Generative AI: Gold rush or bust? (2024)

About a year back, the news of artworks created by artificial intelligence (AI) engines became a topic of prolonged debates across the creative communities. In February 2023, Reuters reported that OpenAI’s ChatGPT had become the fastest growing consumer application within just two months of its launch. In the subsequent months, generative AI (Gen AI) swept through the technology industry like a tsunami, disrupted the product roadmap of the major tech brands, and created a phenomenon termed as the “AI gold rush.” Understandably, this technological disruption created a raging interest among supply chain communities.

How can generative AI play a role in supply chain transformation from “chain” to “network?” What are the key differences between traditional AI and generative AI? What are key use cases, risks and limitations, and opportunities to leverage this emerging technology to accelerate supply network performance and competitive advantage?

Leveraging the differences between traditional AI and generative AI

AI and machine learning (ML) have received wide acceptance in several supply chain functions, such as demand predictions, supplier risk assessment, anomaly detections, route optimization, etc.

With the “big bang” arrival of generative AI, the quick reaction from many business and technology leaders was to assess and adopt it to improve supply chain operations. However, in doing so, one of the key differences between traditional AI and generative AI is often overlooked.

Traditional AI models have focused primarily on detecting patterns to predict a future outcome based on past events and real-time signals received from upstream or downstream supply network operations. As an example, traditional AI models can be utilized to predict demand patterns based on weather forecasts. They can predict downstream customer order impacts by sensing upstream supply delay signals and suggest alternatives to minimize impacts.

Alternatively, generative AI focuses on generating digital content that did not exist before. For example, it can be utilized to create a contract document with jurisdiction restrictions for sourcing materials from global suppliers. It can improvise existing content with additional inputs or create an entirely new document considering the reference of existing content.

Traditional AI, in short, focuses on predicting the outcome to a problem based on historical or real-time signals and prescribing alternatives in certain cases. Conversely, generative AI can generate novel content based on the instructions provided by human operators/associates. This primary difference helps leaders to decide on investing in solutions based on traditional AI, generative AI, or combinations of both.

This is an excerpt of the original article. It was written for the September-October 2023 edition of Supply Chain Management Review. The full article is available to current subscribers. Already have a magazine subscription, log in now (enter email above) or start your PLUS+ subscription for instant access.

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About a year back, the news of artworks created by artificial intelligence (AI) engines became a topic of prolonged debates across the creative communities. In February 2023, Reuters reported that OpenAI’s ChatGPT had become the fastest growing consumer application within just two months of its launch. In the subsequent months, generative AI (Gen AI) swept through the technology industry like a tsunami, disrupted the product roadmap of the major tech brands, and created a phenomenon termed as the “AI gold rush.” Understandably, this technological disruption created a raging interest among supply chain communities.

How can generative AI play a role in supply chain transformation from “chain” to “network?” What are the key differences between traditional AI and generative AI? What are key use cases, risks and limitations, and opportunities to leverage this emerging technology to accelerate supply network performance and competitive advantage?

Leveraging the differences between traditional AI and generative AI

AI and machine learning (ML) have received wide acceptance in several supply chain functions, such as demand predictions, supplier risk assessment, anomaly detections, route optimization, etc.

With the “big bang” arrival of generative AI, the quick reaction from many business and technology leaders was to assess and adopt it to improve supply chain operations. However, in doing so, one of the key differences between traditional AI and generative AI is often overlooked.

Traditional AI models have focused primarily on detecting patterns to predict a future outcome based on past events and real-time signals received from upstream or downstream supply network operations. As an example, traditional AI models can be utilized to predict demand patterns based on weather forecasts. They can predict downstream customer order impacts by sensing upstream supply delay signals and suggest alternatives to minimize impacts.

Alternatively, generative AI focuses on generating digital content that did not exist before. For example, it can be utilized to create a contract document with jurisdiction restrictions for sourcing materials from global suppliers. It can improvise existing content with additional inputs or create an entirely new document considering the reference of existing content.

Traditional AI, in short, focuses on predicting the outcome to a problem based on historical or real-time signals and prescribing alternatives in certain cases. Conversely, generative AI can generate novel content based on the instructions provided by human operators/associates. This primary difference helps leaders to decide on investing in solutions based on traditional AI, generative AI, or combinations of both.

SC
MR