Business Lab

The World Bank Group has a massive mission to “help developing countries escape poverty and share prosperity,” says Vijay Yellai, program manager for enterprise resource planning transformation at the World Bank Group. For example, it provides an wide array of financial products and technical know-how in a complex and ever-changing global setting.   Therefore, for an institution like the World Bank Group, which provides funding and resources to countries with low bandwidth and infrastructure, IT modernization is no small feat.   “So in an ever-changing environment–complexity, risk, and security threats with a global workforce–the World Bank is under pressure to do more with less,” says Yellai. He explains that the challenge is to increase real-time business, as well as quickly respond to changing needs of customers and employees. But also, Yellai continues, “security, risk, and data are key elements. Not to mention the continuous need for business intelligence and quick decision making.” The ultimate goal is to “capitalize on technology to meet our mission and goals.”   And although data collection and processing is key to IT systems, an agile and adaptive approach is needed to keep operational and financial systems current in each business. “Data is very fundamental to that. And data and research help us understand how we are addressing the needs, helps us set up priorities, helps us share knowledge, and helps us measure progress.” Yellai says.   With a modernized IT system, Yellai says, there are a number of innovations that become possible. Predictive analytics, natural language processing, blockchain, and process automation are a few of the technologies emerging to allow for quicker decision-making and efficiency.   “Anything we can do to reduce the work we need to do in technology, but let the technology do more for us, so we can focus our time on the strategic priorities, will be the most exciting thing for us,” says Yellai.   This episode of Business Lab is produced in association with Infosys Cobalt.

Although the world population has continued to steadily increase, farming practices have largely remained the same. Amid this growth, climate change poses great challenges to the agricultural industry and its capacity to feed the world sustainably. According to the World Bank, 70% of the world's fresh water is used in agriculture and droughts and heat waves continue to threaten crops. And that is where the challenge arises to feed the world while mitigating the environmental effects of agricultural practices.   The answer to this challenge, according to Thomas Jung, head of IT Research and Development at Syngenta, is regenerative agriculture. Just as important as clean water and clean air, soil is the critical foundation of agriculture. The crux of regenerative agriculture is to grow more food with less environmental impact by enhancing the health of soil.   “So not much has changed, but we need to feed more and more people,” he continues “How do we address this challenge of feeding the world in a sustainable fashion without exploiting our soils more?”   Regenerative agriculture efforts look to find solutions to help plants stay healthy, find solutions to make crops more resistant to climate change-induced droughts and heatwaves, and use less water in farming.   Therefore, what's necessary is, “moving beyond the traditional agriculture and the way we've been doing this for probably 100 years or more. I mean, this is a leap,” says Jung. “This is an agricultural revolution that is ongoing, and artificial intelligence will play the decisive role in it.”   Although farmers have invaluable knowledge of their own crops and fields, says Jung, AI and machine learning tools can be instrumental in cataloging greater detail, refining algorithms, and creating recommendations for solutions. As more data is collected and algorithms continue to improve to create new innovations, we'll be even closer to understanding our planetary ecosystem, says Jung. Breakthroughs like soil regeneration, really living with sustainable agriculture across disciplines are achievable within the next three years.   “There's a lot of advocacy for open source, for democratized data, for fair data, and we need to bring that to the industry,” says Jung. “This can't just be an NGO or a volunteering thing, that this is how I believe our industry needs to work. So we really want to share, we want to lead by example, we want to nurture the community, and through that, win altogether.” This episode of Business Lab is produced in association with Infosys Cobalt.

The last five years have seen large innovations throughout drug development and clinical trial life cycles—from finding a target and designing the trial, to getting a drug approved and launching the drug itself. The recent use of mRNA vaccines to combat covid-19 is just one of many advances in biotech and drug development. Whether in preclinical stages or in the commercialization of a drug, AI-enabled drug development is now used by an estimated 400 companies and has reached a $50 billion market, placing AI more firmly in the life sciences mainstream. “Now, if you look at the parallel movements that are happening in technology, everyone's in consensus that the utility of what AI can do in drug development is becoming more evident,” says senior vice president at Medidata AI, Arnaub Chatterjee. The pandemic has shown how critical and fraught the race can be to provide new treatments to patients, positioning the pharmaceutical industry at an inflection point, says Chatterjee. And that's because drug development usually takes years. Evidence generation is the industry-standard process of collecting and analyzing data to demonstrate a drug's safety and efficacy to stakeholders, including regulators, providers, and patients. The challenge, says Chatterjee, becomes, “How do we keep the rigor of the clinical trial and tell the entire story, and then how do we bring in the real-world data to kind of complete that picture?”   To build more effective treatments faster, drug and vaccine companies are using data iteratively to improve understanding of diseases that can be used for future drug design. Bridging gaps between clinical trial and real-world data creates longitudinal records. AI models and analytics can then be used to enable feedback loops that are key for ensuring safety, efficacy, and value, says Chatterjee. “We want to create safe and expeditious access to therapy,” says Chatterjee. “So we really have to meet this moment with innovation. With all the new advances happening in drug development, there's no reason why technology and data can't be there.”   This episode of Business Lab is produced in association with Medidata.   Related resources ●     Integrated evidence, Medidata ●     Why artificial intelligence could speed drug discovery, Morgan Stanley

Memory and storage solutions for technology are built into our everyday life, from mobile applications, cars, health-care systems, and more. To meet that need and help propel innovation, Micron Technology said it would invest $150 billion into research and development to build factories for its semiconductor memory chips. This investment looks to expand not only the reach of memory chips but also to innovate new solutions to common problems, says Naga Chandrasekaran, senior vice president of technology development at Micron. “The day we stop innovating, not just in memory, but as a human race, the day we stop innovating, we stop progressing and that's not where we want to be. We want to continue to drive innovation,” says Chandrasekaran. With each iteration of new technology, from phones to cars, consumers are looking for improved performance, lower latency, more storage, and lower costs. Meeting these expectations means finding solutions at an atomic scale and making micro changes to push the boundaries of what's possible. Since its inception 44 years ago, Micron has developed over 50,000 patents. While Chandrasekaran emphasizes that patents are just one part of fostering innovation, they do represent the strides toward greater innovations and the company culture that Micron has worked to establish. While having strong team members is important, the culture that a company fosters is just as crucial when it comes to seeing positive results. Chandrasekaran says that building successful teams that can create so many patents and build technologies with an eye on innovation requires a certain company mindset that doesn't shy away from mistakes or failure. “So we are taking risks on a regular basis, but the key is to make sure we can fail fast and not see those failures as a mistake, but actually learn from them.” Chandrasekaran continues, “That's why failing fast is important, but not being afraid of failing.” In addition to taking risks, diversity has become a significant contributor to driving new solutions. Between 2017 and 2021, the number of women listed as inventors on Micron patent applications quadrupled. Chandrasekaran says that for any sustained success and innovation to be possible, diversity is necessary. “No matter what we say, we are all limited in our thought process in how we approach problems, in how we approach solutions. And even with a growth mindset, we have limitations, because we are who we are based on the experiences and the exposures that we have gained,” says Chandrasekaran. “So diversity brings in not just from a gender diversity or ethnic diversity, but if we look at diversity from a broader scale, it's diversity of thought process.”     This episode of Business Lab is produced in association with Micron Technology.

In any enterprise, digital transformation is not only a technology transformation but enables business transformation itself, driving new products, solutions and innovations. Having an efficient data strategy is critical to any successful digital transformation but requires careful investment into both people and systems.   “To achieve that goal, availability of good data, of the right data, and availability of that to the right people and systems is very, very critical. So that forms the data strategy for any enterprise today,” says chief architect for data and AI services at Kyndryl, Sundar Shanmugam.   Getting the most out of digital transformation investments means evaluating and optimizing agility throughout an enterprise to drive actionable insights, says Shanmugam. A strong data governance framework also goes a long way in keeping data high-quality. Often data governance primarily serves regulatory requirements. But truly effective data governance is holistic, he adds. Data usage, regulations, and the data itself are constantly evolving within an enterprise, effectively making data governance a continuous process.   Although tech teams are often dictating how data should be managed and used, Shanmugam says everyone across the enterprise, including leaders and decision makers, should be data literate.   “End of the day, the people are the ones who design the systems and who develop the systems that consume the data, so the right investment on literacy is paramount in that aspect,” says Shanmugam.   Another key component to digital transformation lies in maximizing investments across business units. The combination of software development and operations to form devOps, AI and machine learning to form MLOps, and finance and operations to form finOps all fall under the broader umbrella of XOps that categorize these merging of IT disciplines with business operations. XOps all come together to deliver value in the most efficient way with each combination focused on maximizing automation, reusability, and agility, says Shanmugam.   “As we say, necessity is the mother of innovation, so that necessity can continuously change,” says Shanmugam. “At the core of that, if we keep that data proper [condition], then it can expand the horizons, not just internally, but even for the other external requirements and use cases.”   This episode of Business Lab is produced in association with Kyndryl.   Related reading Top Trends in Data and Analytics for 2021, Gartner, February 16, 2021

As more business, shopping, and banking is done online and from, well, anywhere, customers increasingly expect high-quality digital-first services that remove the need to go into a physical store or bank. “People were working from home, shopping from home, banking from home, and are more tech and digital savvy than ever,” says Mike Dargan, group chief digital and information officer for UBS. “And if you look at the financial services industry, the ecosystem is constantly evolving, so it becomes more competitive, open, connected, and location-independent every day.” Banks like UBS are experiencing a cultural transformation that makes technology itself integral to the business offering. Because technology is often the first—or only—medium via which customers encounter a business, the quality of that digital customer experience is key to the value it provides. To make that digital-first experience a differentiator for a business, Dargan explains, requires strategically and substantially investing in tech throughout the enterprise. Investing in technology means a targeted focus on the people and platforms behind the customer-facing experiences, not just building out the feature of the month. Dargan's roadmap begins with engineering excellence—attracting and retaining engineers who want to shape the direction of the industry. In addition to offering an inclusive culture and opportunities to solve important real-world problems, companies seeking the best engineers must provide cutting-edge platforms and tools to create an industry-leading developer experience. To enable this—as well as to reimagine what they can offer to clients—UBS has made years-long investments in updating legacy systems to new technology, focusing on infrastructure allowing the launch of cloud-native applications on a cloud-native platform. Internal company processes, though invisible to customers, drive the speed and quality of digital innovation. At UBS, process focus has included training on agile methodologies—teaching employees from all business areas how to get from emerging requirements to high-quality outcomes as quickly and iteratively as possible. Less obvious longer-term investments in the technology future may also pay dividends both in business terms and in customer experience. In the sustainability space, UBS has partnered with the Green Software Foundation to build software standards and best practices for reducing carbon emissions, and has set a goal of net zero greenhouse gas emissions across its operations by 2050. Says Dargan, “the world is faster, more digital, and more data-driven than ever before, so clients really do demand services that are digital-first, anytime, anywhere, and are underpinned by first-class technologies. In fact, I'd say tech is really how our clients experience us every day.”   This episode of Business Lab is produced in association with UBS.

A heritage financial services institution isn't necessarily the first place a technologist looks to grow their career. But that hasn't been a problem for JPMorgan Chase, which has made itself an appealing career destination for technologists. “Technology is not an afterthought,” says Gill Haus, chief information officer of consumer and community banking at JPMorgan Chase. “It is in everything we do, from our offices to our branches to our contact centers to our web and mobile applications.” Haus explains that there's more to being a technology company than using technology to solve problems. “What really makes a technology company is how you think about the way you hire teams, the way you groom teams, the way you build software, the way you deploy that software,” says Haus. “It's how you organize around products, not around your business units.” Like a technology startup, the technical teams at JPMorgan Chase solve real-world problems. “Every single day, we are launching new features and products that make it easier for everyone. It's incredibly exciting,” says Haus. However, unlike a startup, JPMorgan Chase has scale. The financial services institution supports 44 million mobile active customers and 59 million digitally active customers in general. The company spends $12 billion on technology annually. “When we launch a solution or bring a new product to market, we don't bring it to market for 1,000 people. We don't bring it to market for a 100,000 people. We bring it to market for millions of customers immediately, and that is incredible,” says Haus. “There's incredible talent here, as you can imagine, because we have such a technical need. Talent begets talent. It's exciting to go to a place where you know you're going to be challenged.”

The current 5G evolution in network connectivity is expected to drive unprecedented demands for bandwidth, reliability, and security. However, a network of this magnitude and robustness doesn't pop up overnight and enterprises and consumers are just beginning to realize the myriad use cases a 5G network can support. For example, consider the increased number of connected devices in a house like smart thermostats, security cameras, tablets, smartwatches, and mobile phones, of course. Raj Savoor, the vice president of network analytics and automation at AT&T Labs explains, “Currently we estimate the average consumer home footprint has about 13 connected devices, including mobile and other devices.” And although that sounds like a large number, he continues to explain the real scale, “That's going to increase to 30 to 40 devices over the next five years, so a really big increase.” And the real challenge he continues to explain is that, “This growth needs advanced network architectures to support, manage and provide fast, secure, and reliable services.” Bandwidth will also increase five times in the next five years, according to Savoor, as consumers adopt immersive interactive applications. Immersive experiences also require lower latency and jitter, and a lot more security and reliability. For a company like AT&T that supports a large existing network, building the next generation network requires an incremental approach. In fact, AT&T's 5G network has been years in the making. “We look at it as a journey. There are a lot of steps that we've taken over the past few years to build on it, and we have prepared for the next step,” says Savoor. And as businesses and consumers transition to a 5G world, AT&T keeps looking ahead. “We are thinking about the next 20 and 50 years. Network investments take a long time, and we want to make those investments with economics in mind, but also very much ensuring the most reliable network offering,” says Savoor.

The skills and capabilities needed to undergo digital transformation are in high demand as every company jockeys to gain a competitive advantage. To address the skills gap, some companies are prioritizing upskilling and reskilling. But to be effective, learning and development itself must undergo a transformation. According to Daniela Proust, global vice president and head of global people enablement and growth at Siemens, learning and development is at the core of digital transformation. “In light of a major transformation that businesses are facing, either by new business models arising or new innovation and technologies driving a certain business area forward, you see that you need to accompany that structural change, that structural workforce transformation in order to drive business transformation,” she says. Traditional training methods need to also transform. Given the speed of technological change and need for business agility, multi-day offsite training (some of which may not even apply to the employee's role) is no longer viable. Fortunately, the same technologies that are driving digital transformation in other areas of the business can also be leveraged to transform learning and development. “Now people learn more often, for shorter periods of time, but training is much more tailored to what they need in that moment, and that is enabled by technology,” explains Proust. In addition to delivering just-in-time training, a modern learning and development platform can provide valuable insights. “A platform-based learning ecosystem with a learning experience platform at the core enables you to gain insights that we never had in the past.” says Proust. This new approach to learning delivers benefits to both the business and its employees as they acquire the skills that help accelerate the company's digital transformation and fuel their own career growth.   This episode of Business Lab is produced in association with Infosys Cobalt.

Like many banks, National Australia Bank (NAB) decided to outsource a large part of its operations in the 1990s. “We pushed all our operations and a large part of our development capability out to third parties with the intent of lowering costs and making our operations far more process driven,” says Steve Day, the chief technology officer of enterprise technology at National Australia Bank. Unfortunately, achieving these goals had an unintended consequence. “We froze our operations in time,” says Day. “If you roll forward to 2018, we realized that we were still operating like we're in the 1990s. We were very waterfall driven. Our systems were highly processed driven, but in a very manual way, and it took us a very long time to roll out new products and services that our customers really needed.” Meanwhile, young financial services companies were coming to market with innovative products and services and NAB was finding it difficult to compete. “Many customers today are expecting an Amazon experience, a Google experience, a Meta experience, but we were still operating in the 1990s,” says Day. “We stood back, and we looked at it, and we decided that our entire culture needed to change.” What ensued was nothing less than an internal transformation. “Our original teams didn't have a lot of tech skills, so to tell them that they were going to have to take on all of this technical accountability, an operational task that had previously been handed to our outsourcers, was daunting,” says Day. Day and his team rolled out a number of initiatives to instill confidence across the organization and train people in the necessary technical skills. “We built confidence through education, through a lot of cultural work, a lot of explaining the strategy, a lot of explaining to people what good looked like in 2020, and how we were going to get to that place,” says Day. This episode of Business Lab is produced in association with Infosys Cobalt.

With its Emirates Mars Mission, also known as the Hope Probe, the UAE has established itself as only the fifth country in history to reach Mars and the seventh in the world to reach the orbit of another planet. The UAE's first mission to Mars, Hope's goal is to provide the first, complete picture of the Martian atmosphere and its layers to help scientists understand the planet's climate better. The Emirates Mars Mission is unique in that the troves of data collected by Hope are being released to the public. “This contributes to a more knowledge-based economy and fosters the science community's capabilities as a collective. This step was taken to encourage the science community to break the barriers and work together for the greater good,” says Maryam Yousuf, a data analyst for the Emirates Mars Mission. The Hope probe has three main objectives, the first is to understand the lower Martian atmosphere and its weather and climate. Yousuf continues, “The second objective is to correlate the lower atmosphere conditions with the upper atmosphere to explain how weather changes the escape of hydrogen and oxygen. And the final objective that we have is to understand the structure and variability of hydrogen and oxygen in the upper atmosphere and why Mars is losing them into space.” The focus on space for the UAE comes at an important time as mapping Mars will contribute to the work of not just the knowledge economy of the UAE, but advance science for the whole world. “The UAE is basically investing in space, as investing in the space sector means investing in the human capital towards a better future for all,” says Yousuf. This episode of Business Lab is produced in association with the UAE Pavilion Expo 2020 Dubai.

In many areas of the world, environmental conditions are not conducive to traditional farming. As a result, these countries are food dependent. They rely on imported food, which is subject to supply chain issues and nutrient loss during transportation. A company in the United Arab Emirates called Smart Acres is looking to change all that through hydroponic vertical farming. “Living in a region with a lot of non-arable land and in arid conditions, we're not able to produce a lot of the crops needed for consumption for the nation. The UAE actually imports 90% of the food for consumption,” says Aphisith Phongsavanh, lead project manager of Smart Acres. Smart Acres is an indoor vertical hydroponic farm that grows pesticide-free leafy greens using 1/10 of the land and 90% less water than traditional farming. The Abu Dhabi-based farm grows 13 cycles of lettuce a year, yielding 20 times more food than traditional farming would on the same square meters of land. The Smart Acres farm consists of eight shipping containers equipped with modules that use internet of things technology to monitor for humidity, temperature, and the nutrients inside the plants. It's all in the name of creating an environment that's optimized for plant growth and high nutritional value. “When we come in, we have air showers built into our facilities. We make sure that our controlled environment is as sterile as possible to protect the plants from external factors,” says Phongsavanh. Having fine-tuned the process for leafy greens, the team is preparing to expand its crop. First up: strawberries, a local favorite. “There's no leading vertical farm in the UAE right now that is commercializing strawberries. So, in fact, we would love to be the first one to test it and get it right,” says Phongsavanh. Potatoes are also a priority. “A lot of the countries in the MENA region, Middle East, North Africa, get their potato seeds and their potatoes imported from around the globe, such as Europe and North America. We would really like to reduce and curb that dependence on that system, so if we were to grow the Middle East potato seed, it would do wonders for the local ecosystem in terms of Middle East and North Africa.” The company also has plans to develop the Smart Acres Institute of Food Security and Agriculture, a localized food security program. “Our long-term goal is actually to be a pioneer within the region to facilitate the research and development of plant propagation,” says Phongsavanh. “We'd really love to look into growing new crops and plants that can be grown efficiently within this harsh environment.” The future looks promising, as Smart Acres works to increase food production from 11 tons to 155 tons annually. This episode of Business Lab is produced in association with the UAE Pavilion Expo 2020 Dubai.

For many people, the concept of directed energy, or lasers, conjures images of lightsabers and bank vault security systems—the stuff of Hollywood movies. However, the fact is, lasers are commonly used in everyday life applications, from surgery to optical communications. At Technology Innovation Institute's (TII) Directed Energy Research Center (DERC), scientists and engineers are using directed energy to solve some of the world's most complex challenges and make the world a better place. Directed energy is “the ability to create a high amount of energy in a controlled volume at a given distance in order to trigger physical reactions to study the interaction between the energy and the matter,” says Dr. Chaouki Kasmi, who is the Chief Researcher at DERC, which is part of the Abu Dhabi government's Advanced Technology Research Council. The research at DERC reflects the multitude of applications that are possible using directed energy, but the research projects have at least one thing in common: the goal of solving real-world scientific or technical challenges. For example, one of DERC's recent developments is a landmine detection system – the ground-penetrating radar - designed to help developing or previously war-torn countries detect and neutralize unexploded landmines. However, Dr. Kasmi and the researchers at DERC aren't just looking down. They have their sights set much higher and further with projects focused on using lasers for communications on land, to the moon, and even underwater—truly making the entire world a better place with directed energy technology. “The disruptive innovation that we are bringing today is how we can make it affordable for developing countries. The idea is to create a technology that could really help solve a worldwide problem at low cost. And this is very important for us as we would like to have the system deployed at scale,” says Dr. Kasmi. The research scientists at DERC also look for ways to leverage the solutions they develop beyond the initial application. “The way we work is to really create building blocks and to combine or reuse those building blocks in order to tackle additional challenges,” says Dr. Kasmi.

Since the 1940s scientists have studied ways to increase rainfall with the goal of increasing precipitation in arid and semi-arid climates. Today, that endeavor is making incredible leaps and bounds as scientists and engineers apply nanotechnology to improve the effectiveness of cloud seeding. “The global water shortage has continuously intensified by rapid population growth and economic development around the world. Conventional water resources such as rivers, lakes, and groundwater have become very limited, which is driving scientists and engineers to look for alternative water resources,” says Dr. Linda Zou, a professor of civil and environmental engineering at Khalifa University of Science and Technology. Dr. Zou leads a groundbreaking research project using nanotechnology to develop cloud seeding materials. Cloud seeding is a form of weather modification that mimics what naturally occurs in clouds but enhances the process by adding particles that can stimulate and accelerate the condensation process. However, Dr. Zou explains that “The cloud seeding materials used today have been around for many decades. The information and techniques are out of date and their effectiveness is not well understood.” Cloud seeding has strict requirements. To be successful, scientists need the right air temperature, the right humidity, a surface that attracts water and keeps it, and then the correct size material to allow condensation to form on the particle. “Through the advancement in nanotechnology and nanoscience, nowadays we are working to design and engineer cloud seeding materials with optimal properties to ensure water vapor condensation will occur effectively and maximize the rainfall achieved,” explains Dr. Zou. Related materials ·      UAE Research Program for Rain Enhancement Science ·      New UAE cloud seeding test in Texas shows 'promising results', The National News, August 15, 2021

Few today won't agree that sustainability is important not only to the future of the planet and society but to business practices as well. And approaches are evolving beyond designing products to be used as long as possible. “If we're going to design a product or use a product, we're thinking from the very first moment what happens afterwards,” says Corey Glickman, vice president and head of the sustainability and design business at Infosys. “How do I source those materials? How does it function efficiently? And then ultimately, can it be reused? Can it be recycled?” Consumers are driving sustainability efforts as well. They're beginning to weigh company values when choosing where to shop and what to buy. And business leaders are taking note, Glickman says. They're looking at things like return on investment, whether they can afford to change, or afford not to change. “We call it single bottom-line sustainability, where I look at the single bottom line of all those elements, and I start attaching sustainability to it,” Glickman says. “And I start looking at changes of value and then I can build a business case for change.” As companies set sustainability goals—to be carbon neutral by 2050, for example—they're tackling complex challenges: regulations change, supply chains are complicated—especially during the current pandemic—and integrating new technologies into legacy systems is almost always a hurdle, technologically and culturally. Glickman suggests an incremental approach—he calls it micro change, embracing the fact that sustainability isn't a one-and-done paradigm shift. “These are things that can be done in a six-week period, eight-week period, that have tangible proof of concepts that can be measured, that can be done at different levels.” Looking at current infrastructure investments, particularly in North America and Europe, as well as the increasing interest of stakeholders, the sustainability bar is expected to rise. “For the next three years you will see a lot of investment. You will see countries or businesses that want to be leading because they see an advantage,” says Glickman. “Then you will see others have to move along in that direction also.” This episode of Business Lab is produced in partnership with Infosys.

Fueled by innovations in AI, IoT, and blockchain, digital transformation has been accelerating rapidly across industries. But as the world's data is growing at the edge, the stark differences in digital equity and inclusion have become clear. Access to technology, underrepresentation within tech companies, and bias within technology itself contribute to this stark digital divide, says Janice Zdankus, vice president of strategy and planning and innovation for social impact at HPE. From healthcare to manufacturing to agriculture, many organizations don't have a handle on the data they generate. While data is being created quickly, companies often lack a strategy to organize, share and account for bias in their data. ”I think we see today that there's not an equitable exchange of data and those producing data aren't always seeing the value back to them for sharing their data,” says Zdankus. Democratizing data access is key to bolstering data inclusion and equity but requires sophisticated data organization and sharing that doesn't compromise privacy. Rights management governance and high levels of end-to-end security can help ensure that data is being shared without security risks, says Zdankus. Ultimately, improving digital inclusion and equity comes down to company culture. “It can't just be a P&L [profit and loss] decision. It has to be around thought leadership and innovation and how you can engage your employees in a way that's meaningful in a way to build relevance for your company,” says Zdankus. Solutions need to be value-based to foster goodwill and trust among employees, other organizations, and consumers. “If innovation for equity and inclusion were that easy, it would've been done already,” says Zdankus. The push for greater inclusion and equity is a long-term and full-fledged commitment. Companies need to prioritize inclusion within their workforce and offer greater visibility to marginalized voices, develop interest in technology among young people, and implement systems thinking that focuses on how to bring individual strengths together towards a common outcome. This episode of Business Lab is produced in association with Hewlett Packard Enterprises.

Across industries and geographies, the pandemic has triggered a paradigm shift in the way companies—and their employees—conduct day-to-day business. The move to work-from-home and hybrid work models has increased the need for collaboration to facilitate communication and innovation from remote locations, and to keep teams connected and engaged when in-person meetings are difficult or impossible.   Successful collaboration requires creating an equitable experience for all team members, says Faiza Hughell, RingCentral's chief customer officer. “Participant equity is predicated on the ability to empower your employees with the tools, technologies, and programs they need to reach success, to remain productive at all times,” she explains.   And it's more important than ever for companies to evaluate the new technologies teams are implementing to ensure they are facilitating the desired outcomes, as well as to assess a particular technology's potential application to other areas and teams in the company. “In a sales organization, for example, certain teams may have access to certain tools that increase their productivity. Well guess what? Other teams might be able to benefit from that as well,” says Hughell. “It's important when we bring in new technologies that we negotiate our contracts such that they're flexible, so we can determine who needs the technology.”   When evaluating new technologies to drive and support distributed workforces, Hughell suggests paring up individuals, which not only helps people learn, but drives adoption of the new technology and helps the company assess whether it's bringing the expected results. “I always tell my leaders inspect what you expect. Don't just buy a piece of technology, roll it out and expect miracles to happen,” she says. “You have to drive adoption and usage, and you might find that it was the wrong technology and it's not serving your desired outcome or purpose, at which point, make that decision as a leader to fast fail and move on.”   The most important best practice is to do what's necessary to empower your employees to succeed. Providing libraries of bite-size instruction videos, for instance, can help team members learn specific features of new technology related to their work, without having to sit through long training sessions. Creating the atmosphere of learning and collaboration is as important to a company's success as implementing the new technology. “At the end of the day,” says Hughell, “if you can show someone that you're going to help empower their success and help increase their productivity and reduce friction in their day-to-day operation, not many people would argue with that.”   This episode of Business Lab is produced in association with RingCentral.

Artificial intelligence helps build physical infrastructure like modular housing, skyscrapers, and factory floors. “…many problems that we wrestle with in all forms of engineering and design are very, very complex problems…those problems are beginning to reach the limits of human capacity,” says Mike Haley, the vice president of research at Autodesk. But there's hope with AI capabilities, Haley continues “This is a place where AI and humans come together very nicely because AI can actually take certain very complex problems in the world and recast them.” And where “AI and humans come together” is at the start of the process with generative design, which incorporates AI into the design process to explore solutions and ideas that a human alone might not have even considered. “You really want to be able to look at the entire lifecycle of producing something and ask yourself, ‘How can I produce this by using the least amount of energy throughout?'” This kind of thinking will reduce the impact of, not just construction, but any sort of product creation on the planet. The symbiotic human-computer relationship behind generative design is necessary to solve those “very complex problems”—including sustainability. “We are not going to have a sustainable society until we learn to build products—from mobile phones to buildings to large pieces of infrastructure—that survive the long-term,” Haley notes. The key, he says, is to start in the earliest stages of the design process. “Decisions that affect sustainability happen in the conceptual phase, when you're imagining what you're going to create.” He continues, “If you can begin to put features into software, into decision-making systems, early on, they can guide designers toward more sustainable solutions by affecting them at this early stage.” Using generative design will result in malleable solutions that anticipate future needs or requirements to avoid having to build new solutions, products, or infrastructure. “What if a building that was built for one purpose, when it needed to be turned into a different kind of building, wasn't destroyed, but it was just tweaked slightly?” That's the real opportunity here—creating a relationship between humans and computers will be foundational to the future of design. “The consequence of bringing the digital and physical together,” Haley says, “is that it creates a feedback loop between what gets created in the world and what is about to be created next time.” Show notes and references What is Generative Design, and How Can It Be Used in Manufacturing? Four Ways AI in Architecture and Construction Can Empower Building Projects

Next-generation solutions and products are hitting a wall with wi-fi: it's not fast enough, and latency and connectivity issues mean it's not reliable enough. What's an innovator to do? Focus on what's next: 5G and software-defined networking. Nick McKeown, senior vice president and general manager of the network and edge group at Intel Corporation says this technical leap is what will make future innovation possible, “Once you've got a software platform where you can change its behavior, you can start introducing previously absurd-sounding ideas,” including, he continues, “fanciful ideas of automatic, real-time, closed-loop control of an entire network.” While nascent, these technological advancements are already showing promise in practical applications. For example, in industrial settings where there's more analysis happening at the edge, having greater observability into the network is allowing for fine timescale responses to mechanical errors and broken equipment. “Corrective action could be something as mundane as a broken link, a broken piece of equipment, but it could actually be a functional incorrectness in the software that is controlling it,” says McKeown. Grad students and programmers are taking advantage of the advancements in network technology to try out new ideas through academic projects. “One of the key ideas,” says McKeown, “is to verify in real time that the network is operating according to a specification, formally checking against that specification in real time, as packets fly around in the network. This has never been done before.” And although this idea remains in the realm of research projects, McKeown believes it exemplifies the promise of a software-based 5G networking future. Software-defined 5G networking promises applications that we can't yet even imagine, says McKeown. “New IoT apps combined with both public and private 5G is going to create a ‘Cambrian explosion' of new ideas that will manifest in ways that if we were to try to predict, we would get it wrong.”

The internet of things and smart devices are everywhere, which means computing needs to be everywhere too. And this is where edge computing comes in, because as companies pursue faster, more efficient decision-making, all of that data needs to be processed locally, in real time—on device at the edge. “The type of processing that needs to happen in near real time is not something that can be hauled all the way back to the cloud in order to make a decision,” says Sandra Rivera, executive vice president and general manager of the Datacenter and AI Group at Intel. The benefits of implementing an edge-computing architecture are operationally significant. Although larger AI and machine learning models will still require the compute power of the cloud or a data center, smaller models can be trained and deployed at the edge. Not having to move around large amounts of data, explains Rivera, results in enhanced security, lower latency, and increased reliability. Reliability can prove to be more of a requirement than a benefit when users have dubious connections, for example, or data applications are deployed in hostile environments, like severe weather or dangerous locations. Edge-computing technologies and approaches can also help companies modernize legacy applications and infrastructure. “It makes it much more accessible for customers in the market to evolve and transform their infrastructure,” says Rivera, “while working through the issues and the challenges they have around needing to be more productive and more effective moving forward.” A compute-everywhere future promises opportunities for companies that historically have been impossible to realize—or even imagine. And that will create great opportunity says Rivera, “We're eventually going to see a world where edge and cloud aren't perceived as separate domains, where compute is ubiquitous from the edge to the cloud to the client devices.”

Businesses that want to be digital leaders in their markets need to embrace automation, not only to augment existing capabilities or to reduce costs but to position themselves to successfully maneuver the rapid expansion of IT demand ushered in through digital innovation. “It's a scale issue,” says John Roese, global chief technology officer at Dell Technologies. “Without autonomous operations, it becomes impossible to keep up with the growing opportunity to become a more digital business using human effort alone.” The main hurdle to autonomous operations, says Roese, is more psychological than technological. “You have got to be open-minded to this concept of rebalancing the work between human beings and the machine environments that exist both logically and physically,” he says. “If you're not embracing and wanting it to happen and you're resisting it, all the products and solutions we can deliver to you will not help.” Technology and infrastructure-driven AI and ML discussions are expanding beyond IT into finance and sales—meaning, technology has direct business implications. “Selling is a relationship between you and your customer, but there's a third party—data and artificial intelligence— that can give you better insights and the ability to be more contextually aware and more responsive to your customer, says Roese. “Data, AI, and ML technologies can ultimately change the economics and the performance of all parts of the business, whether it be sales or services or engineering or IT.” And as companies gather, analyze, and use data at the edge, autonomous operations become even more of a business necessity. “Seventy percent of the world's data is probably going to be created and acted upon outside of data centers in the future, meaning in edges,” says Roese. “Edge and distributed topologies have huge impacts on digital transformation, but we also see that having a strong investment in autonomous systems, autonomous operations at the edge is actually almost as big of a prerequisite…      to make it work.” Show notes and references What is autonomous operations? Perspectives on the impact of autonomous operations

The global pandemic accelerated the trend toward a work-from-anywhere, distributed workforce. As we approach a post-pandemic world, companies—and employees—expect this trend to become the norm. While IT departments are rapidly configuring and deploying devices, infrastructure, and software to support the shift in a secure and productive way, employees are likewise having to reset priorities and learn new ways to engage with their coworkers and managers, and to navigate their career goals. This shift requires not only changes in technology and IT approaches, but also culture changes for companies and employees alike. Jenn Saavedra, Dell Technologies' chief human resources officer, distills the required cultural shift through the lens of Dell's mission to be people centered. “Our people philosophy,” explains Saavedra, “is ultimately about how to inspire people to be their best and do their best work.” To achieve this goal, Dell focuses on four core areas: (A) achievement, (B) balance, (C) connection, and (D) diversity and inclusion. “We want to enable people to achieve their career goals, to be successful, and continue to grow,     learn, and perform,” she says. None of this was easy—companies across the globe struggled to mitigate the technological and cultural changes required to meet a work-from-anywhere business environment. “Like every other IT organization, we really rallied and made that happen,” says Jen Felch, chief digital officer and chief information officer at Dell Technologies. “You need to keep up so that you are ready for anything, she says. “Companies that were prepared with remote access, with having modern equipment, etc., were more prepared for the rapid work from home.” Looking to the future, Saavedra says companies need to continue to work with—and for—employees. “Because of the degree to which people's lives and routines were disrupted over the past 18 months, we've challenged and redefined the long-held assumptions about the world of work, where we work, how we spend our time.” It's important, Saavedra says, to get past the common mantra that we can't wait for things to go back to normal and to get back to doing things the way we were. “It's really important that we move forward into the future, not look backwards— that's not ever a strategy for success.”   Show notes and references ·      Study: The Data Paradox ·      Study: “Work from anywhere: Empowering the future of work”  ·      Perspectives on workforce and culture trends ·      Dell Digital - Learn more about Dell Technologies' own digital transformation

Hackers around the globe are smart: they know that it isn't just good code that helps them break into systems; it's also about understanding—and preying upon—human behavior. The threat to businesses in the form of cyberattacks is only growing—especially as companies make the shift to embrace hybrid work.   But John Scimone, senior vice president and chief security officer at Dell Technologies, says “security is everyone's job.” And building a culture that reflects that is a priority because cyber attacks are not going to decrease. He explains, “As we consider the vulnerability that industry and organizations face, technology and data is exploding rapidly, and growing in volume, variety, and velocity.” The increase in attacks means an increase in damage for businesses, he continues: “I would have to say that ransomware is probably the greatest risk facing most organizations today.”   And while ransomware isn't a new challenge, it is compounded with the shift to hybrid work and the talent shortage experts have warned about for years. Scimone explains, “One of the key challenges we've seen in the IT space, and particularly in the security space, is a challenge around labor shortages.” He continues, “On the security side, we view the lack of cybersecurity professionals as one of the core vulnerabilities within the sector. It's truly a crisis that both the public and private sectors have been warning about for years.”   However, investing in employees and building a strong culture can reap benefits for cybersecurity efforts. Scimone details the success Dell has seen, “Over the last year, we've seen thousands of real phishing attacks that were spotted and stopped as a result of our employees seeing them first and reporting them to us.”   And as much as organizations try to approach cybersecurity from a systemic and technical perspective, Scimone advises focusing on the employee too, “So, training is essential, but again, it's against the backdrop of a culture organizationally, where every team member knows they have a role to play.”   Related resources: ●     The Data Paradox - Research & Insights ●     Global Data Protection Index

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.”

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.”

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.”

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

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

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

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.”

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.”

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.”

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.

If you drive in the United States, chances are you can't remember the last time you bought a paper map, printed out a digital map, or even stopped to ask for directions. Thanks to GPS and the mobile mapping apps on our smartphones and their real-time routing advice, navigation is a solved problem. But in developing or fast-growing parts of the world: not so much. If you live in a place like Doha, Qatar, where the length of the road network has tripled over the last five years, commercial mapping services from Google, Apple, Bing, or other providers simply can't keep up with the pace of infrastructure change.  “Each one of us who grew up in Europe or the US probably cannot understand the scale at which these cities grow,” says Rade Stanojevic, a senior scientist at the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, a Qatar Foundation university, in Doha. “Pretty much every neighborhood sees a new underpass, new overpass, new large highway being added every couple of months.”  As Qatar copes with this rapid growth—and especially as it prepares to host the FIFA World Cup in 2022—the bad routing advice and accumulating travel delays from outdated digital maps is increasingly costly. That's why Stanojevic and colleagues at QCRI decided to try applying machine learning to the problem. A road network can be interpreted as a giant graph with where every intersection is a node and every road is an edge, says Stanojevic, whose specialty is network economics. Road segments can have both static characteristics, such as the designated speed limit, and dynamic characteristics, such as rush-hour congestion. To see where traffic really is going—rather than where an old map says it should go—and then predict the best routes through an ever-changing maze, all a machine-learning model would need is lots of up-to-data data on both the static and dynamic factors. “Fortunately enough, modern vehicle fleets have these monitoring systems that produce quite a lot of data,” says Stanojevic. Stanojevic is talking about taxis. His team at QCRI partnered with a Doha-based taxi company called Karwa to collect full GPS data on their vehicles' comings and goings. They used that data to build a new mapping service called QARTA that offers routing advice to drivers at Karwa and other operators such as delivery fleets. Stanojevic says QARTA's deeper understanding of the actual road and traffic situation in Doha helps drivers shave tens of seconds off every trip, which translates into a fleet-wide efficiency gain of 5 to 10 percent. “If you're running a fleet of 3,000 cars, five percent of that is 150 cars,” Stanojevic says. “You can basically remove 150 cars from the road and not lose any business.” Although QCRI's system probably can't compete with the big map-services providers in the developed world, it could help cities in the Middle East and other developing regions manage growth more wisely, Stanojevic says. And a few years from now, as more autonomous vehicles take to the streets, machine-learning-based routing advice could look at the big picture in a busy city and help fleets cut carbon emissions by keeping drivers out of traffic jams. “By having some sort of a global view of what's going on in the whole city, autonomous vehicles can actually reroute us to have some sort of global load balancing, to help everyone be better off.” This podcast was produced in partnership with the Qatar Foundation. Show notes and links Qatar Computing Research Institute Sofiane Abbar, Rade Stanojevic, Shadab Mustafa, and Mohamed Mokbel, Traffic Routing in the Ever-Changing City of Doha, Communications of the ACM, April 2021

For the past several years, economists and government leaders have regularly sounded alarms about the dangers of big tech monopolies. On her 2020 campaign website, for example, Senator Elizabeth Warren said “big tech companies have too much power, too much power over our economy, our society, our democracy." In the months since the election, politicians on both the left and right have expressed concerns over how to encourage competition and innovation among the big tech leaders, and even how to hold onto democratic ideals in the face of digital misinformation and conspiracy theories.  The challenge with a company like Facebook is that its business model actively encourages tribalism and anger, which is not the way markets usually work, says Paul Romer, an economics professor at New York University who previously served as the chief economist of The World Bank and was the co-recipient of the 2018 Nobel Prize in Economics Sciences. “When economists defend the market, we have this very simple idea in mind, where I as a buyer give something and get some good back,” he says. “None of those features are characteristic of this new market for digital services, where advertising is like the hidden method of capturing compensation for these firms.”  Users, he says, “are being manipulated in ways that they don't fully understand.” Regulators won't work because big tech firms are too powerful, Romer maintains, while traditional antitrust laws are not well-suited to deal with this problem. However, he says that a progressive tax on digital advertising revenue, passed by state legislatures, could create a unique incentive for companies such as Google and Facebook to split up their businesses and discourage growth by acquisition. Such a progressive tax model, however, needs to be aggressive: “The kind of tax that I think would create a big incentive to change, at say Google and Facebook, the two biggest firms in this market, has to be a tax where the average tax rate they pay right now, given their size, is 35% of their revenue.” Show notes and links: ·     Paul Romer, Taxing Digital Advertising, May 1, 2021 ·     Maryland Breaks Ground with Digital Advertising Tax, National Law Review, March 17, 2021 ·     Once Tech's Favorite Economist, Now a Thorn in Its Side, Steve Lohr, New York Times, May 20, 2021

Executives need to clearly communicate risks but also bring context to data. Tech talk is out: speaking the same language will win the day. It’s drilled into the heads of board directors and the C-suite by scary data-breach headlines, lawyers, lawsuits, and risk managers: cybersecurity is high-risk. It’s got to be on the list of a company’s top priorities. But how many directors get lost in the technicalities of technology? The challenge for a chief information security officer (CISO) is talking to the board of directors in a way they can understand and support the company. Niall Browne, senior vice president and chief information security officer at Palo Alto Networks, says that you can look at the CISO-board discussion as being a classic sales pitch: successful CISOs will know how to close the deal just like the best salespeople do. “That's what makes a really good salesperson: the person that has the pitch to close” he says. “They have the ability to close the deal. So they ask for something.”  “For ages,” Browne says, CISOs have had two big problems with boards. First, they haven’t been able speak the same language so that the board could understand what the issues were. The second problem: “There was no ask.” You can go in front of a board and give your presentation, and the directors can look like they’re in agreement, nodding or shaking their heads, and you can think to yourself, “Job done. They’re updated.” But that doesn’t necessarily mean that the business’s security posture is any better. That’s why it’s important for CISOs to raise the board’s understanding to the level where they know what’s needed and why. Especially when it comes to new advances in cybersecurity, like attack surface management, which is “probably one of the areas that CISOs focus least on and yet is the most important,” Browne says. For example, “many times the CISO and the security team may not be able to see the wood from the trees because they're so involved in it.” And to do that, CISOs need a set of metrics so that anybody can read a board deck and within minutes understand what the CISO is trying to get across, Browne says. “Because for the most part, the data is there, but there's no context behind it.”  This episode of Business Lab is produced in association with Palo Alto Networks.

During the past few months, Microsoft Exchange servers have been like chum in a shark-feeding frenzy. Threat actors have attacked critical zero-day flaws in the email software: an unrelenting cyber campaign that the US government has described as “widespread domestic and international exploitation” that could affect hundreds of thousands of people worldwide. Gaining visibility into an issue like this requires a full understanding of all assets connected to a company’s network. This type of continuous tracking of inventory doesn’t scale with how humans work, but machines can handle it easily. For business executives with multiple, post-pandemic priorities, the time is now to start prioritizing security. “It’s pretty much impossible these days to run almost any size company where if your IT goes down, your company is still able to run,” observes Matt Kraning, chief technology officer and co-founder of Cortex Xpanse, an attack surface management software vendor recently acquired by Palo Alto Networks. You might ask why companies don’t simply patch their systems and make these problems disappear. If only it were that simple. Unless businesses have implemented a way to find and keep track of their assets, that supposedly simple question is a head-scratcher. But businesses have a tough time answering what seems like a straightforward question: namely, how many routers, servers, or assets do they have? If cybersecurity executives don’t know the answer, it’s impossible to then convey an accurate level of vulnerability to the board of directors. And if the board doesn’t understand the risk—and is blindsided by something even worse than the Exchange Server and 2020 SolarWinds attacks—well, the story almost writes itself. That’s why Kraning thinks it’s so important to create a minimum set of standards. And, he says, “Boards and senior executives need to be minimally conversant in some ways about cybersecurity risk and analysis of those metrics.” Because without that level of understanding, boards aren’t asking the right questions—and cybersecurity executives aren’t having the right conversations. Kraning believes attack service management is a better way to secure companies with a continuous process of asset discovery, including the discovery of all assets exposed to the public internet—what he calls “unknown unknowns.” New assets can appear from anywhere at any time. “This is actually a solvable problem largely with a lot of technology that's being developed,” Kraning says. “Once you know a problem exists, actually fixing it is actually rather straightforward.” And that’s better for not just companies, but for the entire corporate ecosystem. Show notes and links: “A leadership agenda to take on tomorrow,” Global CEO Survey survey, PwC

In a recent survey, “2021 Thriving in an AI World,” KPMG found that across every industry—manufacturing to technology to retail—the adoption of artificial intelligence (AI) is increasing year over year. Part of the reason is digital transformation is moving faster, which helps companies start to move exponentially faster. But, as Cliff Justice, US leader for enterprise innovation at KPMG posits, “Covid-19 has accelerated the pace of digital in many ways, across many types of technologies.” Justice continues, “This is where we are starting to experience such a rapid pace of exponential change that it’s very difficult for most people to understand the progress.” But understand it they must because “artificial intelligence is evolving at a very rapid pace.” Justice challenges us to think about AI in a different way, “more like a relationship with technology, as opposed to a tool that we program,” because he says, “AI is something that evolves and learns and develops the more it gets exposed to humans.” If your business is a laggard in AI adoption, Justice has some cautious encouragement, “[the] AI-centric world is going to accelerate everything digital has to offer.” 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 episode was produced in association with KPMG. Show notes and links “2021 Thriving in an AI World,” KPMG

There’s nothing new about conspiracy theories, disinformation, and untruths in politics. What is new is how quickly malicious actors can spread disinformation when the world is tightly connected across social networks and internet news sites. We can give up on the problem and rely on the platforms themselves to fact-check stories or posts and screen out disinformation—or we can build new tools to help people identify disinformation as soon as it crosses their screens. Preslav Nakov is a computer scientist at the Qatar Computing Research Institute in Doha specializing in speech and language processing. He leads a project using machine learning to assess the reliability of media sources. That allows his team to gather news articles alongside signals about their trustworthiness and political biases, all in a Google News-like format. “You cannot possibly fact-check every single claim in the world,” Nakov explains. Instead, focus on the source. “I like to say that you can fact-check the fake news before it was even written.” His team’s tool, called the Tanbih News Aggregator, is available in Arabic and English and gathers articles in areas such as business, politics, sports, science and technology, and covid-19. 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 partnership with the Qatar Foundation. Show notes and links Tanbih News Aggregator Qatar Computing Research Institute “Even the best AI for spotting fake news is still terrible,” MIT Technology Review, October 3, 2018

The digital revolution is here, but not everyone is benefiting equitably from it. And as Silicon Valley’s ethos of “move fast and break things” spreads around the world, now is the time to pause and consider who is being left out and how we can better distribute the benefits of our new data economy. “Data is the main resource of a new digital economy,” says IT for Change director Parminder Singh. Global society will benefit because the economy will benefit, argues Singh, on decentralization of data and distributed digital models. Data commons—or open data sources—are vital to help build an equitable digital economy, but with that comes the challenge of data governance. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff. “Not everybody is sharing data,” says Singh. Big tech companies are holding onto the data, which stymies the growth of an open data economy, but also the growth of society, education, science, in other words, everything. According to Singh, “Data is a non-rival resource. It's not a material resource that if one uses it, other can't use it.” Singh continues, “If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone.” One doesn’t have to look very far to understand the value of non-personal data collected to help the public, consider GIS data from government satellites. Innovation plus the open access to geographic data helped not only create the Internet we know today, but those same tech companies. And this is why Singh argues, “These powerful forces should be in the hands of people, in the hands of communities, they should be able to be influenced by regulators for public interest.” Especially now that most of the data is now collected by private companies. IT for Change is tackling this with a research project called “Unskewing the Data Value Chain,” which is supported by Omidyar Network. The project aims to assess the current policy gaps and new policy directions on data value chains that can promote equitable and inclusive economic development. Singh explains, “Our goal is to ensure the value chains are organized in a manner where the distribution of value is fairer. All countries can digitally industrialize at if not an equal piece, but an equitable pace, and there is a better distribution of benefits from digitalization.” 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 partnership with Omidyar Network. Show notes and links “Unskewing the Data Value Chain: A Policy Research Project for Equitable Platform Economies,” IT for Change, September 2020 “Treating data as commons”, The Hindu, Parminder Singh, September 2, 2020 “Report by the Committee of Experts on Non-Personal Data Governance Framework,” Ministry of Electronics and Information Technology, Government of India “A plan for Indian self-sufficiancy in an AI-driven world,” Mint, Parminder Singh, July 29, 2020

Tim O’Reilly, the “Oracle of Silicon Valley,” wants to shift the conversation about data value to focus on the harm that tech giants are inflicting against us with our own data. It’s “time to wake up and do a better job,” says publisher Tim O’Reilly—from getting serious about climate change to building a better data economy. And the way a better data economy is built is through data commons—or data as a common resource—not as the giant tech companies are acting now, which is not just keeping data to themselves but profiting from our data and causing us harm in the process. “When companies are using the data they collect for our benefit, it's a great deal,” says O’Reilly, founder and CEO of O’Reilly Media. “When companies are using it to manipulate us, or to direct us in a way that hurts us, or that enhances their market power at the expense of competitors who might provide us better value, then they're harming us with our data.” And that’s the next big thing he’s researching: a specific type of harm that happens when tech companies use data against us to shape what we see, hear, and believe. It’s what O’Reilly calls “algorithmic rents,” which uses data, algorithms, and user interface design as a way of controlling who gets what information and why. Unfortunately, one only has to look at the news to see the rapid spread of misinformation on the internet tied to unrest in countries across the world. Cui bono? We can ask who profits, but perhaps the better question is “who suffers?” According to O’Reilly, “If you build an economy where you're taking more out of the system than you're putting back or that you're creating, then guess what, you're not long for this world.” That really matters because users of this technology need to stop thinking about the worth of individual data and what it means when very few companies control that data, even when it’s more valuable in the open. After all, there are “consequences of not creating enough value for others.” We’re now approaching a different idea: what if it’s actually time to start rethinking capitalism as a whole? “It's a really great time for us to be talking about how do we want to change capitalism, because we change it every 30, 40 years,” O’Reilly says. He clarifies that this is not about abolishing capitalism, but what we have isn’t good enough anymore. “We actually have to do better, and we can do better. And to me better is defined by increasing prosperity for everyone.” In this episode of Business Lab, O’Reilly discusses the evolution of how tech giants like Facebook and Google create value for themselves and harm for others in increasingly walled gardens. He also discusses how crises like covid-19 and climate change are the necessary catalysts that fuel a “collective decision” to “overcome the massive problems of the data economy.” 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 episode was produced in partnership with Omidyar Network. Show notes and links “We need more than innovation to build a world that’s prosperous for all,” by Tim O’Reilly, Radar, June 17, 2019 “Why we invested in building an equitable data economy,” by Sushant Kumar, Omidyar Network, August 14, 2020 “Tim O’Reilly - ‘Covid-19 is an opportunity to break the current economic paradigm,’” by Derek du Preez, Diginomica, July 3, 2020 “Fair value? Fixing the data economy,” MIT Technology Review Insights, December 3, 2020

A solar-powered autonomous drone scans for forest fires. A surgeon first operates on a digital heart before she picks up a scalpel. A global community bands together to print personal protection equipment to fight a pandemic. “The future is now,” says Frederic Vacher, head of innovation at Dassault Systèmes. And all of this is possible with cloud computing, artificial intelligence (AI), and a virtual 3D design shop, or as Dassault calls it, the 3DEXPERIENCE innovation lab. This open innovation laboratory embraces the concept of the social enterprise and merges collective intelligence with a cross-collaborative approach by building what Vacher calls “communities of people—passionate and willing to work together to accomplish a common objective.” This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff.  “It’s not only software, it's not only cloud, but it’s also a community of people’s skills and services available for the marketplace,” Vacher says. “Now, because technologies are more accessible, newcomers can also disrupt, and this is where we want to focus with the lab.”   And for Dassault Systèmes, there’s unlimited real-world opportunities with the power of collective intelligence, especially when you are bringing together industry experts, health-care professionals, makers, and scientists to tackle covid-19. Vacher explains, “We created an open community, ‘Open Covid-19,’ to welcome any volunteer makers, engineers, and designers to help, because we saw at that time that many people were trying to do things but on their own, in their lab, in their country.” This wasted time and resources during a global crisis. And, Vacher continues, the urgency of working together to share information became obvious, “They were all facing the same issues, and by working together, we thought it could be an interesting way to accelerate, to transfer the know-how, and to avoid any mistakes.”  Business Lab is hosted by Laurel Ruma, 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 episode of Business Lab is produced in association with Dassault Systèmes.  Show notes and links  How Effective is a Facemask? Here’s a Simulation of Your Unfettered Sneeze, by Josh Mings, SolidSmack, April 2, 2020  Open Covid-19 Community Lets Makers Contribute to Pandemic Relief, by Clare Scott, Dassault, The SIMULIA Blog, July 15, 2020 Dassault 3DEXPERIENCE platform Collective intelligence and collaboration around 3D printing: rising to the challenge of Covid-19, by Frederic Vacher, STAT, August 10, 2020

Some people might not associate the word “trust” with artificial intelligence (AI). Stefan Jockusch is not one of them. Vice president of strategy at Siemens Digital Industries Software, Jockusch says trusting an algorithm powering an AI application is a matter of statistics. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff. “If it works right, and if you have enough compute power, then the AI application will give you the right answer in an overwhelming percentage of cases,” says Jockusch, whose business is building “digital twin” software of physical products. He gives the example of Apple’s iPhones and its facial recognition software—technology that has been tested “millions and millions of times” and produced just a few failures. “That’s where the trust comes from,” says Jockusch. In this episode of Business Lab, Jockusch discusses how AI can be used in manufacturing to build better products: by doing the tedious work engineers have traditionally done themselves. AI can help engineers manage multiple design variations for semiconductors, for example, or sift through routine bug reports that software developers would have had to manually review to figure out what is causing a glitch. “AI is playing a bigger role to allow engineers to focus more on the real, creative part of their job and less on detail work,” says Jockusch. Also in the episode, Jockush explains how AI embedded in products themselves have already won over millions of people—think voice assistants like Siri and Alexa—and will someday become such a common component that people will barely talk about the value or the future of AI. “I mean, how many discussions do you have nowadays about the value of Excel, of cellular calculation, although we use it every day?” says Jockusch. “Everybody uses it every day in something, and it’s so universal that we hardly ever think about it.”

2020 has created more than a brave new world. It’s a world of opportunity rapidly pressuring organizations of all sizes to rapidly adopt technology to not just survive, but to thrive. And Andrew Dugan, chief technology officer at Lumen Technologies, sees proof in the company’s own customer base, where “those organizations fared the best throughout covid were the ones that were prepared with their digital transformation.” And that’s been a common story this year. A 2018 McKinsey survey showed that well before the pandemic 92% of company leaders believed “their business model would not remain economically viable through digitization.” This astounding statistic shows the necessity for organizations to start deploying new technologies, not just for the coming year, but for the coming Fourth Industrial Revolution. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff. Lumen plans to play a key role in this preparation and execution: “We see the Fourth Industrial Revolution really transforming daily life ... And it's really driven by that availability and ubiquity of those smart devices.” With the rapid evolution of smaller chips and devices, acquiring analyzing, and acting on the data becomes a critical priority for every company. But organizations must be prepared for this increasing onslaught of data. As Dugan says, “One of the key things that we see with the Fourth Industrial Revolution is that enterprises are taking advantage of the data that's available out there.” And to do that, companies need to do business in a new way. Specifically, “One is change the way that they address hiring. You need a new skill set, you need data scientists, your world is going to be more driven by software. You’re going to have to take advantage of new technologies.” This mandate means that organizations will also need to prepare their technology systems, and that’s where Lumen helps “build the organizational competencies and provide them the infrastructure, whether that’s network, edge compute, data analytics tools,” continues Dugan. The goal is to use software to gain insights, which will improve business. When it comes to next-generation apps and devices, edge compute—the ability to process data in real time at the edge of a network (think a handheld device) without sending it back to the cloud to be processed—has to be the focus. Dugan explains: “When a robot senses something and sends that sensor data back to the application, which may be on-site, it may be in some edge compute location, the speed at which that data can be collected, transported to the application, analyzed, and a response generated, directly affects the speed at which that device can operate.” This data must be analyzed and acted on in real time to be useful to the organization. Think about it, continued Dugan, “When you’re controlling something like an energy grid, similar thing. You want to be able to detect something and react to it in near real time.” Edge compute is the function that allows organizations to enter the Fourth Industrial Revolution, and this is the new reality. “We're moving from that hype stage into reality and making it available for our customers,” Dugan notes. “And that's exciting when you see something become real like this.” Business Lab is hosted by Laurel Ruma, 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 episode was produced in partnership with Lumen Technologies. Links “Emerging Technologies And The Lumen Platform,” Andrew Dugan, Automation.com, Sept 14, 2020 “The Fourth Industrial Revolution: what it means, how to respond,” Klaus Schwab, The World Economic Forum, Jan 14, 2016 “Why digital strategies fail,” Jacques Bughin, Tanguy Catlin, Martin Hirt, and Paul Willmott, McKinsey Quarterly, Jan 25, 2018

Ask Stefan Jockusch about what a factory might look like in 10 or 20 years, and the answer might leave you at a crossroads between fascination and bewilderment. Jockusch is vice president for strategy at Siemens Digital Industries Software, which develops applications that simulates the conception, design, and manufacture of products such as a cell phone or a smart watch. His vision of a smart factory is abuzz with “independent, moving” robots. But they don’t stop at making one or three or five things. No—this factory is “self-organizing.” “Depending on what product I throw at this factory, it will completely reshuffle itself and work differently when I come in with a very different product,” Jockusch says. “It will self-organize itself to do something different.” Behind this factory of future is artificial intelligence (AI), Jockusch says in this episode of Business Lab. But AI starts much, much smaller, with the chip. Take automaking. The chips that power the various applications in cars today—and the driverless vehicles of tomorrow—are embedded with AI, which support real-time decision-making. They’re highly specialized, built with specific tasks in mind. The people who design chips then need to see the big picture. “You have to have an idea if the chip, for example, controls the interpretation of things that the cameras see for autonomous driving. You have to have an idea of how many images that chip has to process or how many things are moving on those images,” Jockusch says. “You have to understand a lot about what will happen in the end.” This complex way of building, delivering, and connecting products and systems is what Siemens describes as “chip to city”—the idea that future population centers will be powered by the transmission of data. Factories and cities that monitor and manage themselves, Jockusch says, rely on “continuous improvement”: AI executes an action, learns from the results, and then tweaks its subsequent actions to achieve a better result. Today, most AI is helping humans make better decisions. “We have one application where the program watches the user and tries to predict the command the user is going to use next,” Jockusch says. “The longer the application can watch the user, the more accurate it will be.” Applying AI to manufacturing, Jockusch says, can result in cost savings and big gains in efficiency. Jockusch gives an example from a Siemens factory of printed circuit boards, which are used in most electronic products. The milling machine used there has a tendency to “goo up over time—to get dirty.” The challenge is to determine when the machine has to be cleaned so it doesn’t fail in the middle of a shift. “We are using actually an AI application on an edge device that's sitting right in the factory to monitor that machine and make a fairly accurate prediction when it's time to do the maintenance,” Jockusch says. The full impact of AI on business—and the full range of opportunities the technology can uncover—is still unknown. “There's a lot of work happening to understand these implications better,” Jockusch says. “We are just at the starting point of doing this, of really understanding what can optimization of a process do for the enterprise as a whole.” Business Lab is hosted by Laurel Ruma, 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 episode was produced in partnership with Siemens Digital Industries Software.

[Sponsored] AI advancements today are pointing to improvements everywhere you look. But it’s a confluence of technologies—cloud, 5G wireless, smart devices, and more—that will usher in the greatest results, predicts Dell Technologies’ John Roese. If you need a reason to feel good about the direction technology is going, look up Dell’s CTO John Roese on Twitter. The handle he composed back in 2006 is @theICToptimist. ICT stands for information and communication. “The reason for that acronym was because I firmly believed that the future was not about information technology and communication technology independently,” says Roese, president and chief technology officer of products and operations at Dell Technologies. “It was about them coming together.” Close to two decades later, it’s hard not to call him right. Organizations are looking to the massive amounts of data their collecting and generating to become fully digital, they’re using the cloud to process and store all that data, and they’re turning to fast, new wireless technologies like 5G to power data-hungry applications such as artificial intelligence and machine learning. In this episode of Business Lab, Roese walks through this confluence of technologies and its future outcomes. For example, autonomous vehicles are developing fast, but fully driverless cars aren’t plying are streets yet. And they won’t until they tap into a “collaborative compute model”—smart devices that plug into a combination of cloud and edge computing infrastructure to provide “effectively infinite compute.” “One of the biggest problems isn't making the device smart; it's making the device smart and efficient in a scalable system,” Roese says. So big things are ahead, but technology today is making huge strides, Roese says. He talks about machine intelligence, which taps AI and machine learning to mimic human intelligence and tackle complex problems, such as speeding up supply chains, or in health care, more accurately detecting tumors or types of cancer. And opportunities abound. During the coronavirus pandemic, machine intelligence can “scale nursing” by giving nurses data-driven tools that allow them to see more patients. In cybersecurity, it can keep good guys a step ahead of innovating bad guys. And in telecommunications, it could eventually make decisions regarding mobile networks “that might have a trillion things on them. That is a very, very, very large network that exceeds human's ability to think.” Business Lab is hosted by Laurel Ruma, 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 episode was produced in partnership with Dell Technologies. Show notes and links Technical Disruptions Emerging in 2020, by John Roese The Journey to 5G: Extending the Cloud to Mobile Edges, EmTech Next 2020 Meet John on Twitter, @theictoptimist The Fourth Industrial Revolution and digitization will transform Africa into a global powerhouse, by Njuguna Ndung’u and Landry Signé

[Sponsored] The coronavirus pandemic has led to enhanced collaboration, spurred innovation, and increased the use of digital technologies. Telehealth enables doctors to safely connect with patients virtually and to monitor them remotely, whether in different cities or just down the hall. And smarter and smaller medical devices are producing better outcomes for patients—a disruption is sensed, like low blood sugar or a too rapid beating heart, and a therapy is applied, in real time. All of this is aided by improved processing capabilities and data—lots of data, which means AI. And today’s guest is Dr. Laura Mauri, who is the Vice President of Global Clinical Research and Analytics at Medtronic. And she knows all about how data can help drive better patient outcomes, improve the patient experience, and provide valuable information for doctors and medical device creators. Dr. Mauri is an interventional cardiologist and one of the world’s leading experts on clinical trials, but, as she says, the success of a clinical trial really does come down to the patient experience, and how it's improved. Dr. Mauri also has great hope for healthcare and technology. And although she cautions that this work is not simple, you can literally see progress happening—which is the outcome we all want. Business Lab is hosted by Laurel Ruma, 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. Produced in partnership with Medtronic. Show notes and links “Unlocking the power of data in healthcare” A Q&A with Dr. Laura Mauri Open-Source Release Allows Coventor to Be Produced Worldwide Virtual training, remote monitoring solutions provide safety and support

As technology rapidly develops, the number of security and privacy concerns will only continue to grow. In this episode, we look at how companies can build cybersecurity into their business strategies—instead of scrambling to respond when a breach happens. Even with danger lurking around the corner, today’s guest, cybersecurity expert Ann Cavoukian, argues that companies are turning a blind eye to security and privacy issues until it is too late. Cavoukian is the executive director of the Global Privacy and Security by Design Centre, as well as a senior fellow of the Ted Rogers Leadership Centre at Ryerson University. She’s worked closely with the government in Canada as well as private companies on the best way to defend against security attacks. Cavoukian also says that privacy is vital to our society and an indispensable form of freedom, and that developments such as facial recognition technology are among the most egregious breaches of that freedom. Business Lab is hosted by Laura Ruma, 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. Music is by Merlean, from Epidemic Sound.  Show notes and links Ann Cavoukian, Ryerson University Global Privacy and Security by Design “Microsoft presents Dr. Ann Cavoukian on privacy and your business,” YouTube “Dr Ann Cavoukian – Privacy By Design,” YouTube “Will Privacy First Be The New Normal? An Interview With Privacy Guru, Dr. Ann Cavoukian,” by Hessie Jones, Forbes “Dr. Ann Cavoukian: Why Big Business Should Proactively Build for Privacy,” by Hessie Jones, Forbes

In this episode, we look at the need to secure the internet of things, physical workspaces, and the products companies make. From planes to children’s toys to oil rigs, more connected devices are vulnerable to attack than ever before. Ken Munro is an internet-of things security researcher, penetration tester, and writer with two decades of experience in the security industry. He is also the founder of security services company Pen Test Partners. Munro helps expose the vulnerabilities in items we use every day, and he discusses some of the most important skills that cybersecurity experts can have, why companies are at risk for physical security breaches, and something he calls “supersystemic flaws.”  Business Lab is hosted by Laurel Ruma, 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. Music is by Merlean, from Epidemic Sound. Ken Munro, on Twitter Ken Munro, Pen Test Partners “Kids Tracker Watches: CloudPets, exploiting athletes and hijacking reality TV,” Pen Test Partners Security Blog “Think you’ve had a breach? Top 5 things to do,” Pen Test Partners Security Blog  “Internet of Things Security,” a TEDx presentation by Ken Munro

As the new year (and new decade) begins, one thing is certain: cybersecurity will continue to have an increasing impact on business, for better or worse. In this episode, we hear from Stephanie Balaouras, a cybersecurity expert who has spoken to thousands of customers over her 15 years at Forrester Research. She is the vice president and group director of security and risk research, as well as infrastructure and operations research. Balaouras makes the case that all businesses should have a chief information security officer, or CISO, as the world of cyberthreats becomes more intricate and perilous. "Even companies that have a CISO should take a hard look at how high in the organization they report," Balaouras says. "Do they have the right budget? Do they have enough staff? Have you given them the right span of control?" Balaouras also reviews some of the biggest cybersecurity trends in 2019 and makes predictions for 2020. Business Lab is hosted by Laurel Ruma, 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. Music by Merlean, from Epidemic Sound. From the sponsor Cybersecurity isn’t only about stopping the threats you see, it’s about stopping the ones you can’t see. That’s why Microsoft Security employs over 3,500 cybercrime experts, and uses AI to help anticipate, identify and eliminate threats. So you can focus on growing your business, and Microsoft Security can focus on protecting it. Learn more at //Microsoft.com/Cybersecurity. https://www.forrester.com/Stephanie-Balaouras https://go.forrester.com/blogs/category/cybersecurity/ https://go.forrester.com/blogs/a-cisos-guide-to-leading-change/ https://www.forrester.com/report/The+Biggest+Trends+Shaping+Enterprise+Risk+Management+In+2020/-/E-RES148895 https://www.youtube.com/watch?v=MNYcRa1JWuA

From 1999 to 2012, Marissa Mayer was one of the most public faces at Google, where she helped to build the company’s core search and advertising platforms. From 2012 to 2017 she steered Yahoo! through its final years as an independent business. In other words, she’s spent a long time at the center of the Silicon Valley whirlwind.  In this special episode, Business Lab host Elizabeth Bramson-Boudreau asks Mayer how conditions for women technology leaders have changed during her career. The conversation quickly turns to the thinking behind Mayer's 2013 decision to put an end to Yahoo's fairly permissive policy around working from home and how she dealt with the blowback from that decision on social media and the technology press. Mayer sys that if a leader is trying to foster a stronger culture inside their company, they can’t worry too much about what everyone outside the company is saying about them. Mayer goes on to speak about her new company, Lumi Labs, where she says engineers are looking for everyday consumer applications for the latest artificial intelligence techniques. And ultimately the conversation returns to the question of how technology companies can move closer to gender parity, and why the drive to recruit more women into technical roles has to come from the very top. This episode is sponsored by (ISC)2. With more than 140,000 global members, (ISC)2 is the world's largest non-profit membership association of certified cybersecurity professionals. It offers a portfolio of credentials that are part of a holistic programmatic approach to security.