23 Real-World AI Agent Use Cases

Software assistants called AI agents have the ability to automate computer users’ repetitive tasks and respond to routine customer and employee questions. Unlike prior generations of helpers built into business applications, which relied on pre-coded rules or keyword triggers, AI agents take advantage of large language models’ predictive power and ability to communicate with users in natural language to carry out multistep workflows.

Agents can help organizations realize a return on their AI initiatives by reducing errors and streamlining processes for tasks such as researching customers for a deal, writing job postings and offer letters, and evaluating and suggesting repair options for manufacturing equipment. They can also help make individual contributors more productive and keep processes moving ahead, even overnight. Agents can look for information across different tools, taking users’ roles and other context into account and staying topical by pulling information from business documents to supplement the underlying LLMs’ training data. Read on to learn about top enterprise use cases for AI agents that your company may be able to put into practice.

What Is an AI Agent?

AI agents are software assistants, powered by generative AI, that mediate between pretrained LLMs and computer users to carry out a wide range of multistep tasks inside software applications or on the web. Instead of responding to preprogrammed rules or keywords like previous iterations of digital helpers, AI-powered agents can predict the next logical step in a series of tasks and present relevant information or complete steps in a process, tapping business documents to augment their original information sources.

Where Are AI Agents Being Used?

Businesses are building and deploying AI agents to assist with recruiting, explain pay and benefits to employees, field customer inquiries, work on sales deals, make financial projections, and undertake equipment repairs. In healthcare, medical practices and hospitals are using agents to help with scheduling and improve automated note-taking and documentation during patient visits, among other use cases.

23 AI Agent Use Cases for Businesses

While it’s early days for AI agents, adopters are using them to help complete processes that require access to multiple IT systems, deliver data-driven insights, and relieve employees of some of the steps involved in routine tasks. Here are some of the most common places they can be used in healthcare, HR, manufacturing, finance, and customer support.

Healthcare

1. 1. Clinical assistants. Allow physicians to call up information from patients’ medical histories by speaking instead of navigating menus. Clinical AI agents can be authorized to listen during exams to draft clinical notes, reducing documentation time and letting physicians focus on conversing with patients.


2. 2. Disease identification assistance. Compare clinical notes from patient visits with historical patient data to aid in making a list of possible diagnoses given ailments that present similar symptoms.


3. 3. Appointment scheduling. Automate appointment scheduling, patient registration, and bill creation.


4. 4. Drug discovery. Quickly read and extract information from large numbers of publications. Examine molecular structures to aid in finding new molecules that can serve as potential medicines.

HR

1. 5. Job posting. Write job listings with responsibilities, required experience, and other qualifications, in line with the employer’s hiring policies.


2. 6. Interview scheduling. Automatically set up meetings with candidates and send follow-up emails.


3. 7. Employee onboarding. Help fill out forms, conduct training, and set up user accounts and devices.


4. 8. Explain benefits. Converse with employees about paid time off, retirement savings, healthcare, family leave, and other benefits.


5. 9. Employee retention. Examine salary benchmarks and employee tenure, skills, and performance reviews to better determine turnover risks and help identify workers who could thrive in a higher-level job or benefit from a lateral move.

Manufacturing

1. 10. Maintenance estimates. Analyze damaged equipment via photos, estimate needed repairs, and select repair options to get the process started.


2. 11. Optimize delivery. Plan shipping routes based on the latest data to speed up deliveries, pare transportation costs, and reduce carbon emissions.


3. 12. Sales rep guidance. Provide up-to-date information about pricing policy changes so reps can make compelling offers while preserving profitability.


4. 13. Supply chain risk assessment. Analyze data on suppliers, products, and inventories, as well as weather, labor, trade, and other external data, to minimize supply chain disruptions and mitigate risk.

Finance

1. 14. Know Your Customer (KYC) processes. Replace rules-driven workflows with automated identity verification and risk scoring for KYC, the processes banks must follow to confirm their clients are who they claim to be and ask for missing information. Conduct customer assessments across multiple IT systems accumulated through bank mergers and acquisitions.


2. 15. Anti–money laundering and fraud detection. Monitor transactions continuously for suspicious activity.


3. 16. Automated trading. Analyze data on market fundamentals, price movements, trading volumes, risk, and other factors to inform securities trading strategies.


4. 17. Credit underwriting. Pull data from various sources, compute financial ratios, show potential outcomes versus risk tolerance, and draft credit memos for review.

Customer support

1. 20. Triage and route tickets. Learn from past ticket data to classify customers’ requests and better understand sentiment, automate responses to common queries, and escalate queries needing more attention.


2. 21. Speed up returns. Check the policy on returned items, generate a return order, and send it to a customer.


3. 22. Order delivery tracking. Automatically answer often time-consuming questions about order status, schedule deliveries, and suggest optimal delivery routes.


4. 23. Recommend resolutions. Suggest AI-recommended steps to resolve customers’ technical, product setup, or other problems, possibly reducing call center workloads.

Future of AI Agents

More efficient AI models may lead to agents that are able to work more autonomously through routine process steps. Testing procedures will also improve as software vendors add quantitative measures of AI agents’ effectiveness. The industry may also agree on a standard protocol for communications among agents.

Build Your AI Agent with Oracle

Oracle AI Agent Studio for Fusion Applications lets businesses set up autonomous agents in their Oracle Fusion Cloud Applications by customizing prebuilt templates that contain the code needed to get started. Admins for those applications can describe which functions their agents should perform, set limits on their actions, and instruct them about the documents and data they need to tap to complete their tasks. Oracle says more than 50 AI agents are rolling out across the Fusion Cloud Applications Suite to help with a wide variety of real-world use cases. AI agents are included in Oracle Fusion Cloud Applications subscriptions at no additional cost.

AI Agent Use Cases FAQs

What does an AI agent do?

AI agents are generally designed to use the natural language conversation and prediction capabilities of large language models to automatically show computer users insights gleaned from multiple IT systems, or they carry out steps in a process, which can save users time.

Is ChatGPT an AI agent?

No, the ChatGPT service from OpenAI is a chatbot that lets users conduct a typed dialogue with the company’s LLMs, but it doesn’t autonomously take actions on a user’s behalf.