AiThority Interview with Jing Huang, Senior Director of Engineering and Machine Learning at Momentive

Please tell us a little bit about your role at Momentive. How did you arrive at the company?

I currently serve as Momentive’s Senior Director of Engineering and Machine Learning. As the leader of the machine learning engineering team, I am envisioning empowering every product and business function with machine learning. Before Momentive, I worked at Cisco Systems for six years and was also an entrepreneur devoted to building mobile-first solutions and data products for non-tech industries.

How do Momentive’s AI-powered solutions help drive positive customer experiences?

Momentive’s AI-powered technologies help drive positive customer experiences by getting to the heart of what matters to customers most, then making that information available and actionable for use across all of their decision-making processes.

The AI capabilities Momentive has brought to market, such as Automated Insights and SurveyMonkey Genius, enable survey creators to execute more impactful and meaningful surveys. They also help customers collect cost-efficient responses from targeted audiences to uncover relevant insights more quickly than ever before. That speed-to-delivery is imperative, and our unique ability to balance technology with human experiences drives greater value for our customers.

Having data is one thing; having a team of people to translate accurate and inclusive AI-driven insights is another. We effectively combine the two to help our customers make decisions based on data that elevates reality and eliminates biases.

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What are some of the ways AI and ML are being used in the customer experience?

Intelligent technologies fuel better, more confident decision-making for customers when they are built upon sound data that merges humanity and technology to deliver more personalized results. For example, the human loop methodology Momentive introduced for our latest open theming text analysis solutions delivers highly tailored insights unique to each customer and their specific use cases. This type of hyper-personalization transforms the customer experience from one of rote sameness into one where people feel seen and heard, building loyalty and trust.

Machine learning plays a crucial role in helping us harness insights from millions of respondents, identifying what to focus on to deliver a better overall customer experience. Machine learning is also being used to ensure we amplify real customer voices by detecting and filtering out responses that are low quality. With the help of ML, real, relevant, customer voices will be heard, and the service providers can focus on delivering a better overall customer experience with the right information.

AI and ML can also be used to build feedback options directly into a website or chatbot, or by generating automatic emails after specific customer touchpoints, to help customers avoid getting stuck in frustrating loops with bots that lack the context needed to solve their problem. With concepts such as AI-copilot and AI assistant gaining popularity, it will be no surprise to see AI buddy appear in our daily workflows more frequently.

Momentive recently conducted a consumer survey to gauge how much of a role AI plays in customer experience. Can you share some of the key takeaways?

We asked 2,201 consumers about their experiences with AI in CX, focusing on likes, dislikes, and future interests. Key findings show:

• 90% of people across ages, genders, and income levels prefer to work with a human over a chatbot. Respondents said that humans: understand their needs better (61%), provide more thorough explanations (53%), are less likely to frustrate them (52%), and give them more options to address their problems (50%).
• Chatbots are the most visible aspect of AI in customer experience, but customers are interested in applications, too. 52% of consumers are interested in AI that helps them through a product, website, feature, or experience, with 47% also interested in personalized deals and 42% in product recommendations.
• The vast majority of people (89%) believe AI will have an impact on their lives in the next five years, but the technology is still largely misunderstood. Only 18% of people are “very confident” they know when they are interacting with a chatbot and only 14% feel confident about spotting AI-generated content. Less than half felt certain they could identify AI-generated content at all.

The high-level takeaway is that AI can’t replace human interactions, but when used correctly,  and tailored to specific customer objectives and outcomes, AI can shed a great deal of light on the customer experience. The key is to be thoughtful about your approach.

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What are some of the challenges for companies looking to implement AI to improve customer experiences?

Some of the biggest challenges companies face are acceptance and openness to change from inside the company and from its customers. AI technology is here to stay, and it’s always evolving. But it will take time for us, as humans, to adapt to it. Inside the company, implementing AI into the customer experience flow requires changing and breaking some of the existing approaches. It will require extensive training for employees and education at every level of the organization. Successfully implementing AI in the CX workflow is also dependent on customer perception and readiness to use the technology.

Taking a more open approach to customer feedback while testing new AI can have a profound impact when done correctly. Companies that successfully engage and seek customer input every step of the way are able to adapt and scale priorities in real-time. For example, engaging with customers via a brief survey after customer service interactions can reveal critical insights. We help customers build this type of feedback into the customer experience and use it to inform investment and other business decisions.

Beyond CX, how do you see AI/ML assisting in capturing authentic sentiments that can inform business decisions?

CX is just one classic use case that demonstrates the benefits of AI/ML. Other use cases include market research, brand tracking, and concept testing. With the help of AI/ML, we can surface valuable information by capturing and understanding sentiments. AI/ML also helps us identify the right focal points so we don’t get lost in the vast ocean of digital data.

Our AI and ML function as processes that build as they learn to continuously improve via a feedback loop over time and use. They don’t remain static. That’s intentional because neither do human sentiments. They are powerful information sets to inform better business decisions.

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How do you envision ethics being applied to such AI use cases?

AI ethics is a complicated and rapidly evolving field. We understand and respect the complexity of this space. For the customer experience use cases we have discussed here, we are focusing on customer feedback data.

Implementing an ethical and inclusive approach to the data collection process helps ensure it is representative of the population and reduces opportunities for bias and discrimination to pollute results. Ethics is top of mind for our team, and it’s not a one-step practice; it must happen at every stage of the data collection process.

Today, many applied ML/AI features/products rely on open-sourced or paid large language models as the foundation to understand text/image/video, etc. It is important to understand and audit the algorithms before adopting them. It’s also a good opportunity to fine-tune the algorithms with diverse data for specific use cases.

Again, AI ethics is an important topic today. It is vital that we build an effective feedback loop to collect data along the way and keep learning and improving.

Thank you, Jing! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]