Skip to main content


Digital Transformation Demands Intelligence at the Edge

one edge

Acceleration of digital transformation is driving the move of more and more intelligence to the edge—closer to where data is generated. In a conversation with Muneyb Minhazuddin, CMO for Intel’s Network and Edge Group (NEX), we learn about Intel’s “One Edge” strategy, how it is playing out in different industries, and what it means as businesses accelerate their digital transformation initiatives.

How did the pandemic change businesses’ approach to digital transformation?

We’ve been talking about IoT and the physical-to-digital transformation as big priorities for years. But it was generally a science experiment. People were more focused on just keeping the lights on. Retail, manufacturing, and other industries already had plans to go in this direction, but it was a nice-to-have. The pandemic forced it into a must-have.

What happened through the pandemic—if retailers, for example, didn’t put in curbside pickup during the first four or six weeks of pandemic, they literally had to shut down. When it became a necessity for the financial viability of their business, they had to implement it.

Now looking at manufacturing, a Bain report projected that 43% of pandemic-related job losses happened in that segment. People were not coming into factories, and there was no one to operate the machines. By necessity, manufacturers started accelerating automation, looking at computer vision technologies for fault detection, quality inspections, or predictive-maintenance use cases.

The pandemic accelerated the shift to more digital and decentralized operations that provide faster insights. It was an imperative for organizational survival. And this new normal around operating and engagement created the urgency for edge compute.

Let’s talk about Intel’s One Edge Strategy and drivers of intelligence at the edge.

The One Edge strategy—and the coming together of the Intel NEX portfolio—is driven by the digital transformation that is happening in all types of industry segments, such as retail and manufacturing, as I mentioned—supply chain, healthcare, and others. Let’s look at a few examples.

First, retail stores started rolling out self-service checkouts to support social distance guidelines, but observed that they were seeing some losses from fraud. For example, one retailer with over 2,500 stores was losing a billion dollars from the front of the store every year. Now the retailer is deploying camera-based fraud detection technologies at the checkouts with the goal to return healthy margins back to the store.

In this example, the edge AI and computer vision can detect when someone is swapping and scanning a $25 item with a sticker from a $5 item. Or when their shopping basket has ten items, the camera is showing only eight items were scanned. In these cases, the information of that scan needs to be processed locally and intelligently in a timely manner. Loss is prevented when a retailer can stop that individual on the store premises with local security or store management.

Now let’s look at a manufacturing example. Recently, I was on the factory floor of an automobile giant. They needed automation timeliness for the robotic arm in a body shop, which was simply adjusting and putting a screw together. But if they get that wrong, instead of a screw it could be a scratch on the body, which means that whole door is a waste. And the cost of several such damaged doors before it is detected will be hundreds of thousands of dollars. An intelligent edge solution that’s doing quality inspection through computer vision can prevent such incidents.

You can see the need for operational efficiencies is about quality and timeliness. If this intelligence is sitting in the cloud and it takes minutes to understand the data, it’s too late. The business drivers are fundamentally cost savings, automation, and efficiency.

“What you find is that #AI or #MachineLearning inferencing is the major #EdgeWorkload. As more and more devices get connected and create more #data, you need that intelligence at the edge.” – Muneyb Minhazuddin, @intel via @insightdottech

Can you go a little deeper on these customers and use case examples?

What you find is that AI or machine learning inferencing is the major edge workload. As more and more devices get connected and create more data, you need that intelligence at the edge. You don’t want to send all that data back into the cloud, get it processed, and then take an action. Why is that intelligence so important at the edge, and why can’t it be tapped from the cloud? The amount of data, the latency, and the timeliness are all going to impact the efficacy of the outcome.

Playing along with that same example of self-service checkout fraud detection: when an offender is swapping stickers or scanning fewer items, it happens in a matter of minutes or seconds.

If you can stop that individual on the store premises with the local security, the retrieval of that product is faster. When store management can close the loop quickly, they can intercept the perpetrator. Because the moment an individual walks out, it’s too late. All the intelligence needs to happen at the edge—at the scanner itself—with speed and low latency.

I’ve seen the same in manufacturing as I talked about, and other industrial environments. I heard this from a chemical plant, which was mind-blowing for me because of the large amount of chemicals that get processed. And if there is one bad ingredient, they throw away tens of millions of dollars in paint or chemicals. That’s bad for the earth and the planet and everything else.

You can see the need for the operational efficiencies is really about quality and timeliness. But it’s not possible with the latency incurred in sending massive video files to the cloud for analytics. If this intelligence is sitting in the cloud and it takes minutes to go and come back, it’s too late.

In the store you didn’t stop any theft when you were supposed to stop it. On the manufacturing floor you’re throwing away tens of millions of dollars of product due to bad quality. The intelligence needs to be inferred then and there, at the point where the data is created.

How does Intel help go from cloud-centric to edge AI and computer vision-based apps?

The Intel One Edge Strategy is bringing compute and network storage to the edge and providing the intelligence I talked about in the above examples.

This is the pervasive nature of Intel technology everywhere. We have been on the journey across every vertical on both sides of the coin. We’re able to provide the most extensive platforms for the IT side of the house to bring all the applications and services from data center and cloud. This is true on the OT side as well from a silicon and software perspective, with our investment in edge inferencing, and with tools like the Intel® OpenVINO Toolkit.

Can customers do this with other technologies? They absolutely have choice. However, that choice is made up of very siloed compartments. They have a choice on the IT side, a choice on the network side, and they have choice on the OT side. But those are three completely different ecosystems, and they don’t operate on a common architecture.

The outcome I see when people are not taking advantage of Intel architecture and solutions—from our silicon to our software and our intelligence model—is what I call bespoke solutions, which are not scalable. For our partners we see a huge benefit in investing in Intel-based architecture and open, general-purpose compute.

In parallel with these technology advancements there’s a real urgency for the delivery of 5G, which is key to driving low latency. We are seeing Telco Service Providers bundling Intel IoT applications, services, and solutions over their networks, making this possible.

Any other thoughts you would like to share?

Technology keeps evolving. There was a big generational jump going from mainframe to client-server architecture that happened 30, 40 years ago. The next jump was to public cloud. And every time we do this, we streamline IT. This is the next generation—of trying to bring what we’ve done in the client server and cloud to the edge. And that has been happening on its own, but not quite at the rate and pace of streamlining that’s happened in the data center and cloud. The drive to more intelligence at the edge and what we’re doing with Intel’s One Edge strategy is how we see it coming together, and what will make this next technological evolution successful.


Edited by Christina Cardoza, Associate Editorial Director for

About the Author

Georganne Benesch is an Editorial Director for Before this she was an independent writer, authoring blogs, web content, solution guides, white papers and more. Prior to her freelance career Georganne held product management and marketing positions at companies such as Cisco, Proxim and Netopia. She earned a B.A. at University of California at Santa Cruz.

Profile Photo of Georganne Benesch