Martin T. Olsen, Vice President Global of Edge and Integrated Solutions, Vertiv, discusses the most interesting uses of Generative AI happening today in the financial, healthcare, government and industrial sectors.
Generative AI was a game-changer. Although Artificial Intelligence (AI) is not new, the AI landscape changed drastically with the launch of ChatGPT in November 2022.
This chatbot and the LLMs (large language models) behind it – and the newer versions of the LLMs that followed – transformed AI from a tool used only by specialized technologists and data scientists into a tool that can be accessed by everyone.
In the process, it unleashed a technological revolution that will be, at the very least, as disruptive as the Internet – and many believe it will be much more disruptive. Google’s CEO Sundar Pichai claims that AI will have a deeper effect on humanity ‘than electricity or fire’, while Microsoft’s Satya Nadella believes that Generative AI represents the ‘first time a technology developed in Silicon Valley brings benefits to the lives of ordinary people so quickly and so tangibly’.
The Impact of Generative AI on companies
The emergence of Generative AI is expected to have a huge impact on businesses. Goldman Sachs projects that Generative AI has the potential to increase annual labor productivity by approximately 1.5% points over 10 years and lead to a 7% increase in global GDP.
McKinsey is equally optimistic. According to research conducted by the company, Generative AI could add the equivalent of US$2.6 trillion to US$4.4 trillion annually across the 63 use cases analyzed. The company also noted that this estimate would almost double if it included the impact of integrating Generative AI into software currently used for other tasks not analyzed.
New use cases and new tools are emerging practically every day, but below are some of the most interesting uses of Generative AI happening today in the financial, healthcare, government and industrial sectors.
Use cases of AI in financial services and banking
The financial services sector is typically quick to adopt technologies that can improve processes and services because small gains in speed and efficiency can produce large returns. Across the segment, Generative AI has been evaluated or used in a variety of processes, from improving loan and credit risk assessment to managing regulatory compliance, detecting fraud or enhancing customer service.
For example, the latest iteration of the Visa Account Attack Intelligence (VAAI) Score uses Generative AI to assess more than 180 risk attributes in milliseconds and generates a score predicting the likelihood of a bot-assisted brute force card fraud. Visa is developing a Generative AI model to combat card testing fraud. The AI-enabled VAAI Score has six times more fraud detection capabilities than previous models and has reduced the false-positive rate by 85%.
Financial services companies also see potential in Generative AI to improve customer service and decision-making. Bank of America recently introduced an AI-enabled virtual assistant, Erica, to provide customers with personalized financial guidance. Capital One is taking a similar approach with Eno, an AI-enabled natural language SMS assistant.
Generative AI is also helping financial services companies navigate the complex regulatory environment. Compliance management software providers are integrating Generative AI and Machine Learning into their platforms to analyze regulatory rules, policies, and procedures and identify and assess compliance risks.
Use cases of AI in healthcare
Healthcare has been one of the primary beneficiaries of AI with use cases going beyond pharmaceutical development and patient care. AI is being used to automate administrative tasks, improve image analysis, assist in diagnostics, and develop personalized care programs.
One of the most exciting use cases is drug discovery and testing. Generative AI can accelerate the process of identifying compounds for new drugs and the speed of their development. A study by the Boston Consulting Group found that AI can reduce drug development and testing costs and time by 25 to 50%, enabling life-saving and life-changing medications to reach the market faster.
Here are some examples:
Researchers at MIT used AI to analyze over 100 million chemical compounds, leading to the development of Halicin, an antibiotic that proved effective against various strains of bacteria resistant to existing antibiotics.
Insilico used its AI platform to generate and optimize INS18_055, developed to treat idiopathic pulmonary fibrosis (IPF), a type of lung disease. Now in clinical trials, the drug was developed in just 18 months from target identification to designation as a preclinical candidate.
Biotechnology company Recursion used AI on biological image data to identify more than 20 new drugs to be investigated for genetic and ageing-related diseases, many of which are now in clinical trials.
Use cases of AI in the government sector
The government sector could become one of the largest users of AI due to the immense amount of data it handles daily and the multitude of citizens it serves.
Within the US federal government, AI use cases were emerging so rapidly that a database was created to track them. This database now includes more than 700 examples of how departments and agencies are using AI, including analyzing urban heat islands to better protect residents against extreme weather conditions, analyzing unstructured feedback from military veterans to improve service delivery, and speeding up the process of comparing new patent applications with existing patents.
In Argentina, the Ministry of Health is using AI to predict the spread of diseases like dengue based on climate data and population flows. Locally, the Public Prosecutor’s Office in Buenos Aires worked with the AI lab at the University of Buenos Aires to develop Prometea, an AI virtual assistant that helps speed up judicial work.
Use cases of AI in the industrial sector
Industry has already greatly benefited from AI and other advanced technologies, and Generative AI will enable greater efficiencies and better quality. AI is being used to accelerate product design and development, monitor quality, and increase the accuracy of production planning and inventory management.
General Motors uses AI-enabled generative design to drive continuous improvements in vehicle components, focusing on lightweighting. In collaboration with AutoDesk, GM engineers were able to quickly evaluate more than 150 different designs for a seat bracket and generate a design that simplified manufacturing while reducing weight by 40% and increasing strength by 20%.
Airbus had a similar experience with generative design, using it to create lighter partitions for the A320. It used Generative AI algorithms based on growth patterns found in nature to optimize the structure of the partitions. The resulting ‘bionic partition’ is 45% lighter than traditional designs while meeting strict requirements for stress and displacement due to impact forces.
In factories, Generative AI is being used to increase production uptime and reduce service costs. AI models can be trained with data from equipment sensors and recognize patterns in this data that may indicate an impending failure. AI is also being used to analyze historical maintenance data to help identify and resolve issues and analyze failures.
Preparing for the AI Revolution
The question is not if AI will come to your company, but when – if it is not already there. As you get excited about AI’s potential in your organization, it is important to identify the changes that will be needed to enable the AI journey and maximize the return on investment (ROI) in all your AI use cases.