Generative AI Use Cases
Generative AI exhibits a wide array of applications across diverse industries, demonstrating its potential for innovation and advancement. Below are some examples7:
Oil and Gas
In the oil and gas industry, knowing the system behavior and its observed data without prior knowledge of the model that generated it is a common scenario that is called the inverse problem. It spans to various use cases such as generation of synthetic data to fill gaps in historical (production)/transactional (operations) data or direct/indirect measurements for reservoir analysis, equipment, chemical processes, and facilities simulation.
Ensuring dataset quality and interpretation greatly impacts model precision but can take a long time to prepare using traditional ML approaches. Traditional ML approaches also face challenges with limited samples and labeling. Generative Adversarial Networks (GANs) offer effective solutions in this domain, enabling advancements in well logging, lithology identification of complex rock structures, missing oilfield data, multiphase flow reservoir simulation, and more.
On the other hand, LLMs are able to enhance Natural Language Processing and inference to develop geoscience and industry-specific language processing solutions, empowering geoscientists, engineers, and industry users to automate analysis, gain insights, and find answers within large datasets.
Generative AI automates generating personalized content for marketing by leveraging customer data. For example, it can power customized product recommendations based on an individual’s purchase history and browsing behavior. It can also strengthen customer engagement and reduce man-hours by crafting personalized marketing campaigns. CarMax harnesses Azure OpenAI Service to summarize customer reviews, saving the editorial team 11 years of work.8 This boosts customer engagement and empowers the team for more creative content.
Generative AI in healthcare improves patient outcomes and reduces costs, enabling proactive assistance in diagnosis, prevention, risk prediction, and drug discovery.9 Paige.AI utilizes generative AI for enhanced cancer detection, with plans to integrate patient records (knowledge mining) for diagnosis. Additionally, generative AI can streamline administrative tasks, which is projected to save healthcare providers 3 hours daily.9
In MedTech, it helps optimize designs for prosthetics and implants and provides real-time, on-demand surgical insights inside operation rooms. 9 McKinsey’s report indicates generative AI’s potential to accelerate lead identification in drug discovery, shortening the timeline from months to weeks.5
Generative AI holds significant potential in the construction industry, offering automation for architects and designers to visualize plans, incorporate sustainable materials, ensure regulatory compliance, and enhance building efficiency.10 Designers can generate initial concept images using graph-generating AIs like Stable Diffusion, Midjourney, and DALL-E. Large Language Models (LLMs) can help with material sourcing, reducing research time and facilitating optimal selection while providing insights into product performance. Moreover, generative AI aids in risk reduction by identifying material-system incompatibilities, thereby mitigating future rework costs.
Using generative AI, enterprise can leverage the wealth of safety data at their disposal, and enhance their ability to predict and mitigate risks in construction projects. They can identify patterns and suggest preventative measures to avoid future incidents. The ability of LLMs built for the specific entity to provide comprehensive, tailored, and accurate answers in natural language further supports informed decision-making processes within the construction domain.
Food and Beverage
Generative AI has emerged as a game-changer for the food and beverage industry, enabling rapid product development that aligns with emerging trends and facilitates market expansion. Foodpairing has developed a state-of-the-art Generative AI model to explore and discover captivating flavor combinations.11 Tastewise, a leading AI-powered market intelligence platform dedicated to the food and beverage sector, introduced TasteGPT, to empower users to unlock a world of culinary creativity.12
Generative AI can enhance customer service by offering tailored recommendations and solutions. For instance, chatbots equipped with generative AI can troubleshoot customer issues on the fly while waiting for an available representative, potentially reducing the need for representatives’ intervention altogether in many cases and thus reducing costs. It can also analyze customer feedback, extracting and identifying key products or experience improvements and bolstering overall customer satisfaction.5
Generative AI offers numerous benefits to the Human Resources (HR) field across various areas. It can assist in creating job descriptions, supporting employees, facilitating onboarding and training, promoting diversity and inclusion, and providing guidance on HR policies.13 Generative AI can help employers create more inclusive job descriptions and reduce potential bias in the hiring process.14
By incorporating generative AI into knowledge management, organizations can foster knowledge sharing, offer personalized learning and development plans, and enable employees to access HR policy information through natural conversations. Moreover, generative AI has the potential to support employee well-being by providing personalized recommendations for stress management, achieving work-life balance, and more. Additionally, it is expected to generate personalized feedback on employee performance, thereby enhancing performance management.
In summary, generative AI’s impact on oil and gas, marketing, healthcare, construction, food and beverage, customer services, and HR is reshaping industries, and fostering efficiency, personalization, and transformative advancements.
Addressing Concerns and Embracing Opportunities