Fifth Third Bank, a leading regional financial institution with over 1,100 branches in 11 states, operates four main businesses: commercial banking, branch banking, consumer lending, and wealth and asset management. Founded in 1858 and headquartered in Cincinnati, the bank has assets in excess of $211 billion. During the first quarter of 2025, Fifth Third Bank saw loan growth, net interest margin expansion, and expense discipline, which led to positive operating leverage.

Fifth Third Bank has been at the forefront of technological innovation for decades. Press materials surrounding new systems note that the bank is proud to have launched the first online automated teller system and shared ATM network in the U.S. in 1977 by allowing customers 24/7 banking access.

Additionally, this drastically increased the number of ATMs customers could use since they were able to access ATMs owned by other banks. Fifth Third directly invests in AI to enhance its core banking operations and indirectly invests in AI to improve healthcare through Big Data Healthcare, a wholly owned indirect subsidiary.

Fifth Third’s annual filing in 2024 reflects an uptick in the bank’s technology and communications spending, showing a roughly 14% increase compared to 2022. Fifth Third takes a highly intentional and risk-minded approach to AI, having spent the better part of 2024 focused on establishing key governance foundations before rolling out AI capabilities. Additionally, they concentrate deployment on areas where AI brings genuine business value.

This article examines two AI use cases at Fifth Third Bank:

• Conversational AI for streamlined customer service delivery: Leveraging natural language processing (NLP) to reduce calls received by human agents by double-digit percentages and saving millions in costs.
• Surfacing customer satisfaction scores: Leveraging analytics to analyze customer interactions and improve agent performance.

Redefining Customer Service with Conversational AI

The onset of the pandemic in early 2020 brought the stress of heavy call volumes of contact centers to the forefront. One study emphasizes how a voice-first approach is needed to facilitate contract centers’ ability to handle a sudden surge in calls.

Like many companies, Fifth Third Bank saw customer inquiries skyrocket at the start of the pandemic. The bank was already piloting the concept of a chatbot in the immediate years leading up to the pandemic. Then, in early 2020, as the pandemic unfolded, Fifth Third quickly realized it had to help its call center, which was overwhelmed. 

Before Jeanie 2.0, customer service agents on the call center floor were required to scroll up to read the entire conversation history to understand customers’ needs and personalized requests better. Additionally, while Jeanie 1.0 could provide step-by-step instructions to complete specific tasks, it had the following issues:

• Inability to always provide the correct answers to customers’ questions
• Connected customers to agents when it wasn’t necessary
• Required agents to manually search for and use a variety of content to perform different tasks when helping customers, and find the specific script telling them what information they need to send to the customer
• Create their own scripts to answer questions of a more complex range

Screenshot of the Zelle Flow for Jeanie 1.0 showing the problematic early default to agent escalation (Source: UC Center for Business Analytics)

Jeanie’s natural language understanding model is built on LivePerson’s conversational platform and uses traditional AI, according to Senior Director of Conversational AI at Fifth Third Bank, Michelle Grimm.

Jeanie 2.0 is more user-friendly for the Fifth Third agents using it. It uses LivePerson’s predefined content library to streamline and increase efficiency for agents when they are helping customers. The documentation outlines the benefits of using predefined content, including: 

• Time savings for agents
• Ensures consistent, error-free responses
• Maintains a professional tone of voice

Fifth Third Bank’s Annual Report highlights the following benefits of Jeanie:

• Reduced calls requiring a live agent by nearly 10%
• Generated over $10 million in annual savings
• Improved customer satisfaction
• Improved employee retention
• Shortened account opening times by more than 60%

The same report also states that Jeanie underwent a significant update in October 2023, resulting in expanding Jeanie’s capabilities by over 300%.

Alex Ross, Sr. Content Producer for LivePerson, explained additional benefits in a blog post, including Jeanie’s ability to respond to over 150 intents and over 30,000 phrases with over 95% accuracy.

Improving Customer Experience (CX) with AI-surfaced Customer Satisfaction Scores

Traditionally, companies use a survey-based system to manage customer experience and agent performance, and Fifth Third Bank was no exception. The bank primarily relied on customer surveys to gain insight into how customers viewed their interactions with the contact center. However, this method has notable shortcomings, especially in an industry where 88% of bank customers report that customer experience is as essential, or even more important, than the actual products and services. These limitations include:

• Low visibility for managers into agent contributions to customer experience
• Identification of only a limited range of coachable topics

Also, metrics such as average handle time were used to evaluate agent performance. With an increase in automation, average handle time loses its ability to serve as an effective metric. The reason is that automation results in the more complex issues being surfaced to agents, which leads to an increase in average handle time. Fifth Third transitioned away from focusing on surveys in favor of sentiment analysis.

According to the case study published by NiCE, Fifth Third had multiple goals related to customer experience and satisfaction, including:

• Reach the top of independent third-party customer experience rankings
• Gain customer sentiment metrics from every interaction
• Obtain insights from a more representative group of bank customers
• Replace the costly, limited-utility survey program

Fifth Third Bank began using Enlighten AI and Nexidia Analytics by NiCE in the hopes of reaching those goals and improving how they coach 700 agents across 3 locations. More specifically, the sentiment score from Enlighten allows Fifth Third to find an ideal range for average handle time for its agents. 

In the video below, Michelle Grimm, Senior Director of Conversational AI at Fifth Third Bank, explains how the bank has improved agent feedback with Enlighten AI, citing metrics like average handle time at around the 1:21 mark:

Grimm explains how Fifth Third was able to use NICE Enlighten to identify the optimal AHT range beyond which sentiment declines once the upper end of the range is reached; the optimal range identified was 3-5 minutes.

Fifth Third uses the sentiment scores to refine the coaching process and ultimately improve customer interactions. Grimm mentions that her team uses a color-coded system consisting of a standard red, yellow, and green distribution to focus on positive behaviors for reinforcement and address areas for improvement.

The sentiment scores have served as a basis for improved collaboration across Fifth Third’s three geographically distributed call sites: Cincinnati, Grand Rapids, and the Philippines.

The bank also rolled out Nexidia Analytics in 2021. As part of that effort, they analyzed over 15.7 million interactions involving 2,300 agents.

With the shift from survey to sentiment, Fifth Third Bank saw some immediate benefits, including improved employee productivity, higher employee compliance, and lower costs as speech analytics identified processes that could be automated.

Increased sentiment scores can have wide-reaching effects across the entire enterprise over time. In the above video, Grimm is emphatic that positive feedback to agents translates into customer experience. This is why Grimm says that it’s essential for agents to hone areas in which they’re good as well as focus on areas of improvement. She fully recognizes how customer-based the banking industry is, which is why improving sentiment scores is so vital. 

Over time, sustained increased sentiment helps to:

• Strengthen customer loyalty
• Reinforce the bank’s reputation
• Reduce customer churn
• Reduce compliance risks

A 2023 Bain & Company report shows consumers who give high loyalty scores cost less to serve and spend more with their bank. Additionally, they are more likely to recommend their bank to family and friends.

While Fifth Third has not published quantifiable numbers, the bank has indicated in press materials that the use of NiCE Enlighten AI and Nexidia Analytics resulted in increased sentiment scores and lower costs.

Artificial intelligence is no longer a futuristic concept—it is the invisible engine powering modern workplaces. Yet, for Gen Z, this engine often hums with an undercurrent of anxiety. While older generations might approach AI with caution or curiosity, the youngest professionals find themselves thrust into a paradox: they are expected to master AI tools instantly, yet rarely receive guidance, training, or institutional support. The result is a pervasive tension, now recognized as “AI shame,” where employees hide their reliance on technology or feign expertise to avoid judgment.A 2025 survey by WalkMe, an SAP company, highlights the stark reality. Nearly 89% of Gen Z workers use AI to complete professional tasks—but 62% hide their AI usage, and 55% pretend to understand tools in meetings. In essence, the generation most comfortable with technology is also the most anxious about being judged for it. This tension is intensified by a lack of formal training: only 6.8% of Gen Z employees report receiving extensive guidance, while 13.5% receive none. With little structured support, most are left to navigate complex AI tools independently, often resorting to unsanctioned solutions that further heighten workplace insecurity.

The roots of Gen Z anxiety

The unease is not just about capability; it is about expectation and pressure. AI, while designed to increase productivity, paradoxically slows down many young workers: 65% of Gen Z respondents report that AI complicates rather than simplifies their workflows. Meanwhile, 68% feel pressured to produce more, and nearly one in three are deeply concerned about AI’s long-term impact on their careers.Another critical factor is the emerging “AI class divide.” Training and support are often disproportionately provided to executives and senior employees. Only 3.7% of entry-level staff—largely Gen Z—receive substantial guidance, compared with 17% of C-suite leaders. The consequence is a workforce where the heaviest users of AI are the least supported, intensifying stress and limiting the effective use of technology.

Turning anxiety into opportunity: Practical strategies for Gen Z

Despite these challenges, there are actionable ways for Gen Z to reclaim agency over AI:

The broader implication: Rethinking workplace AI culture

The Gen Z experience illustrates a broader challenge for organizations: AI adoption without support creates not efficiency, but anxiety. MIT research suggests a 95% failure rate for generative AI pilots at large enterprises, reflecting the gap between the theoretical promise of AI and its real-world application. Companies that fail to train and support employees risk a workforce where innovation coexists with stress, and potential remains unrealized.Ultimately, for Gen Z, mastering AI is not only a technological challenge but a psychological one. By embracing education, collaboration, and critical thinking, young professionals can transform AI from a source of silent anxiety into a catalyst for career growth. The task for organizations is clear: provide guidance, encourage experimentation, and recognize that the human side of AI is just as important as its computational power.

By Luca Bertuzzi ( September 1, 2025, 08:45 GMT | Insight) — The EU AI Act requires providers of generative AI to label synthetic content, but a specialist warns this may not cover AI agents that act independently. Researcher Alan Chan says the law’s wording is unclear on AI-generated actions, such as bots making purchases or manipulating online content. He argues actions should also be labeled to prevent deception, as existing industry practices may soon be ineffective.The EU AI Act includes a transparency obligation for providers of generative AI systems to indicate whether their content is synthetically generated. But as AI becomes more agentic, this requirement may not be sufficient, a specialist says….

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