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Top Applications of Artificial Intelligence (AI) Across Industries

Top Applications of Artificial Intelligence (AI) Across Industries
Hostman Team
Technical writer
Infrastructure

Today, artificial intelligence has already penetrated all spheres of our lives. Not long ago, it seemed that neural networks and artificial intelligence would not be able to perform most everyday human tasks. However, thanks to computational resources and machine learning algorithms, neural networks have learned not only to compose texts and solve mathematical equations but also to recognize objects in images and videos (for example, for autonomous vehicles), as well as to manage production lines and logistics (for example, to optimize delivery routes). 

In today’s article, we will examine what artificial intelligence can do and what people use AI for in various areas of application. We will also explore real practical examples of using neural networks in everyday tasks.

Introduction to the Application of Artificial Intelligence

Artificial Intelligence (AI) is a branch of computer science that designs and creates systems intended to perform tasks that require human intelligence. Simply put, AI is a computer program that receives and analyzes data and then draws conclusions based on the results. AI is a multifunctional tool that covers a wide range of tasks: processing large volumes of data, learning, forecasting, speech, text, music recognition, and more.

Today, the capabilities of artificial intelligence have become practically limitless. Here are some tasks where AI is already successfully applied and even replaces humans:

  • Processing large volumes of data (Big Data).
  • Automating various routine processes (for example, in IT).
  • Recognizing and analyzing text, images, videos, sound, etc.
  • Forecasting and modeling (for example, in finance or medicine).
  • Personalization (for example, recommendation systems on streaming platforms and online stores).
  • Managing complex systems (autonomous vehicles, logistics, robotics).

This "explosion" in demand for AI is associated with the following advantages:

  • Efficiency: significant acceleration of processes while reducing costs.
  • Accuracy: minimizing human errors.
  • Scalability: processing and analyzing enormous data volumes in real time.
  • Innovation: AI can open new possibilities in fields such as medicine, transport, marketing.
  • Accessibility of technology: with increased computing power and data volume, AI applications have become cheaper and more widespread, allowing penetration into many fields.

Main Areas of AI Application

Let’s look at what AI is being used for in various societal sectors.

Medicine and Healthcare

The medical and healthcare sector is one of the most promising areas for implementing neural networks and AI. The adoption and funding of AI in healthcare are continuously growing. For example, an analytical report by CB Insights noted a 108% global funding increase in 2021. Here are real examples of AI in medicine:

  • In March 2025, an international group of scientists from the University of Hong Kong, InnoHK D24H lab, and the London School of Hygiene developed a special AI model for diagnosing thyroid cancer. Experiments showed the model’s accuracy exceeded 90%. One key benefit is nearly halving the time doctors spend preparing for patient appointments by analyzing medical documents using advanced tools like ChatGPT and DeepSeek.
  • AI is also used beyond text data. For example, it can detect prostate cancer using MRI scans as input data.
  • Major tech companies actively use AI in medical services. Google Health has developed an AI for analyzing mammograms to detect breast cancer.
  • IBM, a pioneer in computing, is deploying AI to handle medical information and assist doctors in selecting personalized cancer treatments. IBM is also advancing generative AI chatbots (watsonx Assistant), which are used in healthcare.

Finance and Banking

The financial and banking sector is no exception. AI is widely used for forecasting (including risk assessment), detecting potential fraud, and offering clients personalized services and offers based on their spending patterns. Specially trained algorithms analyze transactions in real time, identifying suspicious and fraudulent activities. AI is well established in credit and mortgage markets, aiding credit scoring, market trend prediction, investment management, and trading. Some practical examples:

  • Goldman Sachs, a major investment bank and financial conglomerate, employs smart assistants to help employees with tasks such as summarizing documents, editing emails, or translating texts.
  • PayPal uses AI extensively to detect fraudulent transactions in real time, processing billions of operations annually.
  • JPMorgan Chase uses the AI-powered Coin service to analyze legal documents, reducing document processing time from 360,000 hours per year to just seconds.

Industry and Manufacturing

In industry and manufacturing, AI primarily automates technological processes. It also handles equipment diagnostics and various tasks on assembly lines, helping companies reduce production costs, predict equipment failures, and minimize downtime.

  • Siemens, a German conglomerate in electrical engineering, electronics, and energy equipment, uses AI to service its turbines by forecasting equipment failures and optimizing maintenance schedules.
  • Major airlines such as Emirates and Delta Air Lines use the industrial software platform Predix for real-time predictive analytics. This AI usage has cut engine repair costs by 15% and reduced flight delays by 30% due to better failure prediction.
  • French energy engineering company Schneider Electric employs Robotic Process Automation (RPA) to handle labor-intensive tasks related to preparing documents for switchboard operators and managing supply chains.

Transport and Logistics

In transportation, AI is heavily used in autonomous vehicles. AI processes data from cameras and radars to ensure safe movement. In logistics, AI focuses on optimizing delivery routes, performing analytics and forecasting, and managing warehouse inventories, thereby reducing costs and speeding up business processes. City transport authorities use AI to automatically assign drivers to routes or select buses for deployment on routes, taking passenger flow into account.

  • Waymo, a manufacturer of autonomous vehicle technology, actively markets self-driving cars equipped with AI that are already transporting passengers in some U.S. cities.
  • DHL, an international express delivery company, uses AI to optimize delivery routes, cutting time and costs. It also employs robotics extensively in warehouses and sorting centers.

AI in Everyday Life

AI and neural networks are not limited to large industries and companies. Millions of users worldwide use AI-integrated apps and services every day, including:

  • Smart assistants: Voice assistants like Siri, Alexa, and Google Assistant use AI to process voice commands, answer questions, and control smart devices. They continuously learn to improve speech recognition and personalization.
  • Streaming platforms: AI underpins recommendation systems on major platforms such as Netflix, YouTube, Amazon Prime, and Spotify. Algorithms analyze user preferences to suggest content likely to be enjoyed, increasing audience engagement and improving user experience.
  • Natural language processing: AI is used in translators and chatbots—for example, translating between languages or providing customer support on airline and software manufacturer websites.

Promising Directions for AI Development

Although AI already handles many human tasks, its potential remains far from fully realized. Future trends in AI include:

  • Quantum computing: Quantum computers promise to accelerate data processing dramatically, potentially leading to breakthroughs in AI. They will enable solving problems currently inaccessible even to the most powerful supercomputers, such as molecular modeling for pharmaceuticals.
  • Neuromorphic technologies: Neuromorphic chips that mimic the human brain could make AI more energy-efficient and faster, especially valuable for IoT devices and autonomous systems.

Ethical Aspects of AI Application

Ethical issues arise with AI, such as algorithmic bias. Protecting data privacy is also crucial. Developing ethical standards for AI will be a key factor in the further use of neural networks and artificial intelligence.

The Future of Artificial Intelligence

According to some forecasts, by 2030, sectors already actively using AI will grow 3 to 5 times. Digital technology markets where AI is just gaining momentum will grow 6 to 11 times. The main global AI demand will come from retail, medicine, and transport, driven by the development of new solutions that facilitate production processes.

Additional future trends include:

  • Mass adoption of robotics: The widespread use of autonomous vehicles, drones, and robots will expand into more areas, including science and education.
  • Mass use of AI in education: New platforms will emerge, offering personalized learning tailored to each student’s abilities and creating individualized study plans.
  • Development of generative AI: This technology creates text, images, music, conversations, stories, and more. It will be especially valuable for companies engaged in multimedia production, product design, and creative industries.

Limitations and Potential Risks

Rapid AI development and widespread use have introduced many risks, including job losses, data leaks, and AI misuse in criminal and fraudulent activities. To mitigate these threats, some countries are implementing AI regulations. For example, the European Union’s AI Act, effective from February 2, 2025, bans AI systems posing risks to safety, health, or fundamental rights—except for national security cases. It specifically prohibits programs that assess and score human social behavior.

Other limitations include the high cost of development, processing huge data volumes, and high energy consumption.

Conclusion

Today, we discussed various fields where neural networks and artificial intelligence are applied. In today’s reality, AI is everywhere—from algorithms in apps to complex production and healthcare systems. Despite widespread adoption, AI’s full potential is still unfolding, and we must prepare for the broader integration of new technologies into our lives.

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Thus, Canvas is focused on interactive creation, editing, and real-time content collaboration, while Deep Research is aimed at collecting and synthesizing information to provide comprehensive reports.   Deep Research Canvas Purpose In-depth data collection/analysis Interactive creation and editing of content Result Detailed reports Edited documents Mode Autonomous Active Execution time Several minutes Instant Task type Research, reviews, analytics, summaries Writing, coding, prototyping Users can attach various files to their messages, from documents to images. Along with a text prompt, Gemini can analyze media files, describing their content. Thus, the user can create multimodal queries consisting of both text and media simultaneously. This approach increases the accuracy of responses and creates a wider communication channel between humans and AI. In other words, the browser version is the main way to use Gemini. It is also worth briefly discussing how to register for Gemini and what is required for this. In most LLM services, authorization is required. Gemini is no exception. To launch the chatbot, you must sign in with a Google account. The registration process is standard. You need to provide your first and last name, phone number, and desired nickname. After this, you can use not only Gemini but also the rest of the Google ecosystem applications. Mobile App for Android and iOS You can download the official Gemini mobile app from Google Play or App Store. Functionality-wise, it is not very different from the web version available in a browser, but it has deeper features for user interaction and smartphone integration. Moreover, on many Android devices, the app comes pre-installed. Essentially, it is a mobile client that expands cross-platform access to the Gemini language model. The main differences lie in optimization for specific platforms: Content management. On the browser version accessed from a computer, it is much more convenient to work with text, code, tables, graphs, diagrams, images, and video. Conversely, the mobile app interface, designed for touch and gesture interaction, simplifies use on smartphones and tablets, but does not offer the same efficiency as a keyboard and mouse. Voice input and interaction. The mobile app has more advanced voice input and live interaction features (Gemini Live), allowing you to communicate with the model in real time, using the camera to show objects, the microphone for direct conversation, and screen capture to share images. The browser version lacks this functionality. Device-specific features. The Gemini mobile app integrates closely with smartphone functions (clock, alarm, calendar, documents) for more personalized interaction. The browser version exists in a kind of vacuum and knows almost nothing about the user’s computer. 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Access to basic models, Gemini Flash and Gemini Pro. The first is optimized for fast and simple tasks, the second offers more advanced features but with limitations. Limited context window size up to 32,000 tokens (equivalent to about 50 pages of text). No integration with Google Workspace apps (Gmail, Docs, and others). No video generation functions. Data may be used to improve models (this can be disabled in settings, but it is enabled by default). Limited usage quotas for more advanced models and functions. Advanced. An enhanced plan with extended functionality. Suitable for complex tasks requiring deep data analysis. Pricing starts at $20/month. Access to advanced and experimental models without restrictions. Increased context window size up to 1 million tokens (equivalent to about 1,500 pages of text or 30,000 lines of code). Deep integration with Google Workspace apps. Image and video generation functions. Data is not used to improve models. Expanded voice interaction capabilities via Gemini Live, including the ability to show objects through the camera. Priority access to future AI features and updates. Second, there are extended plans for commercial (business) and non-commercial (educational) organizations, offering additional collaboration and management features: Business. Provides extended functionality of the Advanced plan with additional tools for team use. Designed for small and medium businesses. Pricing starts at $24/month. Enterprise. Provides extended functionality of the Business plan with additional tools for AI meeting summaries, improved audio and video quality, data privacy, and security protection. It also has higher limits and increased priority access. Designed for large international companies with high security and scalability requirements. Pricing starts at $36/month. Education. Provides full access to Gemini’s generative capabilities for educational institutions, including many additional features tailored to the learning environment. Custom pricing. Gemini API for Developers Specifically for developers engaged in machine learning and building services based on large language models, Google provides a full API for interacting with Gemini without a graphical user interface. Moreover, Google has separate cloud platforms for more efficient development and testing of applications built with the Gemini API: Google AI Studio. A lightweight and accessible platform designed for developers, students, and researchers who want to quickly experiment with generative models, particularly the Gemini family from Google. The tool is focused on working with large language models (LLMs): it allows you to quickly create and test prompts, adjust model parameters, and get generated content. The platform offers an intuitive interface without requiring deep immersion into machine learning infrastructure. Simply put, it’s a full-fledged sandbox for a quick start in the AI industry. Vertex AI. A comprehensive artificial intelligence and machine learning platform in Google Cloud, designed to simplify the development, deployment, and scaling of models. It combines various tools and services into a unified, consistent workflow. Essentially, it is a unified set of APIs for the entire AI lifecycle, from data preparation to training, evaluation, deployment, and monitoring of models. In short, it is a complete specialized ecosystem. Gemini Gems. A set of features in Google Gemini designed to automate repetitive tasks and fine-tune model behavior. It allows you to create mini-models tailored for specific, narrow tasks: creating recipes, writing code, generating ideas, translating text, assisting with learning, and much more. In addition to manual configuration, there are many ready-made templates. Naturally, Google provides the API as a separate channel for interacting with Gemini. With its help, developers can integrate text generation, code writing, image processing, audio, and video capabilities directly into their applications. Access to the API is possible through the Google Cloud computing platform. Working with Gemini without a graphical user interface is a separate topic beyond the scope of this article. You can find more detailed information about the Gemini API in the official Google Cloud documentation. Nevertheless, it can be said with certainty that working with the Gemini API is no different from working with the API of any other service. For example, here is a simple Python code that performs several text generation requests: from google import genai # client initialization client = genai.Client(api_key="AUTHORIZATION_TOKEN") # one-time text generation response = client.models.generate_content( model="gemini-2.0-flash", contents="Explain in simple words how generative AI works", ) print(response.text) # step-by-step text generation for chunk in client.models.stream_generate_content( model="gemini-2.0-pro", contents="Write a poem about spring", ): print(chunk.text, end="", flush=True) At the same time, Google provides numerous reference materials to help you master cloud-based AI generation: Documentation. Official reference for all possible capabilities and functions of the Gemini API. GitHub Examples. Numerous examples of using the Gemini API in Go, JavaScript, Python, and Java. GitHub Cookbook. Practical materials explaining how to use the Gemini API with ready-made script examples. Thus, Gemini offers developers special conditions and tools for integrating the model into the logic of other applications. This is not surprising, since Google has one of the largest cloud infrastructures in the world. Conclusion The Gemini model stands out favorably from many other LLM neural networks, supporting working with multimodal data: text, code, images, and video. Google, with its rich ecosystem, seeks to integrate Gemini into all its services, adding flexibility to the classic user experience.
19 September 2025 · 14 min to read

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