Business Lab

As businesses look to get the greatest value from their data, investments in cloud infrastructure from customer relationship management (CRM) systems to email to points of sale can help make data more accessible and bolster innovation, says PwC principal in the analytics insights practice, Anil Nagaraj and Microsoft director of product management Azure Synapse Analytics and Power BI, Kim Manis.

Weighed down by legacy systems and rapidly increasing data volumes, many businesses have begun to migrate to cloud infrastructures to modernize their platform. According to Manis, the clearest benefits of moving to the cloud are speed and time to market as cloud platforms allow businesses to focus on core needs and customers rather than infrastructure and integration.

Although migration to the cloud can help businesses focus on their core competencies, making data accessible is key to becoming data-driven.

“The most important thing is building that data culture,” says Manis. “The people making decisions every day in your business actually use that data. So it doesn't matter how much data you have or how many metrics are being tracked in some spreadsheets somewhere if nobody's actually using it to make decisions.”

Industry-leading practices for strengthening data culture and real-time decision-making, says Nagaraj, include empowering teams across the enterprise with data, creating a culture of openness and transparency, and encouraging new innovations utilizing optimized data.

“I think we’ve got to break those barriers to make sure data is truly available to end business users through multiple experiences that you can bring about,” says Nagaraj.

Technologies like AI and machine learning can help enable new business innovations and improve data literacy without the intermediary help from data scientists. However, on top of maintaining interoperability between emerging technologies with cloud migration, businesses, especially those in the finance and healthcare sector, should also focus on data governance. 

“I think it comes down to the data needs to be trusted in the first place and in the right structure in the first place for these AI capabilities to work,” says Manis. “No one is going to trust the new AI capabilities if they don't trust the data. And that's where the governance piece comes in.”

Looking forward, Nagaraj forecasts that cross-industry and cross-customer shared data can help enable critical decision making.

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Building a better train doesn’t end with delivering the railcars. When Siemens was asked to improve train reliability, the company added sensors and built digital models that could predict the need for door maintenance 10 days before a door actually got stuck—allowing mechanics to prevent delays before they happened.

Peter Koerte, chief technology and strategy officer at Siemens, says the potential of the industrial metaverse doesn’t end there. “The minute you start to connect real-world operations with a digital simulation thereof,” he says, “you can enable a lot of new services you even hadn’t thought about in the beginning.” When the covid-19 pandemic struck, and transit usage plummeted, those same sensors were repurposed to monitor capacity and ridership.

The industrial metaverse will provide an interface between the real world and a digital world, built on simulations and digital models of complex human systems like machines, factories, or cities. Koerte names five building blocks that will help the industrial metaverse achieve its full potential: these detail-perfect models, termed “digital twins”; simulations based on realistic physics; tools for seamless virtual collaboration; the ability to build immersive and photorealistic environments; and computing power that allows real-time responsiveness. All these capabilities are developing, and their continual improvement will fuel industrial metaverse innovation.

The industrial metaverse will be a powerful accelerator for digital transformation. The power of simulation will allow designers and manufacturers to get things right in the digital world before committing physical resources. 

And while interoperability and platform challenges will have to be overcome to bring the industrial metaverse to fruition, Koerte is enthusiastic about its revolutionary potential. The metaverse will speed progress toward sustainability goals, he says, by conserving physical resources, building carbon considerations into design processes, enabling more accurate accounting of emissions, and advancing digitalization. He explains, “there’s a lot of very real-world applications that can help us make this much, much better.”

This episode of Business Lab is produced in partnership with Siemens.

The emergent industrial metaverse, report, MIT Technology Review Insights, March 29, 2023

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When seemingly disparate fields, industries, and ways of thinking merge, a convergence happens, which, has the power to build more intuitive and advanced futures for both organizations and the everyday consumer, says Accenture communications, media and technology industry group chair, Kathleen O’Reilly and Daniela Rus, Director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology.

“Today, the kind of superpowers that seem to belong in storybooks can be achieved by mathematical models, computation, new materials, AI, robotics–this convergence of fields,” says Dr. Rus.

This episode is part of our “Building the future” podcast series. It’s a multi-episode series focusing on how organizations, researchers, and innovators are meeting our evolving global challenges. We understand the importance of inclusive conversations and have chosen to highlight the work of women on the cutting edge of technological innovation, and business excellence.

A combination of technology and human ingenuity will push boundaries as companies look to enter a new wave of innovation through data and AI to enable growth. Although O’Reilly estimates that we’re in the early stages of this transformation, she predicts that this convergence will be the biggest change since the industrial revolution.

“We are seeing with the exponential pace of technological innovation, which we believe is going to continue, that this is really creating an opportunity for one of the most exciting periods of positive change and progress for all of history,” says O’Reilly.

Much of this acceleration occurred over the course of these last pandemic years as many businesses and consumers alike take advantage of remote working operations including digital payments, telehealth appointments, and AR/VR experiences. But to anticipate and learn from the future, organizations and leaders always need to look to data and the insights derived from it.

“Intentional futurists,” says O’Reilly, “use AI-based analysis to find patterns, anticipate trends, detect new sources of growth opportunities, understand their consumers, their customers, other enterprises, the markets and their employees better.”

Practically, to bring this convergence from both leadership and academia, organizations need to be mindful of regulations and ethics to drive forward positive innovation and transformation.

“Whether you are a technologist, a national security leader, a policymaker or a human being,” says Dr. Rus. “We all have a moral obligation to use the AI tools to make our world safer, and better, and to make the lives of our citizens safer and better in a just and equitable way.”
 

This episode of Business Lab is produced in association with Accenture.

• Transforming the industry that transformed the world, 2022, Accenture
• Convergence unlocks adjacent growth, 2022, Accenture
• Equality = Innovation, 2019, Accenture

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Making the IT mindset shift from simply offering a technical service to offering an outcome can be a challenge. It’s the difference between offering a customer a laptop and offering them the tools to create a workplace environment that empowers productivity, says Art Hu, Lenovo’s senior vice president and global chief information officer. Once IT teams make this change to focus on business language and outcomes, they can move in the direction of business value delivery and scalability.

Creating a culture of curiosity that values this shift requires a commitment to a long-term journey that prioritizes research and development (R&D) and finding the right business processes to unleash creativity among employees.

“As you know, innovation starts smaller,” says Hu. “It's not possible that everything immediately is something that's billions of dollars and thousands of employees and dozens of countries around the world. You have to seed these things.”

Effectively manufacturing new innovations, especially those that involve emerging technologies like AI and the industrial metaverse, is underpinned by this strong focus on R&D, says Hu.

“There is no place,” says Hu, “if you think about logistics, planning, production, scheduling, shipping, where we didn't find AI and metaverse use cases that were able to significantly enhance the way we run our operations.”

Hu sees the continued potential of AI, how digital transformation will affect and contribute to the recent adoptions of hybrid work, and the continued adoption of as a service to improve agility within enterprises.

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Across industries, technology transformation is a necessity for businesses looking to ensure longevity and remain competitive. But how can a global finance or health care company use technology to create value for its employees and customers at scale? For large-scale technology transformations, laying the right foundation is the key to ensuring success, says Chad Ballard, head of global banking platform tech, consumer and community banking at JPMorgan Chase.

“It starts and ends with focusing on what customers want: the customer is at the center of what we do,” says Ballard. “And so, we’ve got to bring technology—the way that we think about product, the way that we think about design, and the way that we think about data—together in order to be able to deliver that product for the customer.”

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The acceleration of digital transformation and speed of customer demands is turning almost every business into a technology business. Creating, using, or selling technology is now a critical part of every enterprise. But how do companies add emerging technologies and innovations? 

Many companies looking to enter the software economy, the ecosystem of companies that create or are enabled by software, do so through acquisitions, often by targeting startups. Evaluating the potential value of these smaller companies, however, is a specialized skill, says Jeff Vogel, head of the Software Strategy Group for EY-Parthenon. For companies, discovering and accounting for hidden talent and technology risks is a big factor in a successful merger or acquisition.

“They need to believe in the market, that there’s room to grow in that market or room to expand the market; believe in the company’s ability to execute; or believe that they’re coming with a transformation thesis that they’re going to fundamentally change what the company does and how it does it in order to recognize their return,” says Vogel.

Non-technical companies often look to acquisitions, particularly of startups that are touting emerging technology, to make business processes run more efficiently. They also see software investments offering opportunities for high growth and generally high gross margins. But it’s important to gauge the risk and the reward of acquiring software, Vogel says. In the same way that entering the software economy can yield high growth, the market moves fast, making it easy to lose value just as easily as it is gained.

While there are always risks in business, Vogel says that one indicator of a strong acquisition is talent retention and culture. A lack of synergy between company cultures and poorly managed or deployed talent can pose barriers to a smooth acquisition and integration.

“Because software is an intangible IP and it’s very much tied to the people who build it and maintain it, if you have talent drains due to culture, compensation, or other things after an acquisition, that’s usually the leading indicator that the thesis is going to go up in smoke,” says Vogel.

This episode of Business Lab is produced in association with EY-Parthenon. Learn more about EY-Parthenon’s disruptive technology solutions at ey.com/us/disruptivetech.

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Episode notes and transcript can be found here.

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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.

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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.

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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

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