Business Lab

From cars to planes, the future of transportation is already here—and is changing rapidly. Software engineering is increasingly central to both the development and maintenance of all kinds of vehicles. That means more people need to start thinking like systems engineers. Dale Tutt, vice president of aerospace and defense industry for Siemens Software, says this means companies must offer more training and planning for those designing and developing vehicles of the future.

“As you try to address the talent gap, there's a lot you can do to help make the tools easier to use. By better integrating the tools and by bringing in technologies like AI to help automate the generation of different design concepts and the analysis of those concepts using simulation tools, you can extend the capabilities of the system so that it helps empower your engineers,” says Tutt.

“Companies that are the most successful at adopting systems engineering are doing it because systems engineering and the tools being used are becoming almost like the DNA of their engineering organization. Everyone is starting to think a bit like a systems engineer, even in their normal job. The tools and the ecosystem that you use to do systems engineering has a large role in facilitating adoption.”

Nand Kochar, the vice president of automotive and transportation for Siemens Software, says a systems engineering approach can extend more broadly, as engineers think about how cars and vehicles connect to everything else in their environments.

“In a smart city, the system has become the city itself. Take a vehicle in the city, for example. The definition of the system has moved from the single vehicle to include the flow of traffic in the city and to how the traffic lights operate. You can extend that expansive ecosystem to other aspects like building management, for example, into the smart city environment,” he says.

“It becomes a totally different business case than what we have today. These new technologies are furthering innovation, both at the technical level as well as at a business model level. So, as a result of autonomy and the autonomous vehicle deployment, new business models are being formed.”

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The next wave of aerospace is just around the corner, and a lot of that innovation is happening thanks to new, faster methods of development.

“What's happening now is that companies are trying to understand how they take the lessons from Agile software development and apply those to Agile product development,” explains Dale Tutt, vice president of Aerospace and Defense Industry for Siemens. With Agile software development, you can build software and test it relatively quickly.

“When you start talking about an airplane or an air taxi,” Tutt says, “it's expensive to build a prototype and test them, so you have to think about it in a different way and take a different approach. It really takes good program planning.”

This new type of product development, where planes and other kinds of air transport are developed faster than ever, still needs to incorporate safety as a top priority, which creates new kinds of challenges. These kinds of products are different than smartphones or other consumer electronics, Tutt explains.

“Part of it is driven by the safety and reliability you want to have—so that when you're flying around, you can safely operate the vehicle. There's a certain amount of durability and reliability that's built into the design of the product. The amount of investment that these companies or that an individual would make in buying one of these aircraft means there’s an expectation that it's going to last a while, and that you're going to have value in that asset. It's a little bit different than some of the consumer goods that we buy, and it's more expensive to repair them than it is to replace them.”

Balancing speed and efficiency of development is no easy feat when it comes to flying through the air. But Tutt believes we’re living through incredible times where things we can’t imagine now will soon become part of our daily lives.

“Whether it's high-speed aircraft and companies working on the next presidential hypersonic aircraft, or space exploration companies, or urban air mobility and air taxis—there are hundreds of startup companies that are going to transform how we move around large urban areas. And we’ll be moving around in a more sustainable manner because it will be electrically powered. Pretty cool stuff. It's been a long time since we've seen this much innovation going on in aerospace.”

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Companies across all industries are faced with the urgent need to transform the way they do business, including financial services, but changes abound with governance, security, and culture. A shift in mindset and perspective away from “the way things have always been done” is key to a successful digital transformation and to providing the frictionless customer experience banks and other financial services businesses strive to offer.

To stay competitive in the wide-ranging fintech landscape, says Michael Ruttledge, chief information officer and head of technology services at Citizens Financial Group, banks need to become more agile and embrace new technologies. He described the five pillars he has used to guide digital transformations at financial institutions: “The first pillar is moving to agile. Second is moving to a more modern architecture. Third is doubling down on the engineering talent at the bank, and fourth is being more efficient and transforming the technology cost structure. Finally, the fifth pillar is maniacally focusing on security and availability.”

Ruttledge says automation is key to delivering the frictionless experience customers want:

“As we're developing these platforms, we're looking at where can we automate. We're trying to make it frictionless for our customers—for instance, we don’t want it to take a long time for them to open an account because of the amount of information they have to enter. Is there data we can pre-populate? Are there notifications and terms and conditions we can automatically route to the customer, as opposed to them having to send in a document with a signature? We've used a lot of robotics technology. For example, we're using chatbots in our call centers to reduce the call volume to be more efficient.”

By creating the right infrastructure from the bottom up, Ruttledge was able to help Citizens better meet customer needs and expectations. Restructuring has also placed the bank in a good position to take advantage of emerging technologies, says Ruttledge:

“Another area we've touched on in the network space is 5G, which is at least 10 times faster than 4G. We're using it now in some of our branches. As a customer comes into the branch, they're met by a branch member with an iPad, and they're able to complete an application together. That wasn't possible before 5G speeds. Looking further ahead, blockchain is another area where there's a lot of promise for the future. Whether it's in contracts or in trading, the fact that it gives you immutability in terms of the data could facilitate a number of future use cases in the financial services industry.”

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There’s probably no such thing as perfect privacy and security online. Hackers regularly breach corporate firewalls to gain customers’ private information, and scammers constantly strive to trick us into divulging our passwords. But existing tools can provide a high level of privacy—if we use them correctly, says Mashael Al Sabah, a cybersecurity researcher at the Qatar Computing Research Institute in Doha.

The trick is understanding something about the weaknesses and limitations of technologies like blockchain or digital certificates, and not using them in ways that could play into the designs of fraudsters or malware-builders. Successful privacy is “a collaboration between the tool and the user,” Al Sabah says. It requires “using the right tool in the right way.” And testing new technology for privacy and security resilience requires what she calls a “security mindset.” Which, Al Sabah explains, is necessary when assessing new technology. “You think of the different attacks that happened before and that can happen in the future, and you try to identify the weaknesses, threats and the technology.”

There is an urgency to better understanding how technology works with allegedly anonymous technology. “People cannot be free without their privacy,” Al Sabah argues. “Freedom’s important for the development of society.” And while that may be all well and good for folks in Silicon Valley obsessed with the latest cryptocurrency, the ability to build funding structures for all is part of her focus. Al Sabah explains, “Aside from privacy, cryptocurrency can also help societies, specifically the ones with under-developed financial infrastructure.” Which is important because, “There are societies that have no financial infrastructure.”

Al Sabah made a splash in the media in 2018 by co-authoring a paper demonstrating that Bitcoin transactions are a lot less anonymous than most users assume. In the study, Al Sabah and her colleagues were able to trace purchases made on the black-market “dark web” site Silk Road back to users’ real identities simply by culling through the public Bitcoin blockchain and social media accounts for matching data. More recently, Al Sabah has also been studying phishing schemes and how to detect and avoid them.

“There’s more awareness now among users of the importance of their privacy,” Al Sabah says. And that needs to now evolve into teaching security best practices. “So, while we cannot stop new attacks, we can make them less effective and harder to achieve by adhering to best practices.”

Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.

This podcast was produced in association with the Qatar Foundation.

Show notes and links

UNICEF Crypto Fund

“Google’s top security teams unilaterally shut down a counterterrorism operation,” MIT Technology Review, March 26, 2021

“Your Sloppy Bitcoin Drug Deals Will Haunt You For Years,” Wired, January 26, 2018

“Your early darknet drug buys are preserved forever in the blockchain, waiting to be connected to your real identity,” Boing Boing, January 26, 2018

“In the Middle East, Women Are Breaking Through the STEM Ceiling,” The New York Times, sponsored by the Qatar Foundation

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In the coming years, mobility solutions—or how we get from point A to point B—will bridge the gap between ground and air transportation—yes, that means flying cars. Technological advancements are transforming mobility for people and, leading to unprecedented change. Nand Kochhar, vice president of automotive and transportation for Siemens Software says this transformation extends beyond transportation to society in general.

“The future of mobility is going to be multimodal to meet consumer demands, to offer a holistic experience in a frictionless way, which offers comfort, convenience, and safety to the end consumer.” Thinking about transportation differently is part of a bigger trend, Kochhar notes: “Look at few other trends like sustainability and emissions, which are not just a challenge for the automotive industry but to society as a whole.”

The advances in technology will have benefits beyond shipping and commute improvements—these technological advancements, Kochhar argues, are poised to drive an infrastructure paradigm shift that will bring newfound autonomy to those who, today, aren’t able to get around by themselves. 

Kochhar explains, “Just imagine people in our own families who are in that stage where they're not able to drive today. Now, you're able to provide them freedom.”

Show notes and references

·     SAE levels of autonomous driving

·     Siemens Mobility

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In the past decade, machine learning has become a familiar technology for improving the efficiency and accuracy of processes like recommendations, supply chain forecasting, developing chatbots, image and text search, and automated customer service functions, to name a few. Machine learning today is becoming even more pervasive, impacting every market segment and industry, including manufacturing, SaaS platforms, health care, reservations and customer support routing, natural language processing (NLP) tasks such as intelligent document processing, and even food services.

Take the case of Domino’s Pizza, which has been using machine learning tools created to improve efficiencies in pizza production. “Domino’s had a project called Project 3TEN which aimed to have a pizza ready for pickup within three minutes of an order, or have it delivered within 10 minutes of an order,” says Dr. Bratin Saha, vice president and general manager of machine learning services for Amazon AI. “If you want to hit those goals, you have to be able to predict when a pizza order will come in. They use predictive machine learning models to achieve that.”

The recent rise of machine learning across diverse industries has been driven by improvements in other technological areas, says Saha—not the least of which is the increasing compute power in cloud data centers. 

“Over the last few years,” explains Saha, “the amount of total compute that can be thrown at machine learning problems has been doubling almost every four months. That's 5 to 6 times more than Moore's Law. As a result, a lot of functions that once could only be done by humans—things like detecting an object or understanding speech—are being performed by computers and machine learning models.”

And although advances in technology are an incentive to innovate, focusing on customer needs is key. Saha continues, “At AWS, everything we do works back from the customer and figuring out how we reduce their pain points and how we make it easier for them to do machine learning.” The goal is to reach a point where it’s less expensive and machine learning is faster. So with AWS Saha explains, “At the bottom of the stack of machine learning services, we are innovating on the machine learning infrastructure so that we can make it cheaper for customers to do machine learning and faster for customers to do machine learning. There we have two AWS innovations. One is Inferentia and the other is Trainium.”

The current machine learning use cases that help companies optimize the value of their data to perform tasks and improve products is just the beginning, Saha says.

“Machine learning is just going to get more pervasive. Companies will see that they're able to fundamentally transform the way they do business. They’ll see they are fundamentally transforming the customer experience, and they will embrace machine learning.”

Show notes and references

·      AWS Machine Learning Infrastructure

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What if managers and leaders at companies focused on a new goal: to elevate the human experience?

This paradigm shift is something Amelia Dunlop, chief experience officer at Deloitte Digital, advocates for. She and her team have worked hard to measure the amount of humanity in the workplace—a measurement that often depends on how much trust exists between workers and leaders.

Dunlop’s team focused on four signals of trust that leaders can track: capability, reliability, humanity, and transparency. Using these four measurements, which make up Deloitte’s HX TrustID solution, the team was able to predict future behaviors with high accuracy.

It can appear far-fetched to measure seemingly intangible concepts with hard data, and Dunlop acknowledges that many remain skeptical about her use of the word “love” when it comes to work.

“There was part of me that wanted to be deliberately provocative, to say that there is, in fact, a role for love in the workplace. And the way it connects is that worth can be either intrinsic or extrinsic. So, there's an extrinsic measures of worth, such as titles and promotions, how much someone is paid, or who has the awesome corner office. Intrinsic worth is much more about how you feel before you give a presentation, or before you get a job promotion. And do you feel like you are ‘enough’ in a workplace that's constantly evaluating you?”

Especially post-pandemic, Dunlop argues that workers and leaders need to embrace this kind of love and worth so that companies can move into the future successfully.

“There's something about humanizing leadership that I've been thinking a lot about.  When we, as leaders, are willing to make ourselves vulnerable, to show up authentically, drop the professional masks we all wear, be transparent, demonstrate that we care—these are all signals that foster trust.”

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Quoting John Lennon, Bill Kanarick describes the tectonic industry shifts brought on by the pandemic: “There are decades where nothing happens, and there are weeks where decades happen.” After months of hunkering down at home, consumers got used to online shopping, telehealth doctor’s appointments and contactless and curbside pickup, effectively doubling e-commerce sales in the last 18 months.

“So just in a one-year period, what you saw is the intensification of commitment to an investment in digital transformation driven by the pandemic in part,” says Kanarick, EY’s global chief transformation architect for consulting. “Because you had to have a distributed workforce, you had to better meet the customer where the customer needed to be met.”

These new consumerist practices are here to stay, Kanarick predicts—and that means businesses have to reinvent themselves. He discusses how companies are rising to the challenge of new consumer needs and differentiates businesses that will thrive from those that will struggle to survive.

“You have to choose to commit to pursue a different future,” says Kanarick. “There’s no transformation effort on the planet that doesn’t itself come with significant risks. So, you’ve got to also understand how you’re going to mitigate and manage that downside risk.”

But Kanarick is ultimately optimistic about the future, arguing that many companies are steadfast in their commitment to adapting to the evolving digital landscape and keeping pace with customers’ digital habits. 

“If you just simply look at the past year and a half and the rate of change, and frankly in many cases, against seemingly insurmountable odds, the amount of prosperity and reinvention we were able to generate is staggering.”

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Artificial intelligence holds an enormous promise, but to be effective, it must learn from massive sets of data—and the more diverse the better. By learning patterns, AI tools can uncover insights and help decision-making not just in technology, but also pharmaceuticals, medicine, manufacturing, and more. However, data can’t always be shared—whether it’s personally identifiable, holds proprietary information, or to do so would be a security concern—until now.

“It’s going to be a new age.” Says Dr. Eng Lim Goh, senior vice president and CTO of artificial intelligence at Hewlett Packard Enterprise. “The world will shift from one where you have centralized data, what we've been used to for decades, to one where you have to be comfortable with data being everywhere.”

Data everywhere means the edge, where each device, server, and cloud instance collect massive amounts of data. One estimate has the number of connected devices at the edge increasing to 50 billion by 2022. The conundrum: how to keep collected data secure but also be able to share learnings from the data, which, in turn, helps teach AI to be smarter. Enter swarm learning.

Swarm learning, or swarm intelligence, is how swarms of bees or birds move in response to their environment. When applied to data Goh explains, there is “more peer-to-peer communications, more peer-to-peer collaboration, more peer-to-peer learning.” And Goh continues, “That's the reason why swarm learning will become more and more important as …as the center of gravity shifts” from centralized to decentralized data.

Consider this example, says Goh. “A hospital trains their machine learning models on chest X-rays and sees a lot of tuberculosis cases, but very little of lung collapsed cases. So therefore, this neural network model, when trained, will be very sensitive to what's detecting tuberculosis and less sensitive towards detecting lung collapse.” Goh continues, “However, we get the converse of it in another hospital. So what you really want is to have these two hospitals combine their data so that the resulting neural network model can predict both situations better. But since you can't share that data, swarm learning comes in to help reduce that bias of both the hospitals.”

And this means, “each hospital is able to predict outcomes, with accuracy and with reduced bias, as though you have collected all the patient data globally in one place and learned from it,” says Goh.

And it’s not just hospital and patient data that must be kept secure. Goh emphasizes “What swarm learning does is to try to avoid that sharing of data, or totally prevent the sharing of data, to [a model] where you only share the insights, you share the learnings. And that's why it is fundamentally more secure.”

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Advanced cybersecurity capabilities are essential to safeguard software, systems, and data in a new era of cloud, IoT, and other smart technologies. In the real estate industry, for example, companies are concerned about the potential for hijacked elevators, as well as compromised building management and HVAC systems.

According to Greg Belanger, vice president of security technologies at CBRE, the world’s largest commercial real estate services and investment firm, securing the enterprise has grown more complex—security teams must be familiar with controls and hardware on new devices, as well as what version of firmware is installed and what vulnerabilities are present. For example, if an HVAC system is connected to the internet, he questions, “Is the firmware that’s running the HVAC system vulnerable to attack? Could you find a way to traverse that network and come in and attack employees of that company?” 

Understanding enterprise vulnerabilities are crucial to safeguard physical assets but investing in the right tools can also be a challenge, says Belanger. “Artificial intelligence and machine learning need large sets of data to be effective in delivering the insights,” he explains. In the era of cloud-first and industrial internet of things (IIoT), the perimeter is becoming far more fluid. By applying AI and machine learning to datasets, he says, “You start to see patterns of risk and risky behavior start to emerge.”

Another priority when securing physical assets is to translate insights into metrics that C-suite leaders can understand to help boost decision-making. CEOs and boards of directors, who are becoming more security savvy, can benefit from aggregated scores for attack surface management. “Everybody wants to know, especially after an attack like Colonial Pipeline, could that happen to us? How secure are we?” says Belanger. But if your enterprise is able to assign merit to various features, or score them, then it’s possible to measure improvement. Belanger continues, “Our ability to see the score, react to the threats, and then keep that score improving is a key metric.”

And that’s why attack surface management is critical Belanger continues, because “we're actually getting visibility to CBRE as an attacker would, and oftentimes these tools are automated. So we're seeing far more than any one hacker would see individually. We're seeing the whole of our environment.”

This episode of Business Lab is produced in association with Palo Alto Networks.

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