AI Agents in Practice: Examples and Measurable Business Benefits

This article is part of a series on business applications of AI agents, where we examine the characteristics, use cases, and strategic utilization of AI agents in organizations.

In the previous article "AI Agents in Business: What Sets Them Apart from Other Technologies?" we explored the fundamental characteristics of AI agents and how they differ from other technologies. To recap briefly: AI agents are systems capable of operating autonomously in their environment, making independent decisions, executing actions, and learning from their experiences—in contrast to passive AI tools that merely respond to user requests.

In this article, we'll explore business areas where AI agents have proven valuable. The goal is to provide concrete insights into how organizations across various industries are already leveraging this technology and what measurable results they've achieved.

Applications of AI Agents in Business

AI agents offer significant opportunities for process automation across various business areas. AI agent technology is still in its early development stages, and current applications often fall on a continuum where fully autonomous agents don't yet exist—systems still require human oversight or guidance at certain stages.

The following examples represent solutions that leverage characteristics typical of AI agents: autonomous decision-making, proactive action, and learning capabilities. These examples are from large organizations with substantial resources for implementing AI agents. However, it's important to note that the benefits offered by AI agents are scalable and applicable to smaller organizations as well. As you explore these examples, consider:

1. Which current processes in your own organization could benefit from similar automation?
2. Does your organization have the necessary data infrastructure and integration capabilities?
3. What measurable benefits could you target using AI agents?

1. Customer Service and User Experience

In the fast-food industry, every second in the service chain is measurable and directly impacts profitability. Efficient and fast service is a critical competitive factor, and bottlenecks that slow customer flow, especially at drive-through lanes, can significantly affect revenue.

Example: Wendy's FreshAI

Wendy's, a fast-food chain operating over 7,000 restaurants globally, has implemented the FreshAI system, which functions as an AI agent in processing drive-through orders to create a faster, more accurate, and more consistent customer experience.

AI Agent Features:

• Autonomous operation: The system processes orders independently, making decisions about order progression without constant human guidance
• Natural language understanding: The agent recognizes spoken language, slang, and regional expressions (e.g., "chocolate shake" → "chocolate Frosty")
• Multilingual interaction: The system supports both English and Spanish orders and switches languages with a simple command
• Real-time adaptation: The agent dynamically adapts to different ordering situations and continuously learns to improve its performance
• Visual support feature: Customers see their orders in real-time on a digital display, increasing confidence in order accuracy

Measurable Results and Business Benefits:

• 86% of orders are processed entirely without staff intervention
• Nearly 99% success rate when accounting for situations where staff corrected errors
• 22 seconds faster service time compared to regional average
• Staff freed up for food preparation and direct customer service, improving both operational efficiency and customer satisfaction
• Expansion from approximately 100 restaurants (early 2025) to 500-600 restaurants by the end of 2025

Wendy's FreshAI demonstrates how generative AI can successfully transform the customer experience in the fast-food industry. Wendy's sees the FreshAI platform expanding to other channels in the future, including mobile apps, self-service kiosks, and smart home devices. The case highlights the strategic alignment of AI initiatives with business objectives rather than "innovation for innovation's sake." Wendy's CIO Matt Spessard emphasizes that FreshAI "serves as a tool, not a replacement," reflecting the company's view of AI's role as a staff supporter.

2. Financial Management

In the mortgage market, competitive advantage comes from speed, accuracy, and service quality. Mortgage application processing has traditionally been a labor-intensive and time-consuming process where processors must review numerous documents. The ability to automate this process directly impacts a company's competitiveness in the market.

Example: United Wholesale Mortgage

United Wholesale Mortgage (UWM), America's largest mortgage lender, processes tens of thousands of loan applications monthly. UWM has entered into a strategic partnership with Google Cloud in April 2025 to implement AI agents that significantly streamline mortgage processing.

AI Agent Features:

• Intelligent assessment capabilities: AI agents analyze loan applications holistically and support processors in decision-making
• Autonomous document processing: Agents independently extract critical information from documents, compare them side by side, and present them to processors in a clear format
• Communication automation: Agents handle routine communication and prioritize incoming messages
• Real-time expert support: AI agents provide immediate information about loan products and fees, enabling comprehensive document analysis
• Data-driven personalization: Agents utilize analytics to offer more precise loan recommendations to customers

Measurable Results and Business Benefits:

• Productivity growth: Loan processor capacity has increased from six loans to fourteen per day, representing over 130% improvement
• Time savings in document processing: Financial data processing time has reduced from three minutes to 30 seconds per loan
• Reduced workload: Email processing work has decreased by 20%, removing over 50,000 tasks from the employee queue
• Broader service capability: The system serves 50,000 mortgage brokers and their clients

These intelligent agents have fundamentally changed UWM's operating model. The scale is evident in that the system serves 50,000 mortgage brokers and their clients. The strategic implementation of AI agents has given UWM a significant competitive advantage in a market where speed and accuracy determine success.

This case demonstrates how AI agents can transform an entire business model in the financial industry: processors are freed from mechanical tasks to focus on expert work, customers receive faster service, and the organization can scale operations without proportional growth in human resources. UWM's experience confirms that the true value of AI agents lies not just in operational efficiency, but in the ability to grow business and improve customer experience simultaneously.

3. Real-time Decision Making and Risk Management

In the telecommunications industry, competition for customers is fierce and customer churn is high. The industry faces a 27-30% annual churn rate, meaning nearly one-third of customers switch providers each year. In this competitive market, customer experience plays a central role in reducing customer attrition. Simultaneously, operators face the challenge of leveraging vast amounts of data collected from customers across different channels and systems.

Example: Vodafone

Vodafone, one of the world's largest telecommunications companies, operates in multiple countries and serves over 340 million customers, each leaving a digital footprint. Vodafone has implemented an AI agent-based system on Quantexa's Decision Intelligence platform that provides a 360-degree view of customers and supports real-time decision-making in customer service. The system combines data from various sources and helps Vodafone offer relevant services to customers while improving service quality.

AI Agent Features:

• 360-degree customer view: The AI-assisted system collects and analyzes customer data from multiple sources and builds a contextual overview of the customer and their purchased services
• Real-time analytics: The intelligent analytics engine enables the utilization of more comprehensive and up-to-date information in customer interactions
• Data-driven decision support: The system provides customer service representatives with reliable information, streamlining decision-making and enabling more targeted customer communication
• Contextual data management: The system combines separate data sources into a meaningful whole through data contextualization
• Insight Engine: An AI agent that analyzes performance metrics and converts natural language queries into data searches, supporting dynamic and data-driven decision-making
• Enigma: An AI agent that enables efficient access to thousands of technical documents and resources, speeding up information search and usage

Measurable Results and Business Benefits:

• Enhanced analytics: The system produces up to 90% more accurate analyses compared to traditional approaches
• Faster data analysis: Analytical model resolution time is up to 60 times faster than with traditional methods
• Better customer experience: The system enables more targeted and relevant customer communication
• Improved sales: The system supports sales teams by providing timely customer insights and prompts for customer contact
• Better data utilization: Consolidating distributed data sources improves the organization's internal data usability

"It's vital to ensure we have the systems, tools and data that work together to help us connect the dots and ensure we get the full contextual picture of our customers. The use of AI is a key shift in our customer understanding and central to our strategic focus on delivering the best customer experience," says Miryem Salah, Vodafone UK, Chief Data Officer and Head of Digital & Transformation.

Initially designed for SME customers, the solution is scalable to cover different customer segments, further expanding the potential for AI agent utilization.

Central to Vodafone's solution is data contextualization—instead of individual data points, the system builds meaningful connections between data elements, making information more useful and valuable for business decision-making.

4. Systems Monitoring and Troubleshooting

In the energy industry, electrical grid reliability is critical, and disruptions can cause significant financial losses and safety risks. Climate change has increased extreme weather events like storms and wildfires, presenting new challenges to infrastructure resilience.

Example: Southern California Edison

Southern California Edison (SCE), Southern California's largest electric utility, serves over 15 million people across 50,000 square miles. SCE leverages multiple parallel AI agent solutions for monitoring and planning its electrical grid and infrastructure.

SCE's AI Agent Features:

• Continuous monitoring: Agents monitor electrical grid operations and infrastructure condition in real-time, enabling rapid response to anomalies
• Predictive analytics: The system analyzes data to identify potential problems before they occur, reducing unplanned outages
• Decision support: Agents analyze complex grid data and support operator decision-making in grid load management, aiming to develop increasingly autonomous functions by the end of the decade
• Geospatial optimization: The system uses location data for resource allocation and infrastructure planning, prioritizing the most critical areas
• Climate risk management: Agents analyze weather data and environmental information to identify and predict risk areas, improving preventive maintenance
• Real-time situational awareness: Digital twin covering 50,000 square miles, plus chatbot connected to over 100,000 network devices and internal documentation improves real-time situational awareness and speeds response to changing conditions

Measurable Results and Business Benefits:

• 50% faster vegetation clearing through identification and prioritization of high fire risk areas
• Reduced outages and disruptions through predictive analysis of data from 100,000 network devices
• Enhanced risk area identification through digital twin, reducing monitoring blind spots
• Preventive fault management by identifying trends before actual disruptions occur

SCE's AI investments are part of the company's broader strategy to improve the electrical grid's ability to withstand extreme weather and climate change challenges. The company has announced it will strengthen its risk modeling and increase climate projections in future plans. SCE's vision is set for an AI-driven "self-healing grid" by the end of the current decade, where data from millions of sensors is utilized for near real-time fault correction.

5. Healthcare

In healthcare, efficient transfer of patient information between healthcare staff is critical for care continuity and patient safety. Shift changes are particularly high-risk moments when important information can be missed. The administrative burden on nurses is significant, taking time away from direct patient care and causing decreased job satisfaction amid a national nursing shortage.

Example: HCA Healthcare

HCA Healthcare, operating over 180 hospitals and approximately 2,300 care units in the United States and United Kingdom, has developed a virtual AI nurse assistant agent named Cati. The agent is currently in pilot use at several HCA hospitals.

AI Agent Features:

• Information compilation and organization: The agent compiles patient's critical information, latest lab results, and medication changes into a concise format, ensuring care continuity during shift changes
• Interactive information retrieval: Nurses can make natural language queries about patient information (e.g., "latest creatinine value?") and receive immediate answers
• Patient communication support: The system helps produce clear language summaries for patients and families at discharge
• Clinical documentation automation: The system automatically produces structured reports, giving nurses more time for direct patient care
• Integration of information from various sources: The agent combines patient information from different systems and presents it in a clearly usable format
• Remote assistance functions: Enables efficient work for remote nurses through video connection

Measurable Results and Business Benefits:

1. Significant time savings: Producing Cati reports takes significantly less time compared to traditional documentation. In pilot projects, nurses have reported streamlined workflow and time savings
2. Excellent patient satisfaction: At HCA Florida University Hospital, patient feedback on virtual care experience was 100% positive, and at Mission Hospital in Asheville, positive patient satisfaction was 88%
3. Nursing resource optimization: In units utilizing the virtual nurse model, such as TriStar Skyline Medical Center, shift workload balances and nurses can focus more on direct patient care
4. New job opportunities: The system offers job opportunities for experienced nurses considering retirement or with work capacity limitations
5. Scalable automation: HCA Healthcare targets a long-term goal of 75-80% automation rate in clinical documentation, which would significantly free up nurse time for direct patient care

The Cati agent exemplifies how AI can be utilized to streamline healthcare workflows in ways that improve both healthcare staff job satisfaction and patient experience. Its benefits relate to efficiency and quality metrics important to HCA's business.

6. Software Development and DevOps

In e-commerce, technology is at the heart of business, and software developer productivity determines a company's ability to innovate and respond to market changes. Technological agility is a key competitive factor in the industry.

Example: Wayfair

Wayfair is one of the world's largest online furniture retailers with over 22 million customers. The company's technology teams manage hundreds of microservices and millions of lines of code, and developer efficiency is directly connected to business success. Wayfair has implemented two types of AI agents to improve both developer productivity and customer service: Gemini Code Assist developer agents and Agent Co-pilot sales support agents. These agents work in real-time to support users, helping developers produce higher quality code faster and sales representatives serve customers more efficiently.

AI Agent Features:

Gemini Code Assist Developer Agents:

• Act as software developer's work partner, supporting different stages of work
• Provide suggestions on how to develop and improve existing code
• Identify repetitive tasks across software development projects and automate them
• Automatically create tests needed for software quality assurance
• Integrate seamlessly with developers' daily tools
• Communicate with developers in natural language, making usage easy

Agent Co-pilot Sales Support Agents:

• Analyze customer situations in real-time and offer appropriate response options
• Collect and combine information from multiple sources (products, instructions, previous customer conversations)
• Understand customer conversation context and history
• Give sales representatives the ability to modify and personalize suggested responses
• Automatically evaluate response quality before sending them to customers

Measurable Results and Business Benefits:

Gemini Code Assist Developer Agent Results:

• Software development startup time halved (55% faster)
• Software quality assurance coverage improved by 48%
• Software performance improved by 48%
• Over half of developers (60%) report being able to focus on more meaningful tasks
• Significant improvement in developer satisfaction

Agent Co-pilot Sales Support Agent Results:

• 10% shorter processing times in customer service situations
• Overall customer service efficiency improved while maintaining high service quality
• Better ability to identify opportunities for service and product development based on customer feedback
• Better customer satisfaction

According to Wayfair's CTO Fiona Tan, implementing AI agents significantly accelerates the development process for new products and services and helps standardize work practices across the company. The company plans to expand Code Assist tool usage to all software development teams and utilize AI agents in security monitoring and production process management.

In developing the sales support system, Wayfair aims to integrate advanced search technology that enables agents to access real-time information such as product range, customer feedback, and customer purchasing preferences. The goal is also to develop AI agents to model the customer service styles of top-performing sales representatives, so these best practices can be shared across the entire sales team.

Common Success Factors in AI Agent Implementation

The company names, numbers, and results mentioned in this article are based on public documents and press releases from the respective organizations. Analyses represent the author's own observations from publicly available material.

The following success factors appear to unite successful AI agent implementations across different industries:

Clearly defined use case and objectives

In all successful implementations, organizations have started with a clearly bounded problem whose solution provides concrete business value. For example, in Wendy's case, the goal was to free employees for customer service, which is a clear and measurable objective.

Adequate data infrastructure and integration capabilities

AI agents need access to relevant data and systems to function effectively. Vodafone's 360-degree customer view required strong data integration.

Gradual increase in autonomy

Successful implementations have typically started with simpler functions and gradually expanded to more complex operations. Wayfair's Code Assist pilot demonstrates the benefits of gradual implementation, as developers first learn to trust and utilize the agent in simpler tasks before moving to more demanding use cases.

Clear operational authority and boundaries

In successful implementations, AI agents have clearly defined operational authorities—what they can do independently and when they should escalate situations to humans. HCA Healthcare's Cati assistant has clearly defined operational boundaries that ensure patient safety.

Continuous learning and development

Effective AI agents are not static systems but learn and improve through use. Southern California Edison continuously develops its geospatial AI models to improve network reliability in changing climate conditions.

Summary

These examples demonstrate that AI agents are already producing significant benefits across various industries. Fast-food restaurants, mortgage companies, telecommunications operators, energy companies, e-commerce, and healthcare organizations have shown that when properly implemented, AI agents automate complex processes and produce measurable results:

• Improved customer satisfaction
• Cost savings through automation
• Reduced errors and improved accuracy
• Resources freed for more value-adding tasks
• More anticipatory and proactive operations

These successful implementations didn't happen by chance. Successful AI agent implementations share common factors: clearly defined use cases and objectives, adequate data infrastructure, gradual increase in autonomy, clear operational authority, and continuous learning and development.

While the examples are from large organizations, AI agent benefits aren't tied to company size. Smaller and medium-sized organizations can achieve relatively as significant benefits through more agile decision-making, targeted solutions, and ready-made platforms. In smaller companies, streamlining just one or two processes can result in significant improvement in overall efficiency.

AI agent implementation should be viewed as a scalable opportunity that benefits organizations of all sizes in ways adapted to their own needs. Starting can happen with a single, clearly bounded use case.

Consider for your organization:

1. Which current processes in your organization could benefit from autonomous, proactive AI agents?
2. Does your organization have the necessary data resources and integration capabilities for AI agent implementation?
3. What measurable benefits would you primarily target through AI agents?
4. Where would be the lowest threshold to start piloting AI agents?
5. Which pilot project would you start first if AI agents were implemented in your organization tomorrow?

In the next article, we'll delve deeper into the future direction of AI agent development, how organizations can strategically implement them, and what practical steps are worth taking for implementation.

Sources

The results and quotes presented in this article are based on the following public sources:

Wendy's FreshAI

• Wendy's official blog (2025): "Transforming the Ordering Experience"
• Forbes (2025): "Wendy's Serves Up Generative AI"
• Food & Wine (2024): "Wendy's Drive-Thru AI Language Support"

United Wholesale Mortgage

• UWM-Google Cloud partnership announcement (April 2025, BusinessWire)
• UiPath case study: "UWM Accelerates Loan Processing"

Vodafone

• Vodafone official press release (2022): "Quantexa partnership"
• LangChain Blog (2025): "Vodafone transforms data operations"

Southern California Edison

• Neara official case study: "Wildfire Risk Mitigation"
• WWT case study: "GenAI Chatbot at SCE"

HCA Healthcare

• HCA Healthcare Today (2024): "Virtual nursing transformations"
• MedCity News (2023): "HCA generative AI integration"

Wayfair

• Google Cloud Blog (2024): "Latest Gemini models in retail"
• Wayfair Tech Blog (2023): "Google Cloud Next insights"

Additionally, the article utilizes information from McKinsey & Company, MIT Sloan Management Review, and industry annual reports.