Listing the top AI use cases for businesses is difficult. After all, as AI advances, it’s easier to identify what the top models and algorithms can’t do. If you can think of a task that doesn’t require certain types of manual labor, empathy, or emotional intelligence, AI can probably complete it.
It’s little wonder that generative AI alone is set to be worth around $1.3 trillion by 2032.
The latest AI tools can even simulate a level of creativity previously considered only possible for humans. Of course, like any tool or technology, there are areas where AI excels. Defining the best use cases for AI is how you ensure you’re investing in the right systems for your business.
Here’s your guide to some of the most significant AI use cases worth exploring.
The Top Customer-Facing AI Use Cases
Many of the top AI use cases are linked to customer service. After all, contact centers have been using chatbots, virtual assistants, and AI-powered IVR solutions for years. Plus, studies show that 80% of customers interacting with AI software for support have had a positive experience.
More than just improving CX, AI in customer service improves business efficiency and team productivity. The top customer-facing AI use cases include:
Enhanced Customer Service
Various types of AI solutions can improve the customer service experience. With conversational AI tools, companies can leverage natural language processing (NLP), and speech recognition to guide customers through IVR menus, or deliver intelligent self-service experiences over the phone.
Sentiment analysis can help businesses tap into useful insights, and machine learning can help AI systems adapt to different customer needs in real-time. With NLP and text-to-speech capabilities, AI systems can respond to queries and questions on various text-based channels, from websites, to social media, and messaging apps.
Generative AI tools can even help companies create FAQ systems, onboarding guides, videos, and documents that help guide customers through various processes and tasks. Plus, virtual agents can provide step-by-step coaching and guidance to agents in real time.
Improved Personalization and Selling Opportunities
One of the most powerful AI use cases in the customer-facing landscape is the ability to use AI to create personalized interactions at scale. Digital assistants and chatbots can gather data from customer relationship management tools and other systems to deliver tailored experiences to each customer.
Based on previous purchases and interactions, AI systems can even predict what customers might want. For instance, Amazon and Netflix suggest products to consumers based on AI algorithms. Some tools can automatically remind customers to renew subscriptions or reorder products they might be running low on.
This personalized experience can extend into the sales landscape, where recommendation engines can discover data trends to deliver more effective upselling and cross-selling strategies. This gives companies a unique opportunity to improve average order values and customer lifetime values.
Personal Assistants for Customers
Personal AI assistants are everywhere in today’s world. They’re built into our smartphones, intelligent speakers, and even everyday household goods. By 2033, the market for this type of AI technology is expected to be worth $166.97 billion.
Virtual assistants, powered by machine learning, natural language processing, and other AI algorithms, help customers find answers to questions, track down products, and complete tasks. Tools like Amazon Alexa and Siri can place online orders or adjust lighting system settings on demand.
AI assistants in the workplace can surface information for team members, transcribe and translate text, and summarize conversations. Microsoft Copilot and Google Gemini are excellent examples. Some AI assistants even use computer vision to detect objects or people in an image and predict what lies beyond the borders of a photograph.
The Best Creative AI Use Cases
Thanks to the rise of Large Language Models and Generative AI, intelligent systems are more creative than ever. Google has already published countless examples of companies using generative AI solutions like Gemini for creative purposes.
Creating Content with Generative AI
Generative AI is the ultimate tool for content creation. Solutions like ChatGPT and MidJourney empower business users and individuals alike to create novel and unique content for all kinds of purposes. The latest tools can produce high-quality text for blogs, sales documents, websites, social media campaigns, and advertisements.
They can create scripts for video campaigns and even produce images and videos based on textual input. Some multi-modal generative AI systems can also create unique audio and songs. Although these tools still lack some of the creativity of human beings, they can accelerate and streamline content production on a massive scale.
Automating Coding and App Modernization
Powerful large language models are exceptionally effective at generating code for development purposes. Leading companies are increasingly using generative models to generate software code at speed and customize new applications.
More than just producing initial coding frameworks, generative AI solutions can help companies edit code, translate it into different languages, and migrate applications into new environments. Plus, IT and development teams can use artificial intelligence to analyze code performance and discover new ways to enhance the functionality of different applications.
Unlocking Insights
Expert AI systems can be trained on a corpus of data to emulate human decision-making capabilities. Once they have those abilities, these systems can solve complex problems and surface valuable insights for business leaders in various industries.
Intelligent tools can help businesses understand patterns and trends in customer behavior, predict future events, and understand why past events took place. Companies can even use generative AI and LLM solutions to create synthetic data, which can help them train future AI models on more diverse data points, reducing the risk of AI bias and hallucinations.
The Top Technical and Operational AI Use Cases
Beyond helping serve customers and generate amazing assets, data, and content, AI solutions can assist businesses with various tasks and processes. Currently, more than 77% of companies are using AI in their operations, and 83% see it as a top priority for the future.
Enhancing Process Automation
Process automation is one of the top AI use cases in any industry. Artificial intelligence has proven to be a valuable solution for automating time-consuming and repetitive tasks – particularly those prone to human error. Organizations can free team members to focus on more complex tasks by automating processes like data entry or transcription.
Automating common tasks helps businesses reduce operational costs and minimize the risk of errors. It can also improve employee engagement and productivity, ensuring team members can focus on more creative and strategic work.
Today’s AI tools can even automate a range of more complex tasks, from screening job seekers’ applications to qualifying leads and segmenting customers.
Optimizing IT Operations
There are numerous benefits involved in using artificial intelligence for IT operations. Infusing intelligence into IT operations allows companies to harness all of the benefits of NLP, machine learning models, and big data to enhance operational workflows.
The rise of AIOps in the IT landscape has helped countless companies improve the return on investment from their digital transformation investments. It can assist organizations in optimizing spending strategies and configuring new technologies for success.
Plus, in the IT landscape, AI enhances helpdesk support, optimizes cybersecurity and threat detection processes, improves data analysis and business intelligence processes, and streamlines software development and testing.
Improving System Resilience and Maintenance Strategies
Many leading organizations are using real-time root cause analysis tools powered by intelligent automation and artificial intelligence to deliver uninterrupted and consistent services to customers. AI solutions can help IT teams identify the reasons behind incidents, reducing the time it takes to repair and mitigate issues.
AI solutions can also help companies reduce the risk of future issues by enabling predictive maintenance. Companies like GE use predictive maintenance to identify potential emerging problems in systems and implement automatic fixes before they evolve.
Rolls-Royce has even used AI in predictive maintenance to help improve the efficiency of jet engines and minimize the amount of carbon their planes produce.
Cybersecurity AI Use Cases
Although the rise of new AI algorithms presents new challenges in cybersecurity, such as the risk of deepfakes and enhanced malware attacks, AI can also improve cybersecurity. Worldwide, companies are using AI to enable biometric authentication strategies that help minimize the risk of data breaches and fraud.
AI-powered systems are also excellent at monitoring vast amounts of real-time data gathered from networks, computer systems, and more to detect suspicious activity instantly. Some companies even use reinforcement learning to train models to identify and respond to new cyberattacks.
Enabling Robotics
At the moment, AI use cases are still partially restricted by the fact that most AI systems can’t perform manual labor. However, many evolving AI algorithms have the potential to power robotic or mechanical systems that can replicate human tasks.
In industrial environments, narrow AI solutions can perform repetitive and routine tasks with exceptional efficiency and speed. They can assemble products, package items for delivery, and conduct quality inspections. In other industries, like healthcare, AI can help surgeons monitor vitals and detect problems during procedures.
Agricultural machines can automate seeding, spraying, and pruning tasks, while smart home devices, like AI vacuum cleaners, can navigate a home’s environment using computer vision and complete cleaning processes.
Recruiting, Hiring, and HR Practices
Although there are risks to relying too heavily on AI during the recruiting process, as evidenced by Amazon’s early mishaps, AI can still improve HR processes in various ways. It can help businesses screen resumes to find candidates with the right skills and credentials.
AI solutions can also help businesses schedule interviews with potential employees and score their answers to questions using pre-defined algorithms. They can also identify team members suitable for a promotion or leadership position.
AI can even match employees with potential new roles and opportunities based on their skill sets or identify which team members need additional training. Plus, AI assistants can deliver real-time coaching and support to staff during onboarding.
Financial Reporting and Forecasting
Many of the top AI use cases involve AI’s ability to process data and assist businesses in making intelligent decisions. Companies like Intuit are already using artificial intelligence to help with financial planning and cash flow optimization.
However, AI can provide assistance with more than just financial insights. It can help businesses forecast changes in customer demand and purchasing trends so they can manage their inventory more effectively.
AI systems can analyze historical data and real-time insights to determine which products are most likely to deliver the best return on investment for companies expanding their portfolios too.
The Top Industry AI Use Cases
As you can probably see from the versatile AI use cases we’ve covered, artificial intelligence can deliver exceptional business results in virtually any industry. Here are more specific insights into what AI can do for different business sectors.
Automotive AI Use Cases
With AI, automotive manufacturers have a unique opportunity to efficiently predict their customers’ needs and adjust production to evolutions in supply and demand. AI tools can help automotive companies automate workflows, streamline vehicle prototyping processes, and optimize the manufacturing journey with IoT and robotics.
AI can also help organizations to make vehicles more efficient and effective. Intelligent systems can power enhanced navigation and intelligent traffic management tools in vehicles. They can assist with route optimization for drivers, and track potential risks on the road in real-time. AI is even paving the way for the future of autonomous cars.
Education Opportunities
In the education landscape, AI is driving us towards a future of more personalized, immersive learning experiences. AI solutions can help teachers tailor materials to the needs of each individual student and even deliver educational content and feedback to students on demand.
In the education space, AI can help teachers and trainers analyze their students’ work to identify where they might need extra help and support. It can spot instances of plagiarized content during reviews and even automate aspects of the grading process. Some companies even invest in developing dedicated AI tutors for remote and distance learners.
Energy AI Use Cases
While concerns about the carbon footprint of advanced AI tools are well-founded, AI could also help us achieve a more sustainable and efficient future. In the energy sector, companies use AI to improve their cost competitiveness, leveraging data analytics for energy conservation, renewable optimization, and smart grid management.
Organizations can help companies deliver better customer support by providing real-time insights into energy usage rates and metrics. They can also help companies and consumers alike better understand their consumption rates, giving them the guidance they need to reduce their carbon footprint.
Financial Services and Insurance
Countless AI use cases have emerged in the financial sector, influencing insurance, banking, investment, and loan companies. AI-powered FinOps solutions allow financial institutions to maximize customer service and reduce risks in their industry.
AI platforms can use deep learning and machine learning algorithms to spot suspicious transactions, reducing the risk of fraud. Companies can use AI for credit scoring and risk assessment processes and even to deliver personalized investment recommendations to consumers. Plus, AI tools can help businesses remain compliant with industry standards, enabling real-time monitoring and tracking of data and potential risks.
Pharmaceuticals and Healthcare
Healthcare is another industry with many impactful AI use cases. Currently, many healthcare organizations are using intelligent automation and natural language processing to improve data analysis, diagnosis, patient care, and treatment. In healthcare settings, machine learning can improve imaging processes to help professionals detect potential health anomalies faster.
In genetic research, machine learning can provide useful insights into how different genes influence our health and the development of diseases. AI can even help organizations develop personalized treatment plans based on historical data. Plus, in the life sciences industry and pharmaceuticals, AI can assist companies in developing new drugs and medicines for various conditions.
Manufacturing AI Use Cases
Advanced artificial intelligence solutions are extremely powerful in the manufacturing landscape. With AI tools, companies can access predictive insights into market trends and optimize product design processes. They can use AI to find ways of boosting energy efficiency and reducing carbon footprints and leverage automated systems to detect faults in real-time.
AI built into robotic systems can be integrated into assembly lines, helping to streamline development processes. Intelligent IoT (Internet of Things) devices can monitor manufacturing floors and mitigate risks in real-time.
Retail and Ecommerce
The use cases for AI in retail are potentially limitless, covering everything from customer service to sales. AI allows retailers to better understand their customers’ needs and deliver highly personalized shopping experiences. Generative AI can even help retailers build stronger marketing campaigns, sales pitches, and images for selling strategies.
Retailers in any niche can use AI to help them with dynamic pricing optimization, inventory management, and demand forecasting. AI can also provide shoppers with real-time, 24/7 customer service, reducing the strain on human employees. Plus, it can improve customer shopping experiences online with enhanced search functionality and filtering options.
Transportation and Travel
Artificial intelligence is enhancing countless transportation systems today. Solutions like Google Maps already use machine learning algorithms to monitor traffic conditions and help users find the best route to their location. Ride-sharing companies like Uber use machine learning to set prices, match drivers and riders, and predict arrival times.
Elsewhere in the travel industry, AI helps consumers develop personalized itineraries for vacations and business trips and can deliver real-time insights into transportation delays. AI can even support customer service in the hospitality industry, offering access to event recommendations, real-time translation, and customer support as they explore the world.
Supply Chains and Logistics
The number of AI use cases for supply chain and logistics companies has been gradually increasing in the last few years. Many leading companies already use AI to enhance warehouse layouts and space utilization, boosting operational efficiency and reducing risks. AI can track and monitor shipments, provide insights into potential weather conditions and route problems that might delay deliveries, and assist companies in optimizing routes in real time.
Intelligent tools can even automate inventory and resource management. AI-powered inventory management tools can monitor inventory levels and automate replenishment protocols. Advanced tools can even ensure the right tasks are assigned to specific team members simultaneously, ensuring warehouses operate effectively.
Real Estate and City Planning
The application of AI in real estate and city planning is growing, too. Government groups can use artificial intelligence to identify opportunities to improve the layout of different environments based on available amenities and citizen needs. They can also monitor potential threats in real time, using sensors and connected devices to keep local people safe.
In the real estate landscape, AI solutions can automate property evaluation and management processes and help individuals maximize their portfolios in the investment space. Generative AI systems can even power virtual real estate agents who can guide customers through a digital twin of a home in a virtual reality setting.
Media and Entertainment
In the entertainment space, many organizations are leveraging generative AI to create video scripts, develop images and backgrounds, and produce non-playable characters for games. AI also powers various recommendation systems, such as those used by Netflix and Spotify.
Artificial Intelligence can also contribute to storytelling, augmenting content creation and delivery. Intelligent tools can also assist with editing movies and other forms of content at scale.
Legal AI Use Cases
In the legal landscape, AI empowers specialists to streamline case management and simplify the path to justice. More than 82% of lawyers and attorneys believe generative AI could be a valuable tool for legal work. With AI, teams can analyze contracts, conduct legal case research, and complete e-discovery processes faster.
AI can assist with the creation of crucial documents for legal proceedings. It can also conduct in-depth analyses of risks and compliance breaches.
AI Use Cases are Constantly Evolving
Ultimately, companies can explore countless AI use cases today, regardless of which industry or niche they serve. Artificial Intelligence is becoming an increasingly powerful and versatile tool in our lives. However, it’s important to remember that it does still have limitations.
While AI’s applications might seem limitless, business leaders should remember that it also has challenges. A cautious approach to selecting the right AI use cases and implementing the correct technologies is crucial to minimizing risks.
