What is Driving Digital Transformation Today?

“From an AI perspective, one of the biggest impacts is operational efficiency,” says Cyrill Hug, Artificial Intelligence Lead North America at HPE. “If we look at a hospital system that is currently staffing nurses in hallways to watch patients and prevent falls, that is a resource that could be better utilized in the hospital system to actually serve patients instead of just watching someone.”

“That’s where we can apply something like computer vision to help utilize those resources in a better way. And that’s not just human resources, it’s often time, money, human capital that can be spent better in those organizations by automating and orchestrating certain workflows and workloads using AI and machine learning or deep learning,” Hug says.

“We’re hearing a lot about the metaverse these days,” says Sean Graglia from Microsoft. “You can think of it as an umbrella term covering a set of technologies that allow for persistent digital representations and connected aspects of the real world and digital world.”

“When we think about metaverse building blocks, we need to collect the data through Internet of Things (IoT) sensors to be able to represent it. To be able to process it and visualize it, we need AI and analytics to interpret the data and come up with assistance for the consumers on the other end to see what they need to do next. Ultimately, we need devices that will help us visualize the data that we’re working with and work with people in 3D. So it’s all these technologies collectively, as opposed to one popping up that’s going to be a standout,” says Graglia.

“There may be a certain level of maturity organizations need with each of these different types of technologies to really unlock the value of all of them,” says KJ Burke, Principal Technology Strategist Hybrid Cloud, CDW Canada. “The sum of the parts is greater than any individual by itself.”

“One of the key pillars of IoT for Cisco has been edge computing,” says Jason Chung-Tung. “If we’re able to leverage and harness the options that come with edge computing, on any platform, it’s really giving you the ability to drive that cloud functionality. All the intelligence that you have in a more compact way in an edge platform closer to where the sensors are. And one of the things we’re doing with Microsoft is really combining the goodness of both organizations in terms of driving the capabilities that you would otherwise have in the cloud closer to the edge.”

There’s also Industry 4.0, which deals with predictive and preventative maintenance on industrial equipment. “Having the ability to leverage data that’s coming from the machinery, whether it be a vibration, video feeds or temperature sensors, we can predict through artificial intelligence when that piece of machinery is going to fail,” Chung-Tung says. “We all know that one minute of downtime in a manufacturing environment is worth $10,000 USD on average, and in some instances much higher. If you’re able to predict when a piece of machinery is going to fail, you can avoid having to sustain that loss.”

According to Microsoft’s Sean Graglia, a major auto manufacturer rolled out mixed reality to all its dealerships, so they don’t have to fly experts in from across the country or around the world, saving up to $7,000 each time. And an international whiskey manufacturer has reduced training times by up to 70 percent using mixed reality to help accelerate learning.

Even the makers of the world-famous Canadarm are using mixed reality to train astronauts and ground controllers on how to move it. They’re also visualizing data to understand the space missions on the International Space Station (ISS). “Right down from your mechanic that just needs support, all the way up to outer space, we’re seeing this type of value through mixed reality,” says Graglia.

“HPE does a lot of work with space agencies,” adds Cyrill Hug. “We have what we call the space-borne computer that’s live now in the ISS. What we’re doing there is running experiments in space based on data we’re collecting in the ISS, and one of them is trying to predict when some of the trash that’s floating around in space is potentially going to hit the ISS, as more and more satellites that are becoming decommissioned are still floating around in space. Running that computational-intensive workload in a far edge location – you can’t go any farther than outer space – is a very interesting use case that we are working on with our customers.”

“The first issue is data,” says Hug. “Whatever you want to build in terms of the use case, you need to support data and you have to know which format that data is in, where it is stored, how to load it and then create a data set that is actually valuable for training, modelling and making predictions. That’s where many organizations struggle as they try to achieve something that maybe the business has set out in terms of goals.”

“They have to search for the data across the organization, and a lot of legacy systems are in place, especially for organizations that have been around for more than five years. So how do we create a centralized marketplace of data and make that consumable across the organization, be it on-prem, in the cloud or even at the edge?” asks Hug.

When it comes to IoT, the main concern that customers have right now is cybersecurity, says Cisco’s Jason Chung-Tung. “The driver is getting to the data, but in order to get to the data, you need to have things connected. And in order for things to be connected, they’re now exposed. The cybersecurity concern is always there, and it’s hard to alleviate, because you don’t want to be in the headlines of the newspaper” after a data breach.

An aging workforce could also be a barrier to overcome. “These folks are running our power grids, our mines and our transportation systems, and the new generation coming onto the market are not interested in those jobs,” says Chung-Tung. “So all that rich experience, skillset and knowledge that those senior people hold for the production of our operations is going away. There’s a big worry in the industry that they will never be replaced. AI, machine learning and augmented reality is going to change those capabilities, but I think the industry has to mature to understand that.”

According to Chung-Tung, 60 percent of digital transformation projects fail within 12 months. “The reason for that is because organizations turn it into a science project. They’re learning as they go. So it’s building the plane as they’re flying, which is not necessarily the right thing. It’s good in research and development, or in science, but when it comes down to providing a business outcome within six to nine months, you need to go with best practices and validated designs, things that have been proven and have been well-documented, as opposed to trying to figure it out as you’re going.”