Key Takeaways
- AI agents automate tasks and optimise decision-making across industries, improving efficiency and accuracy.
- Industries like healthcare, finance, eCommerce, and real estate use AI for diagnostics, fraud detection, personalised recommendations, and virtual property tours.
- AI enhances automation in travel, sales, education, and cybersecurity, streamlining customer experiences, content creation, learning personalisation, and threat detection.
- AI plays a vital role in agriculture, environmental monitoring, and autonomous vehicles, improving pest control, species tracking, and self-driving capabilities.
- Businesses must adopt AI agents early to stay ahead in an increasingly competitive landscape.
Introduction
AI agents are more than just a tech buzzword. These tools are already reshaping the way businesses operate. From streamlining processes to winning new customers, AI agents are helping companies to achieve success in ways that were never thought possible.
As AI agents continue to mature, we will likely see businesses discover new opportunities that will push them ahead of the competition.
In this article, we will walk you through 12 examples of how AI agents have helped businesses from various sectors overcome challenges and transform the way they work.
What is an AI agent?
Before we discuss the real-life applications of AI agents, we first need to understand what AI agents are. Basically, AI agents refer to computer programs that are capable of automating tasks on behalf of a user or other systems.
AI agents rely on data from large language models (LLMs) as well as connected tools to adapt to the changing environment and find the best course of action. The agents can save interactions into memory, which can then guide multi-step plans and operations.
How do AI agents work?
AI agents act on an observe-plan-act cycle that enables the technology to detect changes and adapt their actions to be more efficient and effective over time.
- Observe: In the observe stage, the AI agent gathers information about the environment. The information can range from user interactions and feedback to key performance indicators (KPIs).
- Plan: The AI agent combines the information from other tools and systems with the LLM data to evaluate and prioritise actions. These actions are based on what problems the business is looking to solve or what goals they are looking to achieve.
- Act: Depending on the plan, AI agents may either choose to connect with enterprise systems, delegate tasks to other AI agents, or ask users for clarification. After performing the action, AI agents can store the information to guide future responses.
For details, read: A Beginner’s Guide to How AI Chatbots Work
Role of AI agents in daily life
AI agents are responsible for automating repetitive tasks with a high degree of accuracy and speed. This is because AI agents can analyse their responses, detect information gaps, and correct errors.
AI agents can also act as advisers to human workers by providing them with insights and identifying patterns within large datasets. Workers can use this information to make strategic decisions.
It should be noted, however, that AI agents do not eliminate the need for human workers. They are still needed to handle complex cases that require direct intervention.
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12 real-world AI agent applications across industries
Various industries have already utilised AI agents to win new customers, deliver memorable campaigns, and ensure services continue to run smoothly.
In this section, we will highlight some of the ways in which AI agents can benefit each sector.
AI agent in healthcare
Speed and accuracy are key to saving lives. However, this leaves doctors with little time to do a detailed analysis of medical images for signs of conditions like diabetic retinopathy.
AI agents were able to overcome this issue by generating pixel-level maps to pinpoint retinopathy lesions. Additionally, AI agents also measured the severity of the disease, thus making it easy for doctors to determine which patients needed the most attention.
AI agents in finance
In a report by Group-IB, credit card fraud in the Asia Pacific region amounted to USD 11.9 billion worth of losses in 2023. To counter these incidents, banks have started embracing AI-powered innovations to improve fraud detection accuracy.
One such innovation is tree-based machine learning algorithms that break down the decision-making process into feature-based questions.
In the case of financial fraud, these questions typically involve queries like transaction amount, location, or merchant category. By pairing these algorithms with fraud detection datasets, banks are able to identify faulty financial transactions.
AI agents in eCommerce
Retailers have already embraced AI-powered recommendation engines to suggest products that customers are most likely to buy based on their preferences. In fact, Amazon was able to generate 35% of its sales using this technology.
Besides recommending products, AI agents are the driving force behind chatbots like Shopify Inbox. These tools are designed to automate interactions between customers and help them get answers to the most basic questions.
Moreover, AI-powered chatbots helped customer support teams focus on conversations that likely lead to a sale. By having AI-powered chatbots take over customer conversations, businesses were able to drive more checkouts and reduce the amount of emails they needed to handle.
Also read: AI Chatbots for eCommerce: Why Does Your Website Need One?
AI agents in retail
In the retail space, AI agents helped retailers stay informed of marketing trends and social media feedback. These insights were crucial in assisting decision-makers in making updates to the product’s design or streamlining their production process.
Besides delivering trends, AI agents have helped retailers stay on top of future demand and determine optimal stock levels. This is done by examining sales data, trends, and market inventory. One such company that has adopted AI agents to manage their inventory is Walmart, and this has led to a 20% reduction in stockouts.
AI agents in real estate
Matterport has used AI agents to create over 5 million virtual spaces that enabled prospective buyers to view homes remotely. These agents use computer vision and spatial analysis to piece images together and create a 3D representation of the property.
By giving prospect buyers the ability to view and measure homes without leaving their home, Matterport was able to save time and attract more customers.
Also read: AI Agents for Real Estate: Smarter Solutions for Property Sales
AI agents in the travel industry
Back in January this year, American-based Delta Airlines launched their “Delta Concierge” app that can help customers ensure a smoother travel experience. In particular, the app can notify travellers about upcoming passport expiration and visa requirements so they can better plan their travels.
Moreover, the app can also provide directions to the bag drop area so that travellers won’t have to waste time wandering around airports.
In the future, the “Delta Concierge” app is expected to release new features. This includes providing weather updates on travellers’ destinations and allowing passengers to arrange for an electric taxi service to the airport.
AI agents in sales and marketing
Marketing AI agents like WriteSonic enable marketing teams to publish content 10 times faster than before.
This is achieved by using natural language processing (NLP) and machine learning algorithms to analyse information, understand context, and generate human-like text. Through this feature, AI agents were able to automate the creation of video scripts and blog articles.
Another use case of AI agents is helping users measure KPIs in real time. This can range from the number of ad clicks to the sales cycle length. Using this information can help users identify which campaigns can resonate with audiences and optimise the sales process. Hubspot, in particular, uses sales data to identify bottlenecks as well as ways to speed up lead conversions.
Also read: How To Use AI As a Sales Rep?
AI agents in education
AI agents are capable of developing personalised and engaging lesson plans.
Take the Korbit learning platform, for example. After an initial assessment plan, the platform can devise a learning platform that is tailored to the student’s strengths and weaknesses, as well as learning objectives and time availability.
To help students who are struggling with problem-solving exercises, Korbit provides hints and explanations so that they can provide the right answers in future exercises.
AI agents in agriculture
The Centre for Agriculture and Bioscience International’s (CABI) Global Burden of Crop Loss Report found that up to 40% of crop loss is due to pests. Controlling them is crucial to ensure a continuous food supply. The problem is that pesticides, while effective at reducing the pest population, can also pollute the soil and harm human health if used excessively.
AI agents like YOLO can counter this issue by analysing images of sticky traps to identify and count captured insects. This information is useful in determining the best time to apply pest control measures.
AI agents for cybersecurity
Traditionally, malware detection used “signatures”, which are special strings of code that are unique to each malware. However, as attackers continue to evolve their malware programs, it becomes harder for experts to locate their signatures.
To overcome this limitation, cybersecurity experts have trained machine learning models to identify malware patterns using popular databases like EMBER.
These databases contain useful information like the distribution of different values, text strings, and the location of important files needed to run the malware. The results are optimistic, with current models achieving a 95% detection accuracy rate.
AI agents in environmental monitoring
The exact number of endangered animals isn’t certain, as many more species are roaming our planet that scientists have yet to discover.
However, according to the World Wildlife Fund (WWF), the extinction rate of animals is between 0.01 to 0.1% annually.
To illustrate, if there are 100 million known animal species on our planet, this means that we are losing at least 10,000 of them each year.
To counteract this problem, Wildbook used neural networks, computers, and satellite vision to monitor animals and estimate the population size. This information is crucial in identifying critically endangered species more quickly so that conservationists can take steps to prevent their extinction.
AI agents in autonomous vehicles
Tesla’s AI-powered autonomous systems use an approach called “imitation learning” in which it absorbs behavioral data from over 500 million vehicles. Through this approach, Tesla’s vehicles are able to navigate safely and adapt their driving style to their human passengers’ needs.
Even when a Tesla vehicle makes a wrong prediction, the AI can save a digital snapshot of that moment and recreate it for the neural network to learn from.
Conclusion
In this article, we have highlighted some of the many real-life scenarios where the power of AI agents truly shined.
From providing crucial insights to automating routine tasks, AI agents have been a supportive force for both workers and operations.
For other businesses that haven’t yet embraced AI agents, they should start sooner rather than later to stay ahead of the competition.
