Artificial intelligence (AI) is rapidly reshaping business, automating complex and tedious administrative tasks, gathering and analyzing data, generating product descriptions and marketing videos, and more.
Globally, an estimated 78% of companies utilize AI, with 71% having adopted generative AI in at least one business function.
Review AI use cases and discover how you can use advanced AI applications, including generative AI, in your business.
Understanding artificial intelligence in 2025
Artificial intelligence (AI) is a computer technology that enables computers and machines to perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, and generating words and images.
The past few years have seen significant expansion in AI capabilities, enabling sophisticated features such as advanced natural language processing (NLP), nuanced pattern recognition, and highly accurate machine vision (i.e., a computer’s ability to interpret and analyze visual information).
These advancements allow businesses to automate more complex tasks, gain deeper insights from data, and create personalized customer experiences.
Modern generative AI tools have become increasingly prevalent, using deep learning and advanced NLP to comprehend complex language inputs and generate remarkably natural-sounding, contextually relevant responses.
Key AI technologies driving business value
If you’re considering AI to improve efficiency and innovation in your business, these key technologies are particularly useful:
- Machine learning (ML): This involves developing AI algorithms that learn from data and improve their performance over time without explicit programming. ML algorithms identify patterns in training datasets and use these patterns to build predictive models.
- Deep learning: A specialized type of machine learning, deep learning relies on artificial neural networks—complex AI systems that organize and process data in a way inspired by the human brain’s structure. Deep learning models can extract more intricate information from vast and varied datasets than other types of ML models, often producing superior results with less need for human intervention.
- Natural language processing (NLP): NLP empowers computer systems to process, understand, interpret, and generate human language—both written and spoken. In 2025, NLP allows AI to produce outputs that convincingly mimic human-created content, from chatbots providing customer support to tools that summarize long documents or generate marketing copy.
- Computer vision: This field of AI helps machines “see” and interpret visual data, allowing AI tools to extract meaningful information from images and videos, or even generate new visual content. Applications range from quality control in manufacturing by identifying defects to facial recognition for security, and augmented reality experiences in retail, offering new ways for businesses to interact with the physical world and improve processes.
- Generative AI: A rapidly advancing subset of AI, generative AI focuses on creating new, original content, rather than just analyzing or acting on existing data. Using models often trained on vast datasets, it can produce text, images, audio, video, and even code. Research from McKinsey values gen AI’s long-term impact at $4.4 trillion of productivity growth annually across 63 use cases.
Top AI use cases transforming industries
Many AI-enabled tools, such as intelligent customer service chatbots and automated data entry systems, are useful across a broad range of industries, while other AI use cases are specific to particular fields.
Here’s how several major industries are leveraging AI technology:
- Retail and ecommerce
- Logistics and supply chain management
- Sales and marketing
- Finance and banking
- Manufacturing
- Human resources
Retail and ecommerce
AI can help ecommerce businesses by automating customer service, personalizing shopping experiences, and optimizing marketing efforts to drive sales and build loyalty.
Personalized customer experiences and recommendations
AI algorithms analyze customer data such as browsing history, purchasing patterns, wish lists, and real-time interactions to personalize product recommendations and deliver tailored content and customized offers.
This goal is making shopping more relevant and engaging for individual customers and increasing their brand loyalty and likelihood of conversion.
AI-powered chatbots and virtual assistants
AI-powered customer service tools, including chatbots and virtual assistants, can handle a wide range of customer inquiries 24/7. They can answer frequently asked questions, track orders, process returns, and even guide customers through the purchase process. Using them frees human agents to address complex issues and may improve overall customer satisfaction.
Advanced inventory management and demand forecasting
Retailers use AI for demand forecasting by leveraging it to analyzesales trends, seasonality, and the impact of promotions. If you own an ecommerce business, you can use various AI tools to optimize your stock levels, prevent overstocking or stockouts, and manage inventory across multiple channels.
Generative AI for product descriptions and marketing content
Generative AI tools can quickly create compelling and SEO-friendly product descriptions, email marketing copy, social media posts, and even ad copy and assets. Using generative AI can significantly accelerate the content creation process, allowing you to list products faster, run more campaigns, and maintain a consistent brand voice across all touchpoints.
Logistics and supply chain management
AI solutions can help you create a resilient and efficient logistics and supply chain operation, leading to reduced costs and improved delivery times.
Enhanced inventory management and demand forecasting
AI-poweredinventory management systems go beyond simple tracking. They analyze vast datasets, including historical sales, seasonality, economic indicators, and more to predict demand. You can use AI’s analytic capacity to optimize stock levels, minimize holding costs, and reduce the risk of stockouts, ensuring products are available when and where customers want them.
Intelligent order fulfillment and route optimization
AI can be used to streamline the entire order fulfillment process. It can automate tasks like warehouse sorting and packing through robotics and optimize delivery routes in real-time by considering traffic conditions, delivery windows, and vehicle capacity. This not only reduces shipping costs and fuel consumption but also enhances delivery speed and reliability.
Proactive supply chain optimization and risk management
AI tools can help you see your entire supply chain. By analyzing data from suppliers, manufacturers, and logistics partners, you can use AI to identify potential bottlenecks, predict disruptions (like port delays or supplier issues), and suggest alternative sourcing or routing strategies. This predictive analytics capability provides supply chain resilience by helping businesses mitigate risks, reduce operational costs, and adapt to changing market dynamics.
Sales and marketing
AI tools are revolutionizing how sales and marketing teams operate, enabling them to predict demand more accurately, increase operational efficiency, personalize customer interactions at scale, and ultimately drive revenue growth.
“Marketing and business operations are very deep subjects, and an AI system that has access to your business context and can surface insights to you and then help you design and execute solutions is a massive power-up,” says Alex Pilon, Shopify staff developer and AI advocate.
Advanced sales forecasting and lead management
Predictive analytics tools powered by AI analyze historical sales data, market trends, customer behavior, and CRM information to generate sales forecasts. These forecasts can help you optimize inventory, allocate resources, and set realistic targets. Forlead generation, AI can automatically identify and qualify promising leads by scoring them based on their likelihood to convert, and even personalize initial outreach, so sales reps can focus on the most valuable opportunities.
AI-driven marketing campaign optimization and automation
AI helps businesses analyze and optimize marketing campaigns by identifying which channels, messages, and creative approaches resonate with different audience segments. It can automate A/B testing, allocate budgets across platforms, and optimize the timing of campaign activities to maximize engagement and conversions.
Deep customer sentiment analysis
Natural language processing–driven AI tools can analyze customer reviews, social media comments, and survey responses, and support interactions to gauge brand sentiment. This can provide your business with actionable insights into customer satisfaction, pain points, and emerging trends, enabling you to address issues before they happen and refine your offerings.
AI tools for content creation and personalization at scale
Generative AI empowers marketing teams to create content—from blog post drafts and social media updates to email copy and product descriptions— quickly. AI also excels at personalizing content at scale, tailoring messages, offers, and experiences to individual customer preferences and behaviors with the hope of boosting engagement and conversion rates.
Finance and banking
Financial institutions leverage AI-powered solutions to combat fraud, ensure regulatory compliance, enhance data security, and deliver personalized services.
Sophisticated fraud detection and prevention
AI systems excel at identifying patterns and anomalies in transactional data. By learning normal customer behavior, AI can flag suspicious transactions, account takeovers, or new types of financial scams in real-time, allowing financial institutions to act quickly to prevent losses and protect customer accounts.
AI-driven algorithmic trading and investment strategies
AI algorithms can analyze vast amounts of market data, news feeds, and economic indicators at speeds that no human can match, identifying trading opportunities and executing trades. These systems can also develop and backtest complex investment strategies, aimingtooptimize returns and manage risk for investment firms and their clients.
Streamlined AI-driven regulatory compliance (RegTech)
The financial industry is heavily regulated, and AI tools (often called RegTech) help institutions stay compliant. AI can automatically review transactions, communications, business processes, and documents to ensure regulatory compliance, reducing manual effort.
Personalized financial advisory services
AI-powered platforms and robo-advisers can help you analyze your personal finances, goals, and risk tolerance to provide financial planning advice, investment recommendations, and budgeting assistance.
Manufacturing
In 2025, AI is integral to modern manufacturing, enabling predictive maintenance, automating quality control, optimizing supply chains, and powering the development of smart factories.
Predictive maintenance and enhanced quality control
AI applications use sensor data from machinery to predict when equipment might fail, allowing manufacturers to schedule maintenance proactively. This can minimize unplanned downtime, extend equipment life, and reduce repair costs. For quality control, computer vision tools can inspect products on the assembly line with incredible speed and accuracy, identifying defects or inconsistencies far more effectively than manual inspection.
Supply chain optimization and intelligent robotics
AI can optimize complex manufacturing supply chains by improving demand forecasting, managing inventory more efficiently, and coordinating logistics. Intelligent robots, powered by AI, perform a variety of tasks, from assembly and welding to packaging and material handling, increasing production speed, consistency, and worker safety.
AI in smart factories and digital twins
AI is a core component of smart factories, where interconnected systems and data analytics drive autonomous decision-making and process optimization. Digital twins—virtual replicas of physical assets or processes—use AI to simulate performance, test changes, and predict outcomes without impacting real-world operations. This facilitates continuous improvement, innovation in product design, and efficient resource utilization.
Human resources
AI tools increase efficiency for the field of human resources. This can impact talent management, enable data-driven decision-making, and personalize employee experiences.
AI in talent acquisition and recruitment
AI systems can automate many time-consuming recruitment tasks, such as screening résumés, identifying qualified candidates from large applicant pools, and even conducting initial candidate engagement through chatbots. This allows HR professionals to focus on strategic aspects of hiring, such as interviewing and candidate experience.
Personalized employee training and development
AI tools can analyze employee performance data, identify skills gaps, and recommend personalized training modules or learning paths tailored to individual career goals and developmental needs. Workforce analytics and performance management
AI can analyze workforce data to identify trends in employee engagement, productivity, and retention. These insights can help HR and leadership make informed decisions about talent management strategies, workplace policies, and initiatives for improving employee satisfaction and performance. AI can also assist in providing objective and continuous performance feedback.
Emerging AI trends and future outlook
As adoption of this new technology becomes commonplace, it’s important to understand the opportunities—and risks—that lie ahead. AI
The rise of generative AI and large language models (LLMs)
Generative AI, powered by increasingly sophisticated large language models (LLMs) and other foundation models, has moved from a niche technology to a mainstream business tool. Its ability to create high-quality text, images, code, and other content is transforming roles and industries. In 2025 and beyond, expect to see even more powerful and accessible generative AI tools integrated into everyday software, enabling wider adoption for tasks ranging from content creation and software development to customer service and scientific research. The key impact for businesses is the potential for massive productivity gains and the creation of entirely new products and services.
Ethical considerations and responsible AI development
With the increasing power and pervasiveness of AI, there’s a growing focus on its ethical implications and the need for responsible development and deployment. Key concerns include algorithmic bias, data privacy, transparency in AI decision-making, job displacement, and the potential misuse of AI technologies. When copy or text is generated, copyrighted text and plagiarism can be an issue. Businesses and policymakers are increasingly recognizing the importance of establishing ethical guidelines and regulatory frameworks.
The convergence of AI with other technologies
AI’s impact is amplified when it converges with other transformative technologies. For example, combining AI and the Internet of Things (IoT) enables smarter devices and more autonomous systems since real-time data from sensors can be analyzed for intelligent decision-making. AI paired with blockchain can enhance security, transparency, and efficiency in areas like supply chain management and financial transactions.
AI use cases FAQ
What is the most common use for AI across industries?
Although it has numerous applications, two of the most common reasons businesses use AI are to automate routine and repetitive tasks and to analyze large datasets.
What are the primary advantages of adopting AI in business?
AI tools can increase efficiency and productivity by automating manual processes. Using AI can reduce operational costs and support decision-making with data-driven insights. It also opens up new opportunities for innovation and revenue generation, while helping business leaders manage risks and adapt to changing market conditions.
What are some potential challenges when implementing AI?
As an emerging technology, AI doesn’t always understand the assignment, and learning to prompt it effectively is a crucial skill. You’ll need access to high-quality, reliable data, and you must be prepared to invest in both the technology itself and the talent required to manage it effectively. Integrating AI with your existing systems can be complex, and you’ll need to focus on change management to ensure your team adopts new tools effectively. Ethical and regulatory concerns—such as data privacy, algorithmic bias, and copyright and regulatory compliance—are increasingly important, and you’ll want to establish clear ways to measure your return on investment to ensure your AI initiatives deliver real value.
