Artificial intelligence provides a way to boost customer experience (CX) by allowing contact center agents to understand customer sentiment.
Early AI-powered chatbots that used natural language processing (NLP) were primitive, but generative AI now allows automated chatbots to be “leaps and bounds better than a couple of years back,” says Ritu Jyoti, group vice president and general manager for the worldwide AI, automation, data and analytics research practice at IDC.
That’s because they can understand the context of customers’ problems from their histories, she says.
“Customer experience is not just from customer support,” Jyoti says. “It’s through the entire lifecycle of a customer journey.”
Here are three industries in which AI can improve CX.
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1. Finance Uses AI to Detect Fraud and Make Service More Personal
AI-powered fraud detection systems can identify suspicious activity swiftly and enable a secure and smooth banking experience. Machine learning (ML) algorithms allow financial institutions to spot suspicious activity in customers’ spending behavior, such as suddenly opening an account in a foreign country and beginning to transfer money, Jyoti explains. On a smaller scale, if a customer buys a Starbucks coffee and usually never goes to Starbucks, AI can pick up this anomalous behavior, she says.
Chatbots and virtual assistants provide 24/7 support in finance and help answer customer questions in real time. AI-driven analytics can personalize financial guidance and make AI responses more relevant to individual customer profiles.
NLP lets organizations understand customer queries and perform bank transfers via voice. NLP can also translate written language for chatbot users, Jyoti says.
AI mobile apps in banking are getting more conversational when interacting with customers. They can make small talk and even crack jokes, says Neil Sahota, AI adviser for the United Nations and co-founder of the UN’s AI for Good initiative.
In addition, technologies such as IBM’s Watson X enable banks to incorporate generative AI in their self-service banking apps to provide contextual answers using natural language understanding and large language models (LLMs).
2. Nonprofits Boost Donor Engagement
AI can help nonprofit businesses make up for a lack of staffing, according to Jyoti.
“Nonprofits are in real need because they don’t have an unending amount of budget to hire so many people,” she says.
In addition, nonprofits can use both ML and NLP to automatically create written messages to donors, which can be quickly adjusted based on their demographics and age, Jyoti says.
“Generative AI can be used to create automated text and an outreach letter, and then AI can also be used after all the responses they get to upload them into the database for easier segmentation and future reference,” she says.
In addition, predictive analytics lets IT leaders identify donors who are likely to increase contributions or turn into recurring donors. AI enables targeted campaigns that can nurture these relationships between nonprofits and donors.
AI agents know when to push and when to ease up on donors if they feel stressed or are “dragging their heels” when it comes to donating, Sahota says. He notes that AI tools can suggest which donors to exclude from upcoming campaigns based on the frequency of recent contacts.
“It’s a whole new type of 21st-century relationship building that’s emerging,” Sahota says. “This is the new customer experience and donor experience.”
3. Utilities Optimize Grid Management and Cut Service Disruptions
In energy and utilities, AI-powered predictive analytics is likely to be transformative. It empowers the industry to gauge when machine failures will occur, for example, allowing companies to monitor thresholds, log files and check alerts to ensure machines are in good condition and proactively fix them if not. They can perform predictive maintenance on machines and spot anomalies before a six-month or three-year machine cycle ends, Jyoti explains.
“It saves tons of time and effort and also avoids unnecessary downtime,” she says.
Predictive analytics provides info on deviations from normal thresholds based on historical usage, weather patterns and circumstances such as a spike or rapid decline in energy use, Sahota says. It can also be used to predict peaks and dips in resource usage so that facilities can be managed more effectively. An energy utility company can more efficiently manage the electric grid this way, lowering the risk of service disruptions.
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“Operationally, they’re trying to make it as efficient as possible, given that their infrastructure is so old,” Sahota says.
Oracle’s data science team for energy and water uses AI to predict energy burden and gas bills. In addition, Amazon Web Services’ generative AI tools allow energy companies to transform customer experience with conversational AI assistants and manage the smart grid to prevent failures. Based on predictive analytics data, energy and utility companies can schedule rolling blackouts.
By integrating these emerging technologies, the finance, nonprofit and energy sectors can offer improved, personalized and efficient experiences that meet the evolving needs of their customers.
