Agentic AI in CX: 5 Industry Use Cases

Having explored the fundamentals of agentic AI and the best ways to implement the technology into the customer experience, in the last of our series of blogs on agentic AI, we’re going to delve into real-life industry use cases for the use of agentic AI in CX.

We’ve seen how agentic AI can analyse scenarios, make decisions, and act autonomously to solve complex problems with minimal human intervention, driving transformation in three key areas:

Customer-facing outcomes – Offering fast, frictionless and personalised experiences to customers.

Employee-facing outcomes – Providing tools that help employees to work smarter, automating repetitive tasks and providing insights.

Operational outcomes – Working behind the scenes to optimise business processes and aid decision making.

Agentic AI’s ability to influence outcomes across an organisation underscores its transformative potential. As the technology continues to evolve, its integration will become a key driver of efficiency, customer satisfaction and strategic growth in every sector. Here we look at how agentic AI is solving problems in five key sectors.

Pain Points: In retail, businesses in competitive niches often work with tight profit margins and high-volume sales. This, in turn, means large numbers of customer interactions. Even the most basic queries across a growing number of channels can put strain on contact centres and other resources.

Solutions: Many retailers are already using AI to handle basic queries, but agentic AI can do much more. Because agentic AI can understand the context of interactions, it can personalise touchpoints across the customer journey, proactively engaging customers, making recommendations and driving revenue.

Dynamic pricing and stock control is one area in which agentic AI is making waves. Walmart uses it to monitor data points, such as competitor prices, demand and inventory levels, adjusting pricing and redistributing stock more effectively.

Pain Points: The hospitality sector has become incredibly complex, with multiple interconnected systems for hotel bookings, flights and entertainment options. For many, vacations are a once-a-year experience and viewed as a luxury. This, along with rising prices, particularly in the hotel sector, means customers have higher expectations.

Solutions: Agentic AI bots are able of creating highly personalised itineraries, as well as carrying out complex booking requests such as reservation changes. Based on the conversation with the customer, it can access relevant systems, deal with requests and facilitate booking amendments in real-time.

Multi-language support can be critical in international travel and a single agentic AI bot can switch language with ease, without having to transfer a customer to a human agent with the required language skills.

Pain Points: Consumer services operations, such as help lines, often deal with complex issues and can be frustrating for those contacting them. Customers are often transferred across multiple departments, or may simply be unable to get a response out-of-hours across multiple channels.

Solutions: While traditional bots could only handle basic tasks, agentic AI bots operate 24/7 with a higher resolution rate without having to pass queries onto human agents. They can even use image recognition to identify products and troubleshoot issues.

One particularly useful application is autonomous service resolution. While a traditional chatbot might direct a customer to a returns page, agentic AI can handle the entire multi-step workflow, verifying the order, checking company policy, offering a refund or replacement and even generating a return label.

Pain Points: A recent study found that administrative overload in healthcare is a huge problem: many health workers report working additional hours to complete administrative tasks. All this at a time when users are looking for more personalised care – and staff shortages are a real problem.

Solutions: When it comes to administration and associated paperwork, agentic AI can be handling much of this, freeing up health workers time to spend more time with patients. Tasks such as appointment scheduling, patient intake, requesting or sending documentation and other complex workflows can all be handled by agentic AI.

Because agentic AI can process huge amounts of patient data, it can also create highly tailored treatment plans, albeit with human oversight.

Pain Points: Financial services is an extremely competitive industry. If customers aren’t happy, they’ll go elsewhere. It’s vital that companies provide exceptional customer service. The sector is highly regulated and data-intensive, all things agentic AI is particularly well-suited to – especially in high-volume, high-risk areas.

Solutions: It should come as no surprise that it’s in financial services where AI is making the biggest impact. 60% of respondents in one study said that customer experience and engagement was the top use for generative AI and agentic AI is set to carry on that legacy. Not only is agentic AI automating more complex repetitive tasks, but it is also helping with dispute resolution, know-your-customer (KYC) compliance, reducing operational cost and minimising human error. Mastercard’s AI-powered fraud prevention system processes 125 billion transactions every year.

Beyond compliance, it can act as a proactive wealth management co-pilot, monitoring balances in real-time, sweeping idle cash into higher yielding accounts, or rebalancing portfolios in response to market signals.