Q&A With Sony Sung-Chu, SVP and Head of Science & Innovation, Businessolver

This interview is part of a series on Workology that features an HR Technology company, its innovators and its features. For this post, we’re talking to Sony Sung-Chu, SVP and Head of Science & Innovation with Businessolver.

Named one of the top 20 technology innovators of 2018 by Employee Benefit News, Sony Sung-Chu leads a team of engineers who drive innovation in artificial intelligence and machine learning for the Benefitsolver platform. After working at Businessolver for nearly three years, Sony moved to Amazon to build and develop a new business intelligence team. He returned to Businessolver in 2016, where he led the development of its AI-powered personal benefits assistant Sofia, launched in 2017. Sony and his team continue to train Sofia, speeding her learning processes, enhancing her sentiment analysis capability, and adding her member service functionality to align with Businessolver’s mobile-first strategy and empathetic “Technology with Heart” design. Sony graduated from Iowa State University with a Master’s Degree in Industrial and Manufacturing Systems with a concentration on Computational Astrophysics.

Sony Sung-Chu, SVP and Head of Science & Innovation, Businessolver

 

Q: Can you introduce us to Sofia and your journey with her? 

A: Of course. First, thank you for identifying Sofia as “her.” From our first concepts, through development and deployment, to a fully realized and consistently learning AI today, we always knew we wanted to personalize Sofia and infuse her with as much humanity as possible, as a way of staying true to Businessolver’s “Technology with Heart” motto. So, it just made sense to refer to her as her.

And then, Sofia’s development is especially personal for me, because her journey with Businessolver is so closely tied to my own since returning to the organization in 2016. Our team spent most of that year developing her after she was “born” as an entry in Businessolver’s annual Hackathon, an event where Solvers form teams to propose new product innovations. I can’t remember if the Sofia team even won the Hackathon that year, but I know we’re all grateful for their ingenuity.

After a year of development work, we introduced Sofia in October 2017 as the industry-first personal benefits assistant, essentially offering supplemental chat support for users on the Benefitsolver® platform. Most of our clients hold their Annual Enrollment period somewhere between mid-October and mid-November. Knowing that’s the time of year when employees have the most questions about their benefits, we wanted Sofia ready to provide additional support to our clients and their members at that critical time, in a way that’s fast, easy, and accessible.

For example, an employee could ask, “Does my spouse have medical coverage?” or “Do I need to verify coverage for my newborn daughter?” and Sofia could type back an accurate, automated response in English, Spanish, or French. She also could document transcripts of conversations and save them to an employee’s record in Benefitsolver, so that employers can track issue resolution and collect data on the most common areas where employees require support. That first year, Sofia handled 20% of our total chat volume during annual enrollment, and 25% of that (about 14,000 chats) came outside of business hours. She saved 290,000 minutes of wait time and even told 150 jokes. 

Over the four years since, as she’s engaged with people and data, Sofia uses her machine learning to look for learned patterns and predict future developments, making her smarter and able to provide more complex answers over time. She’s now interacted with millions of Benefitsolver users and answered almost 1 million benefits-related questions on 300+ topics. In the half of 2021 alone, she’s had more than half a million conversations in interactions with more than 175,000 users. She now speaks 27 languages—including Hindi and Arabic—and assists users with questions on all things benefits, from savings account balances and verifying dependents to finding information related to COVID-19.

Sofia means “wisdom” in Greek, and she definitely lives up to her name, consistently offering accurate and helpful benefits support, 24/7/365, since Oct. 4, 2017. 

Q: How can Sofia’s technology support our larger goals in benefits administration?

A: In the market, there is certainly more awareness on the differences between a chatbot and a personal benefits assistant. Whenever we describe what Sofia can do to help our clients and their employees, it becomes clear that Sofia can do way more than just answer questions like a chatbot can. She can help employees do things like update benefits information, recommend plans to enroll in, and check up on the status of transactions for you. That’s way more than a simple chatbot.

In terms of increasing Sofia’s knowledge base, at first, we started with COBRA and dependent verification. Now we are very close to reaching feature parity with Benefitsolver. In fact, customers don’t even need to click around the website to navigate Benefitsolver anymore; they can simply ask Sofia and she will help them get to the right place. For example, if a user wants to make a payment, instead of going to a website and navigating to a payment page, they can say, “Sofia, make a payment.” And boom, payment done. Based on my information, she should know what the context is, whether it’s a COBRA payment or another type of payment. Then from there, she can help me make the payment without me ever having to click around Benefitsolver.

She can even help them complete a task like uploading a document or verifying a dependent. Also, one of Sofia’s most popular skills is her ability to educate. People ask Sofia about benefits terminology like, “What is a deductible?” More importantly, during the pandemic, Sofia helped inform millions of users with information and news about COVID-19 workplace policy and vaccines.

Q: Why is empathy so important when it comes to AI?

A: While AI performs exceedingly well at improving efficiency, without empathy it can come across as cold or even unhelpful. However, when developed with empathy, AI helps people feel productive and supported, even pampered. Many organizations, including Businessolver, use AI this way to deliver quality customer service.

For example, health emergencies happen on their own timeline and it’s never convenient. But Sofia is online 24/7 to help. No, she isn’t trained to set a broken finger, but she can help members quickly access ID cards, find the nearest off-hours urgent care center, or answer pressing benefits questions when they’re tired, stressed, and hurting.

Secondly, more than 60 million Americans speak a language other than English at home. Asking people to navigate benefits information in a non-native language is a sure-fire way to create costly care gaps. Sofia now can respond in 27 languages, meaning more people can get personalized benefits guidance in their native tongue.

Third, even the most seasoned, professional member service rep can come to the end of their rope. When assisting a frustrated customer, sometimes emotion can build on both sides, which can damage both the organization and the person in need. However, Sofia is empathetically designed and trained to recognize sentiment so she’s able to understand not only what is being said, but how.

And lastly, human brains are hardwired for bias and that hardwiring can overpower heartfelt intentions, leading to unfair assumptions that can get in the way of work. When starting with a diverse, empathetic, and expert team, AI like Sofia can be developed and trained to treat everyone equally. At Businessolver, we continuously invest R&D time to improve our processes to identify and deal with potential biases in Sofia. In general, we look at four types of biases:

1) Reporting bias, which happens when we don’t consider every cohort based on use cases.

2) Selection bias, which occurs when our data underrepresents certain groups.

3) Extrapolation bias, which happens when we over generalize and assume that what’s true for one group applies to others.

4) Implicit bias, which occurs when we train AI based on our own assumptions. Like assuming everyone likes science jokes.

It takes continuous analysis of our user base to be able to safeguard against these types of biases; but this process is part of our dedication to our clients and users. We want to first seek to understand who uses Sofia. Then consider how they want to use Sofia, not how we want them to use Sofia.

This process ensures that the bidirectional communication between our users and Sofia is considerate and aware of the diversity, equity, and inclusion our clients and users expect to experience. This combines the efficiency of artificial intelligence with the empathy of humanity to create an exceptional experience.

Q: Can you tell us a bit about what the adoption of AI technology looks like for organizations?

A: Sure. We believe that customer service and virtual assistants are on course to converge. The greater the convergence, the more helpful Sofia is. Essentially, we can imagine a future where virtual assistants will eventually be the primary UI in many applications, because an application like Sofia should be able to simplify and enhance the way we interact with systems.

This is particularly true of systems where the processes that have the end user as an actor are complex, as is the case in benefits administration and health care.

Natural language processing (NLP), computer vision, and information search and retrieval give us a chance to do something like the example above about making a payment. Ideally, we will have the ability to communicate with systems using our natural language and behind the scenes, Sofia has access to a set of tools that help her serve up the right content or provide the right service.

For example, these tools could be using bidirectional encoder representations from transformers (or BERT) as a way to find relevant information in documents or computer vision to classify documents. Or using optical character recognition (OCR) to read information and provide feedback or access an algorithm for making a recommendation.

In the short term, we want to introduce more personalized services (meaning we collect data about you—this is the explicit data) and more intelligent services. To do this, we can make inferences using our models for things like recommendations, prescriptive action, sentiment analysis, and so on using implicit data or a mix of both.

Q: Why is a personalized user experience so important?

A: We obsess over our users’ needs. That inherently motivates us to think about how they want to interact with their benefits. Benefits are very personal, but they are also extremely complex. We want to create a personalized benefits experience that is both streamlined and easy to use so that our users can get the help they need and elect the right benefits to keep themselves and their families healthy and happy.

We are constantly looking to refine that personal interaction. The way we teach Sofia new skills comes from our desire to invent and simplify each user’s unique journey as they learn about their benefits, elect them, use them, and make decisions across that spectrum.

This above anything else guides Sofia’s design, research roadmap, and functionality. Whenever we release new features, we are already critically thinking about the next milestone and how we will push the envelope again in our next iteration to help our customers reach new heights.

Learn more about Businessolver and Sofia here.

Posted in

Jessica Miller-Merrell

Jessica Miller-Merrell, SPHR, SHRM-SCP (@jmillermerrell) is a workplace change agent, author and consultant focused on human resources and talent acquisition living in Austin, TX. Recognized by Forbes as a top 50 social media influencer and is a global speaker. She’s the founder of Workology, a workplace HR resource and host of the Workology Podcast.

Reader Interactions

Comments

ON AIR WITH WORKOLOGY

Pin It on Pinterest