4 AI & ChatGPT Use Cases in Customer Experience Management

As generative AI tools and large language models like ChatGPT gain traction across industries, financial institutions are continuing to analyze how AI can be applied to operations as well as customer experience (CX) management.

From automating repetitive tasks to helping support teams provide personalized, real-time support, ChatGPT has begun transforming how banks and credit unions deliver customer journeys.

In this article, we explore practical use cases of ChatGPT and similar AI models in customer experience management, especially within regulated and complex environments like banking, where relationships, transparency, and compliance matter.

Why AI Models Like ChatGPT Are Changing Customer Experience Management

ChatGPT is the leading generative AI model with 60% of the chatbot market and 80% of platform visits (at the time of this writing). Google Gemini is second in market share, with just 13.5%—a sliver of ChatGPT’s dominance.

Other models have small footholds in the market, typically in specific use cases, like capital market analysis or quantum computing. For example, Bloomberg uses its proprietary BloombergGPT in its terminal operations. FinBERT is leveraged by companies trying to uncover sentiment from financial news to predict what’s next. Anthropic’s Claude Sonnet is used by companies like Nasdaq, Voya, and PwC for its superior financial reasoning. (Note: Even though it is a top scorer, Claude entered the market later so it does not have the consumer adoption of OpenAI.)

Source: PYMNTS.com

Did you know? By 2026, more than 80% of independent software vendors (ISVs) will have embedded generative AI capabilities in their enterprise applications, up from less than 1% today.

The surprise emergence of DeepSeek serves as an example of the rapid evolution of AI, underscoring the need for companies to remain agile. We believe in a multi-model approach for that reason, which is how we’ve structured our own CXM software. We typically suggest enterprises undergird their AI strategy with infrastructure that is model agnostic so they can pivot to the right models for the right use cases to optimize CX.

Why AI/ChatGPT Matters for Customer Experience Management

The dominant LLM, ChatGPT, excels at understanding and generating human-like responses. In customer experience management, that translates into faster replies, smarter support, more accessible knowledge, and greater personalization—all while reducing the burden on internal teams.

Banks and credit unions are turning to ChatGPT and other generative AI tools to:

Improve responsiveness across support channels
Enhance the quality of responses to customer interactions
Power self-service AI voice and chatbots that understand intent and sentiment
Uncover insights from cases, customers, and journeys
Analyze customer sentiment trends and tailor future interactions accordingly

Banking Use Cases for ChatGPT's AI

By embedding ChatGPT into workflows and platforms, institutions can modernize CX delivery without adding headcount or introducing friction. We recommend four primary use cases for applying AI to CX management.

1. Conversational AI For Banking CX

Chatbots are the most obvious use case for AI in customer experience. In OvationCXM’s latest research, we discovered that 60% of business banking customers are comfortable using an AI voice or chatbot for support from their primary financial provider.

Legacy bots have typically followed rigid decision trees, offering limited, scripted responses. Worse, they didn’t recognize emotion or intent. If a customer expressed frustration, the bot often seemed clueless and responded with a cheerful “Anything else I can help you with?”

ChatGPT and other LLMs have changed that almost overnight. It uses natural language understanding to interpret a wide variety of prompts and responds with nuance and empathy. When integrated properly, ChatGPT is used in CX to power intelligent, brand-aligned bots.

Use Cases

Sentiment analysis: Understand customer feelings and adjust tone
Contextual Understanding: Recognize the full history and back story of a customer query
Faster and more accurate support interactions

“Legacy bots didn’t always understand what you meant. Now, with large language models, they do. That’s a big shift.” —Alan Finlay

Organizations can get themselves into trouble, however, if they rely on AI agents that are siloed themselves and don’t have access to data to provide a full picture of a customer relationship. These bots become one more disjointed technology, adding to operational chaos.

A foundational data orchestration layer becomes essential to ensuring AI models are fueled with robust centralized customer data to provide appropriate support and recommendations.

Banking CX Impact

Faster resolution for common questions
Reduced pressure on contact center teams
Better first impressions and smoother journeys

2. Using AI/ChatGPT For Agent Assist and Knowledge Delivery

The need for human agents remains, but AI now comes alongside them and speeds up many tasks so they can do their jobs more effectively with less effort. ChatGPT and other LLMs are now:

Summarizing full customer histories across chats, emails, and calls
Delivering relevant answers instantly from knowledge bases
Recommending personalized, brand-aligned responses in real time

The time savings are incredible. By eliminating time-consuming scrolling through dozens of tickets or tabs or toggling into different platforms to retrieve updates, teams can save time, resolve issues faster, and do so with greater confidence in the responses, even if the product is unfamiliar. Using a single, summarized view, support teams are simply more efficient.

“You just saved 10 to 20 minutes, maybe an hour on that one single customer interaction.” —Alan Finlay

Use Case Examples

Case summaries that provide status and context
Journey summaries that flag friction points
Suggested replies reviewed and approved by agents

Did you know? McKinsey & Company estimates GenAI may automate up to 30% of working hours in the U.S. by 2030

Banking CX Impact

Reduces wait times and backlogs
Elevates agent confidence and consistency
Maintains brand voice while scaling quality

3. Unlocking AI for Self-Service

Customers don’t always want to talk to someone—they want to solve their issues themselves. In our Banking CX Report, 38% of businesses said they would give more business to their banking provider if it expanded its self-service options. ChatGPT-powered search and knowledge delivery make this possible.

Banking CX Impact

Customers can get accurate answers using natural language
AI can point customers to relevant support articles, forms, or next steps so they can help themselves
Teams can analyze frequent questions to hone in on where to augment help resources

When integrated into your journey orchestration layer, self-service becomes more than a customer portal—it becomes an integral part of the journey.

Use Case Examples

In-platform assistants that pull up policies, steps, or docs
Intelligent escalation when self-service fails
Customer signals triggering proactive support follow-ups

4. Smart AI-Powered Journey Orchestration

Unstructured feedback like chat logs, call transcripts and notes, and survey responses contain valuable CX signals. ChatGPT can identify, categorize, summarize, and interpret this data to:

Detect churn risk or dissatisfaction
Recommend next-best actions
Feed journey orchestration platforms with real-time intelligence

It’s not just about playing defense. AI can design proactive strategies by surfacing issues before customers complain—or worse, leave.

With tools like OvationCXM’s no-code journey builder and orchestration alerts, business teams can: spot when steps stall and take corrective action, monitor CX trends by product, segment, or partner, and automatically escalate high-touch journey issues to human agents. When business lines have tools to design journeys directly, using AI recommendations, they can act before they lose customers.

AI insights during monitoring also make it easy to modify journey orchestration immediately as needed to improve the experience and outcomes.

Proactive customer experience management keeps journeys on track and ensures they are moving forward to their goals.

Use Case Examples:

Orchestration alerts that highlight friction or underperforming journeys or steps, sent to relevant teams
Automatic recommendations on the optimal next steps in a journey
Sentiment analysis and monitoring that can kick out journeys to human agents if there’s a risk

Banking CX Impact:

Increase completion rates
Reduce escalations
Drive loyalty by solving problems early

Did you know?

By 2027, it’s expected that more than half of enterprise AI models will be industry- or function-specific, according to Gartner.

This proactive customer experience management keeps customers on track, addressing issues as they happen vs. waiting for them to raise their hand for help or worse, leaving the bank.

Roadmap: How to Use AI models like ChatGPT for CX Orchestration

When leveraging LLMs for CX delivery, the process loosely follows this roadmap:

Map current state customer journeys to identify all of the stakeholders, systems, and partners involved.
Uncover friction points and inefficiencies caused by duplicated efforts or data stores, cumbersome steps or missing steps, communication gaps, and wasted efforts that could be done differently.
Map the ideal journey state. What would make it easier and faster for the customer to reach their goal and for teams and vendors to support that progress?
Detail gaps in technology and tools, including disconnected communication and data stores.
Use ChatGPT recommendations with a no-code journey builder to close CX gaps.
Iterate journeys on the fly using real-time CX data analyzed by AI.

In customer experience management, ChatGPT is both a support layer and an insight generator. It reduces manual workload, speeds up responses, and helps teams orchestrate and then coordinate the flow of journey steps from onboarding through any type of support customers need to use the product.

“We only use AI when it’s the best way to solve a specific customer problem.” —Alan Finlay, Head of Product, OvationCXM

Conclusion: Guarantee Smarter Customer Experiences with AI

Designing modern customer experiences in banking demands more than empathy and process. It requires tools that adapt in real time, scale across teams, and work in harmony with humans. Generative AI makes this possible.

At OvationCXM, we help banks orchestrate complex journeys using the best AI available—and the best design principles behind it. From proactive alerts to AI-assisted recommendations and messaging, the result is faster resolutions, higher satisfaction, and better business outcomes.