20 Real-World Examples of GenAI Applications Across Leading Industries

6 Generative AI Use Cases: Real-World Industry Solutions

In the case of building an application for lawyers, we need to make a representative selection of limited old cases. Those cases are the basis for defining standard scenarios of the application based on which we implement the application. For example, if the lawyer specializes in financial law and taxation, we would select a few of the standard cases for which this lawyer has to create scenarios. Every building and evaluation activity that we do in this phase has a limited view of representative scenarios and does not cover every instance. Yet, we need to evaluate the scenarios in the ongoing steps of application development.

The role of AI in this ever-changing innovation-driven market is unmatched to any other influence on a business. It is now more important than ever companies make the right strategic decisions to implement AI capabilities. Generative AI can reduce adviser response time by up to 35%, support consultants during the resolution process by managing different sources of knowledge, and improve the quality of results by up to 40%.

How Generative AI is Shaping Martech Innovation

For example, the same SSN should be masked by the same identifier so that a downstream application can use the relationship in building effective applications. Phi-3 and Gliner perform very well in PII masking, but the best-performing model for this use case at the time of this writing is the Llama-3.1-8B model. Additionally, smaller models can be hosted on-premises, providing better control over the shared data with these language models.

Some use cases are highly underutilized by marketers, while others are underutilized by software vendors. IMD complies with applicable laws and regulations, including with respect to international sanctions that may be imposed on individuals and countries. This policy applies to all applications for IMD programs from individuals or organizations, and any commercial or non-commercial partnerships. You can use it to generate different business scenarios to find the one that’s most efficient. Since then, researchers have used Transformers in combination with what they already know about how AI works to create new AI models that are better than anything before. AI can now create text, images, audio, and video using both commercial and open-source AI models.

1. Context and Orchestration of Performance Evaluation for Generative AI Applications

These tools can also translate content into multiple languages, ensuring message consistency across different markets. Beyond text, GenAI can also create visuals, such as vivid images or infographics for ads. GenAI goes beyond traditional static analysis tools in bug detection, doing more than just catching syntax errors—it also identifies potential vulnerabilities and logic flows before they escalate into bigger problems. Software development teams can use generative AI coding solutions to scan their codebase for security weaknesses that could compromise confidential data. These AI tools flag risky areas and suggest ways for fixing them, delivering a proactive approach to debugging and preventing costly errors. The technology optimizes food supply chains by plotting and analyzing variables such as transportation costs, spoilage rates and market demand, ensuring fresh produce reaches consumers faster and at reduced costs.

• In the fiercely competitive retail sector, Walmart’s utilization of AI into supply chain operations exemplifies how cutting-edge technologies enhance decision-making, responsiveness, and overall supply chain resilience.
• Just like in the real world, think of different agents that take different roles and collaborate with each other.
• GenAI can tailor the student learning experience, turning lessons into visual dramas for some and crafting narratives and games for others based on students’ preferences, needs and capabilities.
• However, its rise has sparked significant debates around copyright law, particularly regarding the concept of fair use.
• Because they leverage speech-to-text to create a transcript from the customer’s audio.

We use this question in step 2 to create a semantic search query in our vector database using the cosine similarity metric. In other words, it does not help anyone to have the best model in the world if the RAG pipeline always returns mediocre results because your chunking strategy is not good. Also, if you do not have the right data to answer your queries, you will always get some hallucinations that may or may not be close to the truth.

Effective Supply Chain Management

Approximately 35 percent of enterprises are doing their own GenAI initiatives in-house. ISG said past research highlighted the importance of revenue growth as a top enterprise objective for the adoption of AI. However, higher-value use cases in the future will be those that do not involve HITL process so that enterprises can achieve more dramatic scaling. Over 200 professionals—including C-level executives and leaders across sales, marketing, HR and financing—were surveyed from a cross-section on major industries across 10 regions.

In fact, 66% of companies in life sciences are using it to enhance their business operations, including compliance improvement and supply chain transformation. For compliance, genAI automates the monitoring and reporting processes, ensuring that companies adhere to regulatory standards more efficiently and with fewer errors. In supply chain management, it uses predictive analytics to forecast demand and optimize inventory levels, leading to more efficient logistics and distribution strategies.

However, sometimes, it’s important to manually review conversations that potentially hold the biggest lessons for the customer service team. As such, it’s no wonder that around 80 percent of contact centers are using AI-powered technologies, such as AI assistants, to revolutionize customer service. Additionally, they are smarter than ever, leveraging machine learning, natural language processing (NLP), generative AI, and advanced algorithms to make contact center teams more productive and efficient. In recent years, tools have helped, with virtual assistants providing instant access to pertinent information, offering real-time coaching, and automating tedious tasks like post-contact processing.

If you use GenAI correctly, it will help you completely transform your business operations. Creating engaging property listings at scale, automating tenant requests, and boosting real estate acquisitions are just a few ways it supports productivity across the organization. Asset managers can use GenAI to collect and analyze property-level data, including sales records, rental prices, property characteristics, and market trends to manage the business more effectively.

Predictive maintenance has emerged as a game changer in the manufacturing industry, owing to the application of artificial intelligence. By leveraging advanced predictive analytics and machine learning algorithms, AI in the manufacturing industry enables companies to proactively monitor and predict equipment failures, minimizing downtime and optimizing maintenance schedules. AI in the supply chain enables leveraging predictive analytics, optimizing inventory management, enhancing demand forecasting, and streamlining logistics. ML algorithms can analyze historical data, identify patterns, and accurately predict demand fluctuations. For instance, an automotive parts manufacturer can use ML models to forecast demand for spare parts, allowing them to optimize inventory levels and reduce costs. To avoid problems related to health, economy, and society caused by outbreaks, it’s key for both the private and public sectors to have access to unbiased, accurate data in real time.

• Research centers participating in the race for delivering new drugs or medical technologies to the market must have access to verified data from all areas of the business.
• Before rolling out such technologies, businesses need a deep understanding of the common practical tasks that users find themselves needing to complete and what frustrates them most.
• The company collaborates with over 250 life science organizations across the globe, helping them develop novel solutions for more than 600 illnesses.
• “Successful players acknowledge Generative Al’s strengths in leveraging unstructured data and have actively taken efforts to utilize its potential,” noted Van Engelen.

The conceptual illustration shows the overall concepts in black, an example definition in blue and the outcome of one instance of an execution in green. All those metrics are synthetic and aim to provide a relative comparison between different LLMs. However, their concrete impact for a use case in a company depends on the classification of the challenge in the scenario to the benchmark. For example, in use cases for tax accounts where a lot of math is needed, GSM8K would be a good candidate to evaluate that capability. HumanEval is the initial tool of choice for the use of an LLM in a coding-related scenario. We can evaluate how well our application serves its intended purpose end-to-end for such large orchestrations with different data processing pipeline steps.

Vendor invoice processing

Meta and Microsoft both invested large sums in new nuclear power generation, as well as other clean energies like geothermal, to power its AI growth. 2024 saw a continued uptick in AI adoption and investments, accompanied by an expected dramatic increase in energy demand from data centers. During that boom, corporations across industries are increasingly turning to AI to solve sustainability challenges like reporting and calculating their carbon footprint with various regulatory disclosure guidelines looming.

We map our concepts for evaluation scenarios and evaluation scenario definitions and map them to classic concepts of software testing. The start point for any interaction to create a new test is via the entAIngine application dashboard. An evaluation scenario should be executed many times because LLMs are non-deterministic models. We want to have a reasonable number of executions so we can aggregate the scores and have a statistically significant output. In step 1, we extract a single question from the case-specific input context (the customer’s email inquiry).

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Harvey is fine-tuned on vast amounts of legal data, specifically designed to analyze complex scenarios, with some lawyers reporting that they value it for its accuracy and depth. According to the 2023 “International Legal Generative AI Survey” by LexisNexis, nearly half of all lawyers surveyed said they believe generative AI will transform their business, with a staggering 92% anticipating at least some impact. For example, in wealth management, GenAI helps banks like Wells Fargo suggest optimal investment strategies and create customized portfolios based on individual risk appetites. Manufacturing teams have to meet production goals across throughput, rate, quality, yield and safety. To achieve these goals, operators must ensure uninterrupted operation and prevent unexpected downtime, keeping their machines in perfect condition. However, navigating siloed data — such as maintenance records, equipment manuals and operating procedure documentation — is complicated, time-consuming and expensive.

These solutions suggest code snippets in real-time, provide smart autocompletions, and even refactor code to make it more efficient. GenAI is beneficial in handling repetitive tasks, like setting up standard functions or offering ready-to-use code blocks. Additionally, it is useful in finding relevant methods, classes, or libraries within large codebases, and suggesting how to implement them for specific functionalities. Thousands of U.S. companies will be required to comply with the European Union’s Corporate Sustainability Reporting Directive in short order.