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Making Artificial Intelligence (AI) Work

To build lasting AI capability in your organization, take a layered approach.

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This is an excerpt of the original article. It was written for the March-April 2020 edition of Supply Chain Management Review. The full article is available to current subscribers.

March-April 2020

Are you ready for NextGen technologies? Just the other day, I had the opportunity to tour one of Amazon’s highly automated robotic fulfillment centers. I expected to be dazzled—and I was. But it wasn’t because of the automation. The tour was a reminder that there’s no question that NextGen technologies such as Artificial Intelligence, blockchain, robotics, 3D printing and 5G are going to be the differentiators in tomorrow’s supply chain. The question is: Are you ready?
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Recent years have seen the convergence of data science and supply chain management. It has been driven by the increased utilization of business intelligence and Big Data along with the growing interest in artificial intelligence (AI). AI promises new levels of insight, actionable intelligence and automation that just aren’t possible by conventional technologies or people alone. Rather, AI can complement and expand the capabilities of your supply chain organization. That’s the opportunity to differentiate and defend your business referred to in Andrew Ng’s Harvard Business Review article above.

As with any emerging technology, the question for an organization is where to start? In many instances, that question may be directed as much to the data science team within your supply chain as to the day to day practitioners. As Ng noted, it’s not easy—especially for legacy enterprises.

In this article, we offer a strategic path for established and successful organizations that are yet to embark on a journey to effectively implement AI. To build lasting AI capability, we take a layered approach. It consists of four layers that are akin to an inverted sand cone.

Starting at Layer 1, a legacy organization can build lasting AI capability, sequentially and cumulatively proceeding all the way up to Layer 4. In each layer, the organization builds capability by carefully fine tuning and learning-by-doing through trial-and-error. Capability in Layer 4 is built cumulatively on the capabilities developed in the layers illustrated below. Based on the nature of data from Layers 1 through 4, we propose a sequence of capability building with respect to cognitive technologies that goes from numerical to textual to image and finally to advanced data such as speech and video.

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Sorry, but your login has failed. Please recheck your login information and resubmit. If your subscription has expired, renew here.

From the March-April 2020 edition of Supply Chain Management Review.

March-April 2020

Are you ready for NextGen technologies? Just the other day, I had the opportunity to tour one of Amazon’s highly automated robotic fulfillment centers. I expected to be dazzled—and I was. But it wasn’t because…
Browse this issue archive.
Access your online digital edition.
Download a PDF file of the March-April 2020 issue.

Recent years have seen the convergence of data science and supply chain management. It has been driven by the increased utilization of business intelligence and Big Data along with the growing interest in artificial intelligence (AI). AI promises new levels of insight, actionable intelligence and automation that just aren't possible by conventional technologies or people alone. Rather, AI can complement and expand the capabilities of your supply chain organization. That's the opportunity to differentiate and defend your business referred to in Andrew Ng's Harvard Business Review article above.

As with any emerging technology, the question for an organization is where to start? In many instances, that question may be directed as much to the data science team within your supply chain as to the day to day practitioners. As Ng noted, it's not easy—especially for legacy enterprises.

In this article, we offer a strategic path for established and successful organizations that are yet to embark on a journey to effectively implement AI. To build lasting AI capability, we take a layered approach. It consists of four layers that are akin to an inverted sand cone.

Starting at Layer 1, a legacy organization can build lasting AI capability, sequentially and cumulatively proceeding all the way up to Layer 4. In each layer, the organization builds capability by carefully fine tuning and learning-by-doing through trial-and-error. Capability in Layer 4 is built cumulatively on the capabilities developed in the layers illustrated below. Based on the nature of data from Layers 1 through 4, we propose a sequence of capability building with respect to cognitive technologies that goes from numerical to textual to image and finally to advanced data such as speech and video.

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MR

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