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Generative AI Has Changed the Game 

While artificial intelligence (AI) isn’t new, the AI landscape changed dramatically with the release of ChatGPT in November 2022. That chatbot and the large language model (LLM) behind it, and newer versions of LLM that followed, transformed AI from a tool that only skilled technologists and data scientists were using to one anyone could access.  

In the process, it sparked a technology revolution that will be at least as disruptive as the internet—and many believe much more so. Google CEO Sundar Pichai has claimed that AI will have a more profound 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 benefits the lives of everyday people so quickly and tangibly.” 

The Impact of Generative AI in Business 

The emergence of generative AI is expected to have enormous impact on business. Goldman Sachs projects that generative AI has the potential to raise annual labor productivity by around 1.5 percentage points over a 10-year period and drive a rise in global GDP of 7%. 

McKinsey is equally optimistic. According to research the firm conducted, gen AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across 63 use cases analyzed. The firm also noted that this estimate would roughly double if the impact of embedding gen AI into software currently used for tasks beyond those analyzed were included in their forecast. 

New use cases and tools are emerging almost daily, but here are some of the most interesting uses of gen AI in finance, healthcare, government and manufacturing happening today. 

AI Use Cases in Financial Services and Banking 

The financial services industry is often quick to adopt technologies that can improve processes and services because small gains in speed or efficiency can yield large returns. Across the industry, gen AI is being evaluated or used in a variety of processes from enhancing loan and credit risk assessment, to managing regulatory compliance, detecting fraud, or enhancing customer service. 

For example, the newest iteration of the Visa Account Attack Intelligence (VAAI) Score uses gen AI to evaluate more than 180 risk attributes in milliseconds and generate a score predicting the likelihood of a type of brute-force card fraud aided by bots. Visa develops a gen AI model to combat card-testing fraud The AI-powered VAAI Score has 6 times the fraud-detection features of previous models Visa develops a gen AI model to combat card-testing fraud and has reduced the rate of false positives by 85%. 

Financial services firms also see potential in gen AI to enhance customer service and decision-making. Bank of America recently introduced an AI-powered virtual assistant, Erica, to provide customers with personalized financial guidance. Capital One is taking a similar approach with Eno, an AI-powered natural language SMS assistant.  

Gen AI is also helping financial services companies navigate a complex regulatory landscape. Compliance management software providers are embedding gen AI and machine learning in their platforms to analyze regulatory rules, policies and processes; and identify and assess compliance risks.  

AI Use Cases in Healthcare 

Healthcare has been one of the leading beneficiaries of AI with use cases extending from pharmaceutical development to patient care. AI is being used to automate administrative tasks, enhance analysis of medical images, assist in diagnosis, and develop personalized care programs. 

One of the most exciting use cases is drug discovery and testing. Gen AI can accelerate the process of identifying compounds for new drugs and speed their development. A study by the Boston Consulting Group found that AI can cut 25–50% off the cost and time of drug development and testing, enabling life-saving and life-changing drugs to get to market faster. Here are a few examples:  

  • Researchers at MIT used AI to screen over 100 million chemical compounds, leading to the development of Halicin, an antibiotic that has been found to be effective against many drug-resistant bacterial strains. 
  • Insilico used its AI platform to generate and optimize INS018_055, which is designed to treat idiopathic pulmonary fibrosis (IPF), a form of lung disease. Now in clinical trials, the drug was developed in just 18 months from target identification to preclinical candidate nomination. 
  • Biotech company Recursion has used AI on biological image data to identify over 20 investigational new drugs for genetic- and age-related diseases, several of which are now in clinical trials.  

AI Use Cases in Government 

Governments may turn out to be among the largest users of AI due to the huge amounts of data they deal with daily and the large constituencies they serve.  

Within the U.S. federal government, use cases for AI were emerging so quickly a database was created to track them. That 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, analyzing non-structured feedback from military veterans to improve service delivery, and accelerating the process of comparing new patent applications to existing patents. 

In Argentina, the Ministry of Health is using AI to predict the spread of diseases like dengue fever based on climate data and population movements. Locally, the Public Prosecutor's Office of Buenos Aires worked with the University of Buenos Aires AI Lab to develop Prometea, an AI virtual assistant that helps expedite the work of the justice service. 

AI Use Cases in Manufacturing 

Manufacturing has benefitted enormously from AI and other advanced technologies already and gen AI will enable even greater efficiencies and 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 generative design powered by AI to drive continuous improvement in vehicle components, with a focus on light weighting. Collaborating with AutoDesk, GM engineers were able to quickly evaluate more than 150 alternate 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 a stronger, lighter weight partition for the A320. They used generative AI algorithms based on growth patterns found in nature to optimize the partition's structure. The resulting "bionic partition" is 45% lighter than traditional designs while meeting strict requirements for stress and crash force displacement. 

In the plant, gen AI is being used to increase manufacturing uptime and reduce service costs. AI models can be trained on data from equipment sensors and recognize patterns from that equipment data that may indicate impending failure. AI is also being used to analyze historical maintenance data to aid in troubleshooting and failure analysis.  

Preparing for the AI Revolution 

The question isn’t if AI will come to your business, but when—if it isn’t already there. As you get excited for the potential of AI in your organization, it’s important to identify changes that will be needed to enable your AI journey and maximize the ROI of your AI use cases. 

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