What are some questions you’d like to ask your customers? And if you could interview them, how much time do you think it would take? How much money would you spend doing it?

Marketers spend hundreds of thousands of dollars every year on research and messaging to boost customer experience and loyalty, as well as bring in new customers and drive revenue. But those customer voices already exist online, giving you valuable feedback every day about your business. Your customers are talking, are you listening?

Before modern AI was widely available, finding insights in unprompted feedback was difficult because such data is unstructured, i.e., it’s what people say about your brand in reviews, on social media, and in videos in their own words, about any topic. With advances in AI, large language models can analyze and summarize unstructured data to provide the insights you need, on feedback that’s already available, without the need for additional outreach.

What makes today’s AI so good at processing your customers’ feedback? Large language models are trained on enormous amounts of data, including billions of web pages, books, scholarly papers, and more. Much of this data is from conversations we have online, the words we use in social posts, reviews, and forums. With such deep knowledge of how we talk to one another, today’s AI has become exceptionally good at not only sounding like a real person, but also analyzing and understanding customer feedback like a real person—while operating with the speed and capacity of a modern machine.

For example, a barbecue restaurant started seeing negative reviews about food quality. After asking AI what customers thought about their food, they learned their brisket was dry– coinciding with a change in oven temperature. With this info, the corporate team was able to quickly send out new instructions for the brisket cooking temperature, correcting the issue in a matter of days, not weeks, and preventing hundreds of customers from having dry brisket. There’s a lot more to this than good brisket of course—customers felt heard, which encouraged their loyalty; and less negative reviews coming in meant new customers were no longer being discouraged from trying the restaurant. Considering 94 percent of U.S. diners base their decisions off online reviews, that’s pretty important.

AI also provides a huge opportunity to avoid major company crises. The Chipotle E. coli outbreak of 2015 is a good example. Diners had already been reporting food poisoning online well before the outbreak spread to eleven states. Had the company been using AI for deep listening, they might have learned of the problem much faster, stopped the spread, avoided the crisis, and kept their reputation intact. Instead, this crisis follows them around—people still remember.

With AI-powered technologies, it’s now possible to get insights in real time and not days or months after the fact. Companies no longer have the luxury of claiming ignorance. They can’t say “we didn’t know” or “we had no way of knowing” when AI-powered services are ready to alert them to safety issues, harassment, discrimination, and more when they happen. Key decision makers can be notified immediately and take action right away to prevent a crisis. But there is more to maintaining a good brand reputation than gaining awareness of negative sentiments, positive information is important, too.

At a popular quick-service chain, management decided to introduce a new customer service training program for team members, encouraging them to be more proactive in helping customers select their meal. By asking AI which location had the best customer service, they were able to identify a winning location as well as a worker to reward. They were also able to learn more from the worker about how they went above and beyond, and used the information to uplevel their customer service training across the company at other locations. AI gave them the opportunity to celebrate and empower their staff—boosting employee loyalty as an added benefit—improve training across the company, and ensure customer service consistency.

By implementing AI-powered technologies and services into your company’s business and reputation management strategy, you can do everything from proactively managing ever-changing customer demands to maintaining brand consistency to testing new products and practices to leveraging good and bad feedback. And all of this is in service of improving customer experience, customer loyalty, and new customer acquisition—and maybe even gaining some special benefits of staff appreciation and loyalty to boot. AI might give quick service a whole new meaning: Quality Standards Raised.

Dan Cunningham is the Chief Technology Officer of Chatmeter. He is is a passionate R&D leader focused on building high-caliber software engineering organizations with a culture of innovation, curiosity and a love of building great software and technology. As the Chief Technology Officer at Chatmeter, Dan is responsible for driving an AI-first product strategy, leveraging his extensive experience in modern agile methodologies, DevOps practices, and innovative software development.