Intelligent Automation
Transformation in a Time of Uncertainty
May 4, 2023 | New York
Companies large and small are faced with an uncertain economy brought about by rising prices, higher wages and fears of shrinking growth rates. The Bloomberg Intelligent Automation Roadshow made a stop at the company’s headquarters in New York to take another deep dive into the ways in which organizations can offset economic pressures and thrive by implementing intelligent automation systems that enhance operational efficiencies and stakeholder value
Click here to watch the full May 4 briefing.
Speakers:
- Art Amador, Co-Founder & Chief Operating Officer, EquBot
- Yin Aphinyanaphongs, Director of Operational Data Science & Machine Learning, NYU Langone Health
- Jack Berkowitz, Chief Data Officer, ADP
- Alex Cook, Senior Vice President & Head of Strategic Capabilities, New York Life
- Brian Fallon, Vice President, Data, AI & Automation, IBM
- Charles Gagnon, Chief Information Officer, New York Genome Center
- Hari Gopalkrishnan, Head of Retail, Preferred, Small Business & Wealth Management Technology, Bank of America
- Neil Green, Executive Vice President & Chief Digital Officer, Otis
- Suvankar Mazumder, Chief Technology Officer, S&P Global Ratings
- Parul Mishra, Vice President, Product Management, IBM Watson Orchestrate
- David Odenath, Global Head of Quantitative Investment Strategies & Solutions, HSBC
Bloomberg participants:
- Ritika Gupta, On-Air Reporter & Producer, Bloomberg
- Janet Wu, Anchor & Reporter, Bloomberg
Event Highlights:
Panel Discussion: Automation that Packs a Punch
Yin Aphinyanaphongs, Director of Operational Data Science & Machine Learning, NYU Langone Health said measurement is incredibly hard in medicine “There’s an enormous amount of effort spent in proving that [new drugs] actually work. The data analytics are less about predictions as they are about reducing variability and the people who are making assessments on patients.” NYU’s COVID-19 adverse-event model, which predicts deterioration of a patient’s condition, was initiated by nurses. “They were looking at patients, and they had no idea how to communicate with the next person coming on staff about how sick a patient was. The success of the project was about creating a unified language that reduces variability in how people communicate with each other,” Yin said. There are pilot projects in place at NYU for an in-basket messaging solution, in which a machine reads data points to determine the prioritization level of patient needs and to alert a medical team if a human response is needed.
Transforming a 178-year-old company while servicing more than 2.2 million elevators annually requires thinking digital first, connected systems and AI predictive analytics toward reducing equipment downtime, said Neil Green, Executive Vice President & Chief Digital Officer, Otis. Increasing efficiency and safety includes deploying mobile phones to Otis’s 34,000 mechanics around the world with an app that records elevator sound, vibration and acceleration. An AI algorithm uses that data to make recommendations about issues to look for. It can also provide a solution for incorporating legacy. “Mechanics that have been doing the job for 30 years can actually listen and be able to predict what they’ve got to fix,” Green said. “Others are not so keen just yet, so this app allows mechanics to address issues with a lot more precision.”
“I like to think of ourselves as a tech-enabled data company,” said Suvankar Mazumder, Chief Technology Officer, S&P Global Ratings.”Data is always going to be the fundamental ingredient that drives everything, but really, it’s the technology that’s going to make it far more reachable and useful for our leaders.” Mazumber says S&P constantly experiments with new technologies to improve processes. “We have our own data science division, which purely looks at all different kinds of ways that we can automate and different levels of automation.” He cautioned that automation can very quickly take companies and people in the wrong direction, making it imperative to determine what data flow should be automated.
IBM Sponsor Spotlight: Think Like a Chief Automation Officer to Drive Innovation and Productivity with Augmented Intelligence
On using intelligent automation to optimize how people work, Art Amador, Co-Founder & Chief Operating Officer, EquBot, said his company has $3.5 billion in products tied into investment analytics all over the world. Equboy has seen proof that the investment is boosting employee productivity, creativity and innovation. “We have over 100,000 models running right now, and without all the automation tools we’re using, we wouldn’t be able to manage all those.” He noted the great tools available that are related to data bias and model drift, “But at the end of the day, no matter how much you automate, we still need people,” including to train the machines.
Parul Mishra, Vice President, Product Management, IBM Watson Orchestrate said AI is a truly exciting solution for every business. “More than that is the power of AI in automation. At IBM, we have done a lot in the AI space. We have had some missteps in figuring out the right channel.” IBM is conscious of the need for AI implementation to be unbiased. “We’re very, very careful. With great power comes great responsibility. We’re very conscious of how AI is applied.” She equated the automation journey with that of elevators, where, long after they were automatic,passengers were more comfortable having an attendant press the buttons.
Skepticism is among the major challenges driving automation, said David Odenath, Global Head of Quantitative Investment Strategies & Solutions, HSBC. He credited IBM for its guidance in overcoming those concerns as the company increased its automated systems. “Developing the product was relatively easy, but not convincing folks to take that leap into the AI space beyond what was more traditionally quantitative.” It took a lot of training and webinars, which is where EquBot also came in. “Since then, there’s been a lot of success and a lot of people following in our wake,” Odenath said.
Case Study: Building Integrated Tech for a Wealth of Clients
“Nobody wakes up in the morning to go banking,” said Hari Gopalkrishnan, Head of Retail, Preferred, Small Business & Wealth Management Technology, Bank of America. The bank is working to create experiences that make the customer feel like banking is integrated into their everyday lives, that make them say, “Wow!” Customer experience starts with insight and analytics, and with responsible use of data, Gopalkrishnan said. “We then create intelligent experiences based on who you are and provide insights on your financials.”
Built five years ago, BofA’s Erica chatbot has tallied a billion transactions to date. The bank’s ambitions with customers extend beyond the transactional, including asking them about their life plans. “We’ve had millions of customers do that, and we can now use that information to generate the next set of personalized experiences. We’re just scratching the surface of personalization.”
Gopalkrishnan described the effect technology can have on customer demographics. He used a story about his mother to illustrate how the pandemic prompted baby boomers and seniors–”customers we thought technology had left behind”– to adopt banking innovations. “What we found is that AI really has democratized access to knowledge, to education, to capabilities.”
Panel Discussion: Automation and ROI
If you receive a paycheck in any of 140 countries, odds are it was generated by ADP, said its Chief Data Officer, Jack Berkowitz. ADP handles just under 20 percent of the U.S. payroll and moves about $3 trillion annually. That’s the combined GDP of France and Italy, he said. On managing massive datasets with automation, “We have to look at and understand all the millions of people, and then collapse it down into something simple that people can consume, offering compensation benchmarks as a public service. For example, we’ll take data on 25 million people every month and crunch them down into 6,500 job titles, so we can see what jobs are growing and how much people should make,” Berkowitz said.
Charles Gagnon, Chief Information Officer, New York Genome Center said that everything that happens after data sequencing in the laboratory is automated, saving hours on processing. Now, robotics is being introduced into labs. “All of these tools allow us to do research that we would not be able to do because of our size. It provides returns very quickly for small teams.” His company’s biggest challenges are the speed at which things change and the lack of resources, at times, as a nonprofit. They use the public cloud for access to management resources and to invest funding in the right areas of focus.
Buying insurance without lengthy interviews and lab testing is the future that’s already begun, said Alex Cook, Senior Vice President & Head of Strategic Capabilities, New York Life. That includes using digital fact-finding, aggregation of information, and predictive modeling to find holistic solution sets for people, as opposed to taking blood samples and handling physician reports that can be thousands of pages long. Digitizing the process shrinks it from about a month to as little as the same day. To integrate legacy data, including handwritten reports, New York Life is using AI to scrape both structured and unstructured data for use by underwriters and models. “That’s a critical part of how you automate, and it’s taking advantage of some of the current generation of AI to enable that to happen.”
This Bloomberg briefing was Proudly Sponsored By
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