McKinsey’s AI Frontier Studies Updated Value Assessments from McKinsey’s AI Frontier Studies
Day by day it seems like technological advancements widen the scope of what qualifies as “artificial intelligence.” McKinsey expresses this exact sentiment in a... Updated Value Assessments from McKinsey’s AI Frontier Studies

Day by day it seems like technological advancements widen the scope of what qualifies as “artificial intelligence.” McKinsey expresses this exact sentiment in a recent discussion paper from the McKinsey Global Institute that analyzes the potential value-add of different deep learning models across multiple industries. Over 400 use cases across 19 industries and through nine business functions were collected and analyzed for the insights drawn in the paper. Five different deep-learning models were the main focus of the report:

  1. Feed-forward networks
  2. Recurrent neural networks
  3. Convolutional neural networks
  4. Generative adversarial networks
  5. Reinforcement learning

Insights from the paper include illustrating how relevant the use of various deep learning models apply to different industries compared to traditional analytical techniques (regression, decision-tree modeling, clustering, etc.) and statistics that demonstrate the various future uses of deep learning models. Key insights from McKinsey’s analyses include:

  • Two-thirds of the opportunities to apply AI (deep learning techniques) can be used in improving current/existing analytical models and use cases.
  • The industries that would see the most value from incremental use of AI over traditional analytical techniques include travel, retail and transport & logistics.
  • Although deep learning models can provide more insights, the data requirements for these techniques are far greater.
  • To achieve AI’s full potential will include the use of unstructured data types such as video, images and audio.
  • Across business functions, AI will have the most impact on marketing and sales followed by supplier logistics.

Based upon the insights gathered during this analysis, the McKinsey team estimates “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.”

Although this discussion paper represents only one perspective, its insights illustrate the tremendous potential impact and value-add that AI has across multiple industries and business functions. As each new iteration of various softwares and tools come about, the capabilities associated with AI will only become more apparent. McKinsey provides a glimpse into what this future of AI holds in this paper and will continue to update their assessment as time goes by.

Megan Elizabeth Shulby, ODSC

Megan Elizabeth Shulby, ODSC