5 Real-World Problems Agentic AI Is Solving Today

In July 2024, McKinsey declared AI agents to be “the next frontier of generative AI.” However, while the tech is promising, many entrepreneurs may still be unsure of what agentic AI is—let alone the practical problems agentic AI can solve in their own businesses. 

From retail to healthcare, education, and beyond, the applications of agentic AI are far reaching. This article takes a closer look at five real-world agentic AI applications across industries. 

Interested in diving deeper into how agentic AI is used? Explore agentic AI’s core capabilities.

1. Reducing Burnout in Healthcare with Agentic AI

Doctors and other healthcare professionals struggle with mental health and work-life balance, with 46% of health workers reporting feelings of burnout. According to the US Department of Health and Human Services, excessive workloads and lack of organizational support are key drivers of healthcare burnout. Fortunately, these issues are both excellent applications for agentic AI.

A few examples of agentic AI in action could be:

• Analyzing test results and other patient data to generate treatment plans or potential diagnoses, which doctors can then review.
• Referencing and cross-checking patient records when writing prescriptions to avoid allergies or harmful drug interactions.
• Scheduling appointments, providing patient support, and automating other time-consuming tasks.

By handling repetitive tasks and supporting clinical decision-making, these AI agents can take much of the weight off the shoulders of doctors, nurses, and other medical professionals. In doing so, the agents could give these workers the extra time and energy to provide their patients with higher-quality treatment.

2. Closing Learning Gaps with Agentic AI in Personalized Education

While proper education is crucial to the next generation’s success, not all students get the support they need. The National Center for Education Statistics (NCES) reported that, 21% of public schools had multiple teaching vacancies. Similarly, the University of Southern California Center for Economic and Social Research found that only 2% of students with a C average or lower grades receive high-quality tutoring. 

These gaps between student needs and resources to meet those needs have left many at a disadvantage. NCES also reports that 44% of students were behind in at least one subject. Fortunately, educators are now exploring what problems agentic AI can solve in their schools. 

With AI agents, schools can now offer students responsive, 24/7 support that adapts to their unique needs and blind spots, regardless of how many teachers or tutors a school has available. While it’s no perfect substitute for teaching, AI agents can help build personalized tutoring plans, grade exercises, and help students troubleshoot problems.

3. Improving the Buying Experience with Agentic AI in Customer Service

The customer service industry has utilized AI for years: In 2015, for instance, IBM Watson partnered with Go Moment to create Ivy, a “question-answering engine” that improved the customer experience for hotel guests. However, these nascent systems often served more headaches than service, with a 2022 survey by Forbes contributor Chris Westfall finding that 80% of consumers who interacted with a chatbot were left frustrated.

Unsurprisingly, agentic AI problem solving is leaps and bounds ahead of these narrow AI systems. Where chatbots are limited to preprogrammed instructions and struggle to understand context or complicated prompts, AI agents create personalized experiences based on user behavior and input. 

For example, an agentic AI use case could go something like this:

1. A customer realizes that their e-commerce order is late. They contact customer support and are put in touch with an AI agent.
2. The agent listens to their concerns and responds appropriately.
3. It autonomously pulls up the customer’s order details, checks their shipment tracking information, and confirms that the package is delayed.
4. The AI then generates a set of solutions based on the company’s policies—such as offering a free reshipment, a refund, or a discount—and presents them to the customer.
5. Once the customer makes their choice, the agent takes action to meet their request: whether that consists of finding the customer’s financial information to process a refund, placing a replacement order, or applying a discount to their account
6. Finally, the AI agent confirms the resolution with the customer, shares any necessary follow-up details (like a new tracking number), and schedules a check-in to ensure the customer’s satisfaction.

While there will always be a place for human interaction and oversight, agentic AI excels at handling simple or repetitive customer service requests, freeing human teams to spend more time on their other tasks. They also operate 24/7, avoiding the delays that might stem from time zone differences.

Gartner anticipates that, by 2029, agentic AI will autonomously resolve 80% of common customer service issues, cutting operational costs by 30%. This shift is a clear example of how agentic AI use cases can help businesses deliver faster, more reliable customer support.

Want to learn more about real-world agentic AI applications? Discover how AI is transforming customer interactions. 

4. Fixing Operational Inefficiencies with Agentic AI for Supply Chain Optimization

Agentic AI’s autonomous nature is invaluable for the logistics industry, where even slight disruptions can cost millions in damage. Traditional forecasting methods often rely heavily on historical data, making it difficult to keep pace with unpredictable factors like climate change or capacity shortages. 

Agentic AI problem solving, meanwhile, offers a smarter, faster approach. AI agents can rapidly analyze and reference real-time data such as weather patterns, traffic conditions, supplier delays, and market shifts to predict disruptions before they happen. 

An example of how agentic AI is used would be an agent autonomously adjusting supply chains to work around these issues–for example, by rerouting shipments to minimize downtime. 

Interested in AI for logistics? Learn about the impact of AI on supply chain efficiency.

5. Solving the Data Overload Problem with Agentic AI for Data Analysis

Though knowledge may be power, it’s possible to have too much of a good thing. The more data a business has, the longer it will take them to parse through it, remove any errors or inconsistencies, and process it into a usable format. 

Fortunately, agentic AI is intelligent enough to automate the complex but highly time-consuming task of analyzing vast datasets. 

FullStack Labs recently created an AI-powered auditing system that does just that. The platform autonomously transcribes calls for its client, searches the transcripts for potential regulatory risks, and then anonymizes sensitive information as needed. 

With agentic AI problem solving, the client saw a 99% reduction in manual review, with human auditors now having to review five sentences over the approximately 500 they handled before. 

What Problems Can Agentic AI Solve in Your Business?

Agentic AI problem solving might be a novel concept, but it is one that many companies are already eager to adopt into their own operations. Agentic AI’s market value is expected to jump from $5.1 billion in 2024 to over $47 billion by 2030, and Gartner predicts that, by 2028, 33% of enterprise software applications will incorporate these systems. 

With that in mind, it’s clear that within the next few years, agentic AI will shift from a novelty to a mainstream business tool. Companies that start exploring real-world agentic AI applications today will be better equipped to stay competitive, improve efficiency, and deliver smarter customer experiences tomorrow.

If you’re wondering what problems agentic AI can solve for your company, FullStack Labs can help. We build custom AI solutions designed to meet your needs, in whatever form that might take. Contact us today to get started. 

‍Want to learn more about agentic AI? Discover other practical applications of intelligent agents.