Business Lab

Successfully improving customer satisfaction through AI means becoming data-driven, prioritizing employee feedback and resources, and letting business goals guide technology deployment, says senior product marketing manager at NICE, Michele Carlson.

Integrating data and AI solutions throughout the customer experience journey can enable enterprises to become predictive and proactive, says vice president of product marketing at NICE, Andy Traba.

AI can create seamless customer and employee experiences but it’s important to balance automation and human touch, says head of marketing, digital & AI at NICE, Elizabeth Tobey.

Operationalizing data allows enterprises to optimize decision-making, deliver growth, and improve customer experiences, according to Mark Birkhead, firmwide chief data officer at JPMorgan Chase.

Amid shifting customer needs, CPG enterprises look to machine learning to bolster their data strategy, says global head of MLOps and platforms at Kraft Heinz Company, Jorge Balestra.

Deploying blockchain technology can bolster innovation and create a more secure way to bank, according to Suresh Shetty, the CTO at Onyx by JP Morgan at JPMorgan Chase.

Successful digital transformation starts with the right team, an agile mentality, and a strong data foundation, says global digital solutions manager of procurement and supply chain at bp, Raimundo Martinez.

Investments into downsized infrastructure can help enterprises reap the benefits of AI while mitigating energy consumption, says corporate VP and GM of data center platform engineering and architecture at Intel, Zane Ball.

Deploying sustainable software practices can reduce emissions and infuse greater efficiency, resiliency, and cost-effectiveness.

Immersive AR/VR technologies can add greater value across workplaces and customer interactions, according to global head of Immersive Technology Research at JPMorgan Chase, Blair MacIntyre.

Sustainable computing practices have the power to both infuse operational efficiencies and greatly reduce energy consumption, says Jen Huffstetler, chief product sustainability officer at Intel.

Data — how it’s stored and managed — has become a key competitive differentiator. As global data continues to grow exponentially, organizations face many hurdles between piling up historical data, real-time data streams from IoT sensors, and building data-driven supply chains. Senior vice president of product engineering at Hitachi Vantara, Bharti Patel sees these challenges as an opportunity to create a better data strategy.

The road to decarbonization is daunting, especially while trying to keep pace with innovation. According to the director of data platform product marketing at Hitachi Vantara, Ian Clatworthy, companies need to take their own initiative when it comes to sustainability measures and just start somewhere.

AI can enable business transformation to deliver positive outcomes and propel sustainability goals. Delivering the best outcomes means optimizing operations and forging clear digital strategies for business transformation that are focused on resolving client and business challenges.

Implementing strong working methodologies, cost-efficient cloud migrations, and emerging AI and machine learning tools build better experiences at J.P. Morgan Private Bank.

In 2017, BP took on a cloud-first approach that committed to building any new hardware or system builds on the cloud. Just a year prior, only 2% of BP applications lived on the cloud. At the close of 2022, 90% of BP applications had migrated to cloud environments, changing product and service integration and BP’s overall digital operating model.Cloud transformations like BP’s can help enterprises improve operational resiliency, accelerate technology adoption, and reduce data center carbon emissions, says vice president of Digital Foundations at BP, Keisha Garcia. Migrating to the cloud at that scale, however, is challenging and extensive, especially when dealing with a legacy IT estate.“Utilizing cloud platforms provides the necessary computational power and tools to implement advanced analytics, predictive modeling, as well as simulation techniques, which also enables us to continuously improve our sustainability performance,” says Garcia.To successfully migrate to the cloud and subsequently collaborate and deploy cloud technologies, Garcia stresses the importance of clear communication among employees as well as stakeholders. “Involve application teams, service owners, end users early in the development and delivery of the strategy. Again, just bringing everyone along for the journey, I cannot overstate how important that is,” says Garcia.A hybrid approach to transformation that combines cloud migration with the retention of some applications, dedicated data centers, and intermediary migration environments can ensure cost effective and secure operations. With enterprise-wide communication underpinning any successful transformation, Garcia outlines having a strong and flexible governance framework, collaborating with external digital partners, and adapting to agile ways of working as best practices for complex cloud migrations.Looking to the cloud-enabled future, Garcia identifies the convergence of AI and edge computing, mounting progress in quantum computing, and the proliferation of IoT connected devices as transformative technologies that will drive forward better business outcomes. “I think that the convergence of edge computing and AI presents an exciting opportunity for the real-time data, a real-time low latency processing and decision making at the network edge, which is extremely critical for us, given all of the platforms, rigs that we have out across the globe,” says Garcia.

Building fair and transparent systems with artificial intelligence has become an imperative for enterprises. AI can help enterprises create personalized customer experiences, streamline back-office operations from onboarding documents to internal training, prevent fraud, and automate compliance processes. But deploying intricate AI ecosystems with integrity requires good governance standards and metrics.To deploy and manage the AI lifecycle—encompassing advanced technologies like machine learning (ML), natural language processing, robotics, and cognitive computing—both responsibly and efficiently, firms like JPMorgan Chase employ best practices known as ModelOps.These best governance practices involve “establishing the right policies and procedures and controls for the development, testing, deployment and ongoing monitoring of AI models so that it ensures the models are developed in compliance with regulatory and ethical standards,” says JPMorgan Chase managing director and general manager of ModelOps, AI and ML Lifecycle Management and Governance, Stephanie Zhang.Because AI models are driven by data and environment changes, says Zhang, continuous compliance is necessary to ensure that AI deployments meet regulatory requirements and establish clear ownership and accountability. Amidst these vigilant governance efforts to safeguard AI and ML, enterprises can encourage innovation by creating well-defined metrics to monitor AI models, employing widespread education, encouraging all stakeholders’ involvement in AI/ML development, and building integrated systems.“The key is to establish a culture of responsibility and accountability so that everyone involved in the process understands the importance of this responsible behavior in producing AI solutions and be held accountable for their actions,” says Zhang.

From securing a hybrid workforce to building pipelines for ever-increasing data streams and keeping multiple mission-critical systems up and running, the modern IT department faces numerous pressures. As director of IT for the packaged food company Conagra, Amit Khot is optimistic about the ways modern technology solutions and infrastructure can enable businesses to thrive and innovate.Khot describes the power of advanced data analytics to both improve a company’s understanding of its customers and to optimize its operations. The ability to combine internal company data with data collected from social media and at point of sale will enable savvy companies to recognize new patterns. These advanced analytics, he says, will go beyond answering standard questions about financials and historical performance to provide insight into more complex questions about customers’ thoughts and changing preferences.Meanwhile, these same data tools can also be used to fine-tune daily business operations, pinpointing issues with order fulfillment, improving long-range supply-and-demand forecasting, and digitizing manufacturing plant processes. Koht explains, “Planning is looking into the future, depending upon your past historical data, as to what your future demand and supply should look like. We have gone through a journey to modernize our planning platforms.”A modern enterprise resource planning (ERP) system is also a must for a distributed organization like Conagra. A single connected ERP system can manage and provide visibility into business processes that involve multiple divisions or departments. By doing so, a modern ERP can also ease highly complex processes, such as the technology integration of a newly acquired company.Says Khot, “having a single view of finance, having a single view of the supply chain as early and as fast as possible, is one of the most important things that can help us get synergies out of the business as fast as possible.”

From traditional manufacturing companies using AI in robots to build smart factories to tech startups developing automated customer service and chatbots, AI is becoming pervasive across industries.“AI is no longer just in assistant mode, but is now playing autonomous roles in robotics, driving, knowledge generation, simulating our hands, feet, and brains,” says Lan Guan, global lead for data and AI at Accenture.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.Researchers are similarly unlocking the value of AI through machine learning and robots that are developed to augment rather than replace human capabilities across manufacturing, health care, and space exploration. The robots of the past were kept in cages on factory floors and in labs, but this new era of AI-enabled robotics allows humans to work interdependently with robots to boost productivity, increase quality of work, and enable greater flexibility, says Julie Shah, professor in the department of aeronautics at MIT. Shah is also the co-lead of the Work of the Future Initiative at MIT.“Sometimes it can feel as though the emergence of these technologies is just going to sort of steamroll and work and jobs are going to change in some predetermined way because the technology now exists,” says Shah. “But we know from the research that the data doesn't bear that out actually.”Although there are longstanding concerns about AI taking jobs and vastly changing workforces across the globe, Guan and Shah paint a picture of a future where AI empowers and supports human innovation. Taking a human-centered approach enables human invention and ingenuity to be augmented by AI and AI-enabled technologies. A critical question Shah asks throughout her research is: “How do we develop these technologies such that we're maximally leveraging our human capability to innovate and improve how we do our work?”With more and better collected data, researchers and organizations alike have the opportunity to learn from the future when deploying AI and robotics. From ethics to varied use cases, the AI landscape is constantly shifting and decisions that academics and enterprises make now can have long term ramifications. A strategic practice of foresight offers a solution of envisioning multiple futures and forming strategies in the present.“I'm very optimistic about all these amazing aspects of flexibility, resilience, specialization, and also generating more economic profit, economic growth for the society aspect of AI,” says Guan. “As long as we walk into this with eyes wide open so that we understand some of the existing limitations, I'm sure we can do both of them.”

As the impacts of climate change like desertification, biodiversity loss, pollution, and severe weather persist, the United Arab Emirates (UAE) is approaching the green transition and decarbonization with a great sense of urgency. Between its ratification of the 2015 Paris Agreement, its $160 billion investment in renewables over the next 30 years, and its green initiatives collaborations with other nations and private sector leaders, the UAE has made aggressive efforts toward a net-zero future—leading its region in the process.  While full-scale decarbonization will require a systemic shift in the UAE’s energy, manufacturing, and transport sectors, there is an enormous opportunity for low carbon sustainable economic development, says UAE Minister of State for Public Education and Advanced Technology, Her Excellency Sarah Bint Yousif Al Amiri.Building the low-carbon  industries of the future means leveraging advanced and emerging technologies like AI, IoT, and robotics to improve efficiency, incentivizing energy efficiency among manufacturers, and promoting scalable decarbonization best practices.As one of the world’s largest integrated energy companies, ADNOC, is faced with a generational challenge of minimizing emissions while maximizing energy outputs, says ADNOC Executive Director of Low Carbon Solutions and International Growth, Musabbeh Al Kaabi.Beyond implementing nature based solutions such as mangrove planting, ADNOC is implementing and piloting new technology to permanently remove carbon through mineralization, says Al Kaabi. Startups and players outside the traditional energy sector are also emerging with new innovations employing AI, supercomputing, and big data analytics that can help accelerate the energy transition.By establishing a resilient science and technology ecosystem within the UAE and investing in clean energy projects and renewables worldwide, the nation looks to address climate change challenges regionally and globally, says Al Amiri. Looking forward, these investments and policies will create new green business models and services that can enable the UAE to achieve both carbon neutrality and strong economic growth through its pragmatic, resilient, and inclusive approach.