The most valuable AI use cases for business

AI is being applied to an expanding range of business challenges and use cases. Across business functions and industries, teams are using AI to improve customer experiences, increase productivity, manage operations and improve decision-making. While early AI initiatives often focused on automating routine tasks, today’s applications are helping businesses address more complex challenges and uncover new opportunities for growth.

The most successful AI initiatives tie to specific business objectives and measurable outcomes. As capabilities continue to mature, the range of practical AI business applications will expand. The following examples highlight valuable ways organizations are using AI to improve outcomes.

AI improves customer experience by delivering more seamless and personalized interactions across the customer journey. AI systems analyze large volumes of customer data to help businesses better understand customer preferences, anticipate needs and tailor experiences in real time.

AI enables personalized recommendations that help businesses deliver relevant products, services and content. By integrating data across online, mobile, in-store and social channels, organizations deliver more consistent omnichannel customer experiences. This connectivity helps customers move seamlessly between touchpoints.

Sentiment analysis evaluates customer feedback and identifies opportunities to improve satisfaction and loyalty. Many organizations also incorporate AI into customer relationship management (CRM) systems to automate routine tasks, improve decision-making and strengthen customer engagement.

Customer service remains one of the most common and valuable AI applications. Companies use AI to answer customer questions, resolve common issues and provide support across digital channels including websites, mobile apps, messaging platforms and contact centers. In fact, mature AI adopters (organizations operating or optimizing AI into their customer service functions) reported a 17% increase in customer satisfaction.¹

Modern AI systems can understand natural language, access knowledge bases and guide customers through tasks that once required human assistance. Many businesses use AI-powered customer service chatbots, virtual agents and conversational AI systems built on large language models (LLMs) to provide 24/7 support.

Contact center automation and tools help customer service teams work more efficiently. Human agents use AI-powered agent assist tools to summarize conversations, retrieve relevant information, suggest responses and automate post-call documentation. These capabilities reduce administrative work and allow human agents to spend more time addressing complex customer needs.

When deployed effectively, AI helps organizations improve response times, reduce support costs and create more consistent customer experiences across channels.

Customer data has long informed business decisions. AI allows organizations to take personalization much further by tailoring interactions, recommendations and content to individual users in real time.

Organizations use AI-driven personalization to deliver more relevant recommendations and experiences. Recommendation engines analyze purchasing history, browsing activity, product usage and other signals to identify relevant products, services and offers.

Personalization platforms can adapt content, recommendations and offers during customer interactions rather than relying only on predefined audience segments. AI can also help sales teams identify opportunities for cross-selling and upselling by highlighting products or services that complement existing customer purchases.

Retailers, streaming platforms, financial institutions and healthcare providers are just some of the organizations that use AI-powered personalization to better understand customer needs and deliver more relevant experiences. Effective personalization can improve customer satisfaction while increasing engagement, loyalty and conversion rates.

AI is increasingly embedded within the devices people use every day. Smartphones, smart speakers, wearable devices, connected vehicles and home automation systems all rely on AI to improve functions and create more intuitive user experiences.

Product capabilities such as voice interaction, speech and language translation, image recognition, navigation, health monitoring and personalized recommendations are powered by AI. Many items incorporate on-device AI, which allows them to process information locally rather than relying entirely on cloud services.

For businesses, AI enables the development of smarter products and services. Manufacturers can use AI to improve device performance, adapt functions to individual users and deliver value to customers.

AI agents are AI systems that can operate with a degree of autonomy, or agency. Because of this, AI agents are rapidly transforming how organizations gather, analyze and act on information. They can be trained to analyze information, reason over it and act based on context and objectives. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.²

Agentic AI systems combine LLMs, machine learning and other AI technologies to perform complex tasks. Unlike self-contained LLMs that mostly generate responses, AI agents can connect to external tools and data sources, retrieve real-time information and carry out actions. They can plan multi-step workflows, adapt to changing conditions and learn from past interactions.

Organizations are beginning to deploy AI agents across a wide range of business functions:

• Customer service organizations use AI agents to resolve routine support issues and manage customer interactions. Research by the National Bureau of Economic Research (NBER) shows that when customer support professionals were given access to AI agents, their productivity increased by an average of 14%.³
• Financial institutions use AI agents to support research, compliance reviews and fraud detection.
• IT teams use them to automate service requests and system administration tasks.
• Software development teams use AI agents to generate code, test applications and identify bugs.
• Supply chain teams use AI agents to monitor inventory, coordinate logistics and respond to disruptions.

Organizations are using AI agents for complex workflows that previously required significant human involvement. Their ability to combine reasoning, automation and real-time access to information makes them one of the fastest-growing areas of AI adoption.

AI-powered assistants help people manage tasks, access information and work more efficiently. What began as simple voice assistants has evolved into a broad category of AI tools that support both consumers and employees.

Consumers use AI assistants to manage calendars, control connected devices and access content. Organizations use them to help employees summarize documents, draft communications, schedule meetings, answer questions and locate information. Examples such as Microsoft and ChatGPT Enterprise can be embedded in customer relationship management (CRM), productivity and collaboration platforms. Many of these assistants combine LLMs with enterprise search systems, allowing employees to access information from internal documents, knowledge bases and business applications through a single interface.

AI assistants support routing, information retrieval and other time-consuming tasks. Instead of navigating multiple applications or searching through large amounts of information, users can quickly receive answers from systems that process natural language queries.

Project teams use AI assistants to support project management. AI can summarize meetings, track action items, monitor project status and identify potential issues. Some AI platforms can also help managers prioritize work, allocate resources and automatically generate project updates.

Many software platforms now include built-in AI assistants that help users perform tasks without leaving the applications they’re using. They’ve become a productivity tool across a wide range of industries and functions.

Computer vision enables AI systems to interpret and analyze images, video and other visual information. It can process large amounts of visual information quickly and consistently. Tasks that might require significant manual review can often be completed in real time. Automating these tasks helps organizations improve efficiency, increase accuracy and operate at greater scale. Computer vision offers various uses across industries and functions:

• Healthcare providers use it to assist with medical imaging analysis
  
• Logistics and transportation companies use it to monitor facilities, track inventory and improve workplace safety 
  
• Manufacturers use it to inspect products for defects and monitor production quality
  
• Retailers use visual recognition technology to improve inventory management and better understand customer behavior
  
• Security teams use it to monitor facilities and identify potential threats
  
  As cameras, sensors and connected devices become more common, computer vision is helping organizations gain greater visibility into their operations while reducing the need for manual inspection and monitoring.

Organizations collect more data than ever before. Still, extracting meaningful insights is a challenge. AI helps businesses identify patterns, relationships and opportunities that might otherwise go unnoticed to support more informed decisions.

AI analytics often depend on effective data ingestion and integration to bring together information from CRM systems, enterprise resource planning (ERP) platforms, operational databases and other sources. This integration helps create a more complete view of the business.

AI can analyze both structured and unstructured data, including emails, documents, customer feedback, images and other information sources that were historically difficult to analyze at scale. Traditional analytics often focuses on reporting what happened; AI can help explain why, predict what might happen next and recommend actions. Businesses use these capabilities for forecasting, anomaly detection, customer analysis, demand planning, performance metrics and risk assessment.

Organizations generate and manage large volumes of documents and records. AI helps automate the extraction, analysis and routing of information from both structured and unstructured documents, helping organizations streamline key business processes.

Modern document processing systems combine technologies such as optical character recognition (OCR), natural language processing (NLP) and generative AI to understand contracts, invoices, purchase orders, claims forms and other business documents. AI can extract key information, classify documents, identify exceptions and route work to the appropriate employees or systems.

Organizations also use AI to automate business workflows that traditionally required significant manual review. Common applications include invoice processing, contract analysis, procurement workflows, employee onboarding, compliance documentation and insurance claims processing. AI agents and intelligent automation can further streamline these processes by coordinating actions across multiple systems and helping employees complete tasks more efficiently.

Generative AI has become one of the most common business applications of artificial intelligence. Unlike traditional AI systems that primarily analyze data or make predictions, generative AI can create new content including text, images, video, audio and software code.

The ability to produce original content from simple prompts creates new opportunities for organizations, but it also raises questions around copyright, authenticity and responsible use.

Most generative AI applications are powered by foundation models such as LLMs, which can generate and analyze content based on natural language prompts.

Many organizations first adopted generative AI to improve productivity and content creation. Marketing teams use it to draft articles, advertising copy and campaign materials. Designers use it to generate concepts, illustrations and visual assets. Video production teams use AI to create or edit content more quickly, while software developers use it to write code, generate documentation and support testing activities.

Generative AI is being used to summarize reports, analyze documents, assist with research and help employees work more efficiently with large volumes of information. Many software platforms now embed generative AI directly into everyday workflows. This inclusion allows users to interact with applications through natural language and automate routine tasks.

Human resources teams use AI to improve recruiting and talent acquisition, employee support, workforce planning and talent development. Automating administrative tasks allows HR professionals to put more focus on strategy and employee engagement.

Recruiting is a widely adopted AI use case. AI can reduce hiring timelines and help organizations manage large applicant pools. It can help identify qualified candidates, screen applications and match skills to job requirements. HR-focused chatbots improve communication throughout the hiring process. Still, many organizations maintain human oversight to help address concerns around fairness, transparency and compliance.

For employee development and workforce planning, AI can identify skill gaps, personalize training opportunities and help employees navigate career development options. HR teams can use workforce analytics to better understand staffing needs, retention risks and trends within the organization.

For change management, AI can analyze employee feedback, identify adoption challenges and recommend training during business transformations. These insights help leaders understand workforce readiness and support employees as new technologies and processes are introduced.

AI improves operational visibility, productivity and cost efficiency. It analyzes operational data in real time to identify bottlenecks and help organizations make more effective use of people, equipment and resources.

AI is increasingly used to automate routine operational tasks, monitor performance and optimize scheduling. Organizations can use predictive analytics to anticipate demand, effectively allocate resources and quickly respond to changing conditions. AI-powered assistants and agents also help employees manage workflows, coordinate activities and complete operational tasks more efficiently.

Organizations use AI to improve visibility and efficiency across supply chains. AI systems analyze data from suppliers, logistics providers, warehouses and operational systems to help businesses better anticipate disruptions and quickly respond to changing conditions.

AI supports demand forecasting, inventory optimization and logistics planning. Organizations can use predictive analytics to identify potential shortages and optimize transportation routes. Computer vision, robotics and AI agents are used in warehouses and distribution centers to automate inventory tracking and fulfillment.

AI is also helping procurement teams examine supplier performance, pricing trends and purchasing data to support sourcing decisions and improve efficiency.

Modern AIOps (artificial intelligence for IT operations) platforms use machine learning, predictive analytics and generative AI to shift IT operations from reactive troubleshooting to proactive management.

AIOps platforms continuously monitor complex IT environments, including hybrid cloud infrastructure. Rather than relying on manual thresholds, AIOps uses real-time anomaly detection to quickly pinpoint performance issues.

AI assistants, or copilots, for IT operations allow engineering teams to interact with complex system data using natural language. This accelerates root cause analysis and can automate parts of incident remediation and software delivery workflows. These capabilities can help organizations increase the value of their digital transformation investments.

AI helps organizations optimize application performance by continuously monitoring workloads, resource usage and system behavior. In complex environments, IT teams often lack the time to continually adjust computing, storage and database resources as demand changes.

AI can automatically adjust resource allocation based on real-time demand. This helps ensure that enterprise applications perform consistently and reliably under fluctuating traffic conditions.

Cybersecurity and security operations center (SOC) teams use AI to detect suspicious activity, identify anomalies, uncover fraud and respond to threats in near real time. Because modern enterprise networks generate massive volumes of network logs, application data and user activity records, manual review is no longer scalable.

A recent IBM Institute for Business Value (IBV) study found that 58% of leading CEOs expect AI to have a transformative impact on the enhancement of security and risk management.⁴ AI solutions help security teams efficiently detect threats, identify phishing attempts and manage vulnerabilities. By proactively mitigating risks, AI helps security teams contain threats early and reduce the impact of security incidents.

AI helps organizations maintain business continuity by automating failover processes, supporting disaster recovery and identifying risks before they cause outages.

AI-powered resilience platforms combine data from across the IT environment to provide a unified view of system health. By combining the predictive monitoring of software systems with automated backup and recovery, organizations can identify and address potential outages before they affect users.

Predictive maintenance remains a high-value industrial use case for AI, evolving from schedule-based maintenance into equipment health analysis. Machine learning AI models can identify subtle patterns that signal impending mechanical issues by continuously analyzing real-time data streams from IoT (Internet of Things) sensors and equipment logs.

By predicting equipment failures before they occur, organizations can avoid fixed maintenance schedules and perform maintenance when it’s needed. Some organizations also use AI-powered digital twins, which are virtual representations of physical assets or systems, to simulate operating conditions, predict failures and evaluate maintenance strategies before real-world changes are made.

AI allows robots to perform tasks that require perception, navigation and decision-making. In manufacturing and industrial settings, organizations increasingly deploy collaborative robots—or “cobots”—that safely work alongside human employees to streamline assembly, quality inspections and materials handling.

Similarly, distribution centers rely on autonomous mobile robots (AMRs) to optimize warehouse automation, navigate floor plans by using computer vision and speed up order fulfillment. AI-powered robotics is also being adopted across many industries: Agricultural robots use AI for autonomous pruning, seeding and targeted crop spraying. Surgical robotics can help physicians perform certain procedures with greater precision and control.

Generative AI and AI-powered coding assistants have transformed enterprise software development. These tools integrate directly into the developer workflow and support end-to-end development activities. They can generate code from natural language prompts, conduct automated code reviews, generate tests, create documentation and identify bugs.

These capabilities are especially valuable for legacy application modernization at scale. Developers can use generative AI to efficiently migrate legacy codebases to modern architectures, programming languages and app frameworks. Successful migration improves code consistency, reduces manual errors and speeds time-to-market for cloud-native applications.

AI is transforming nearly every industry by helping organizations automate processes, improve decision-making and uncover new opportunities for growth. Applications vary by sector, but most organizations use AI to increase efficiency, reduce operational costs, use data more effectively and improve customer experiences.

Automotive manufacturers use AI throughout the product lifecycle, from design and engineering to production, sales and after-sales support. AI helps manufacturers forecast demand, optimize supply chains and improve production planning. Computer vision systems inspect vehicles and components for defects, while robotics automate tasks like assembly and materials handling. These technologies support the more efficient and consistent production of high-quality vehicles.

AI also plays a central role in connected and autonomous vehicle technologies. Manufacturers use AI to support advanced driver-assistance systems, improve vehicle diagnostics and analyze data collected from connected vehicles to improve safety, performance and maintenance.

Banks, financial institutions and fintech companies use AI to improve customer service, strengthen fraud detection and support faster decision-making.

Generative AI in financial services powers chatbots and virtual assistants that provide customers with more personalized support and financial information. It can extract key information and summarize large volumes of documents. AI systems also analyze transaction data to identify suspicious activity, detect anomalies and help prevent financial crime. Sometimes they are used to write and optimize code for trading-focused AI algorithms.

Beyond generative AI, banks and fintech providers use AI to optimize operations, automate compliance tasks and improve efficiency across back-office processes.

Educational institutions are using AI to create more personalized learning experiences and improve administrative efficiency. AI-powered learning platforms can adapt content and pacing based on a student’s progress, which helps educators provide more targeted support.

Teachers and administrators use AI to analyze student performance and identify learning gaps. For diverse learner populations, AI-powered translation, transcription and accessibility tools help make educational content more accessible.

Energy companies use AI to optimize power generation, improve demand forecasting and support more efficient power grid management. Utilities can analyze data from smart meters, sensors and connected infrastructure to better understand consumption patterns and balance supply and demand.

AI is also helping organizations integrate renewable energy sources, monitor equipment and predict maintenance issues. In customer-facing applications, AI assistants can help consumers better understand their energy usage and offer opportunities to reduce consumption.

Governments are exploring AI to improve public services, gain insights from large volumes of data, enhance operational efficiency and strengthen economic competitiveness. As AI becomes a strategic technology, governments around the world are investing in research, establishing governance frameworks and collaborating with private industry, academia and international organizations to help ensure its responsible development and deployment.

Government agencies are applying AI across a wide range of functions. Examples include citizen services, cybersecurity, healthcare, disaster preparedness, law enforcement, procurement and the modernization of legacy IT systems. AI technologies help agencies automate repetitive tasks, improve decision-making and analyze complex datasets. AI-powered virtual assistants can deliver faster, more personalized services to citizens, while predictive analytics can support emergency response.

When using AI, governments must address risks related to bias, discrimination, privacy, cybersecurity, data protection and equitable access to services. Establishing clear governance, supporting regulatory compliance, promoting transparency and adopting trustworthy AI practices are essential to balancing innovation with public trust.

AI is helping improve healthcare and medicine through clinical decision-making, streamlining administrative tasks and enhancing patient care. Healthcare organizations use AI-powered tools to automate documentation, summarize clinical notes, manage records and support scheduling. These efficiencies help healthcare professionals spend more time with patients. AI can also assist with remote patient monitoring, virtual nursing and personalized treatment.

In medicine, AI plays an important role in diagnostics and research. Computer vision technologies help analyze medical images such as X-rays and CT scans to identify potential abnormalities. AI-powered systems can help reduce medication errors and support safer, less invasive surgical procedures.

AI is also accelerating medical research and drug discovery. AI helps researchers analyze large volumes of scientific, clinical and genetic data to identify potential treatments, improve clinical trials and advance precision medicine.

With AI, insurance providers can automate claims processing, improve underwriting and strengthen fraud detection. AI systems can review documents, analyze claims data and identify potential risks faster than manual processes.

Many insurers also use AI-powered chatbots and virtual assistants to improve customer service. Predictive analytics helps insurers better assess risk, set premiums and identify emerging trends that might affect future claims.

Manufacturers use AI to improve efficiency, product quality and supply chain performance. Computer vision systems inspect products for defects, while predictive maintenance solutions monitor equipment sensors and analyze data to identify potential failures before they happen.

Generative AI is also being used to accelerate product design and engineering by helping teams quickly explore multiple design ideas. On the factory floor, AI supports production optimization, inventory management and process automation to help reduce waste and improve operational efficiency.

Marketing agencies and teams use AI to create content, analyze customer behavior and improve marketing campaign performance. Generative AI helps marketers efficiently produce articles, emails, advertisements, social media content and creative assets. Analytics tools help identify audience trends and measure engagement.

AI also supports customer segmentation, personalization and campaign optimization. Marketing teams can use AI to identify target audiences, recommend messaging and determine the channels most likely to drive engagement. Predictive analytics helps forecast campaign performance, while AI-powered automation tools can manage and optimize marketing activities across multiple channels.

As organizations collect more customer data, AI is becoming an important tool for improving marketing effectiveness, increasing conversion rates and delivering more relevant customer experiences.

In the retail industry, AI is improving customer experiences, personalizing marketing and optimizing operations. Recommendation engines analyze customer behavior to suggest relevant products, offers and price promotions. AI-powered assistants help customers find information and receive support.

Retailers also use AI for demand forecasting, inventory optimization and supply chain management. Generative AI is used to create product descriptions, customer communications and marketing content. These capabilities help retailers improve efficiency and deliver more personalized shopping experiences.

AI in e-commerce improves conversion rates and operational efficiency. E-commerce automation targets merchandising, product recommendations and customer support processes. Organizations use AI to optimize search results, personalize product listings, generate product descriptions and recover abandoned shopping carts. AI can also automate pricing decisions, inventory updates and promotional campaigns.

AI in sales automates routine tasks and provides actionable insights, giving sales professionals more time to build relationships and close deals.

AI improves sales prospecting by helping teams identify and prioritize high-quality leads, personalize outreach and engage prospects more effectively. AI-powered assistants can analyze customer behavior, qualify leads and provide sales representatives with relevant insights before meetings.

AI also optimizes sales communications by recommending messaging, timing and target audiences based on past interactions. In addition, it helps organizations improve conversion rates by identifying where prospects drop out of the sales funnel and recommending corrective actions. Predictive forecasting enables teams to focus resources on the opportunities most likely to drive revenue. On average, sales executives who use AI for lead generation and lead scoring forecast 25% higher revenue growth.⁵

AI also enhances sales enablement by delivering timely recommendations, content and insights tailored to each buyer’s industry, priorities and stage in the purchasing journey. Sales teams use AI assistants to summarize meetings, draft follow-up communications, prepare account research and automatically update CRM systems. Predictive analytics help forecast pipeline performance, improve planning and identify potential risks.

AI is helping telecommunications companies improve network performance, streamline operations and deliver better customer experiences. AI can analyze vast amounts of customer and network data to identify usage patterns and predict potential issues. These capabilities help operators improve reliability and better understand customer behavior, including identifying customers who are considering switching providers.

AI plays a critical role in network management and maintenance. It is used to monitor network operations in real time, detect anomalies and automate tasks such as traffic routing and capacity planning. Predictive maintenance tools identify potential hardware or software failures before they cause disruptions, which helps improve operational efficiency, lower costs and enhance network performance.

In telecommunication sales, marketing and customer service, AI analyzes customer preferences and engagement patterns. It can help deliver more personalized offers and optimize customer journeys. AI-powered virtual assistants and chatbots also provide faster support, resolve common issues and assist service representatives during customer interactions.

Transportation and logistics organizations use AI to optimize routes, manage fleets and monitor supply chains. AI systems analyze traffic patterns, weather conditions and operational data to improve scheduling and reduce delays.

AI supports warehouse automation, inventory tracking and vehicle safety systems. Logistics providers also use predictive analytics to forecast demand, improve capacity planning and identify potential disruptions. AI-supported autonomous vehicles are also expected to play a larger role in transportation systems.

The most successful AI initiatives are typically focused on solving specific business problems rather than adopting AI without a clear business objective. Whether the goal is improving customer service, accelerating software development, optimizing supply chains or supporting employees with AI assistants, organizations are finding practical ways to create measurable business value and ROI (return on investment).

AI capabilities will continue to evolve and new opportunities will emerge across nearly every business function and industry. Organizations that take the time to identify high-value use cases, build the right foundation and adopt AI responsibly will be best positioned to realize its long-term benefits.