Vertiv GmbH

Generative AI has changed the game

Although artificial intelligence (AI) is not new, the AI landscape changed dramatically with the release of ChatGPT in November 2022. This chatbot and the large language model (LLM) behind it – along with the newer LLM versions that followed – turned AI into a tool that is no longer limited to skilled technologists and data scientists, but accessible to virtually everyone.

In doing so, it has triggered a technological revolution that many believe will be at least as transformative as the internet – if not far greater. Google CEO Sundar Pichai has claimed that AI will have a more profound impact on humanity “than electricity or fire.” Meanwhile, Microsoft CEO Satya Nadella believes that with generative AI, “for the first time, a technology developed in Silicon Valley is benefiting ordinary people in their everyday lives so quickly and so tangibly.”

The impact of generative AI on businesses

The rise of generative AI is expected to have enormous implications for businesses. Goldman Sachs forecasts that generative AI could increase annual labor productivity growth by approximately 1.5 percentage points over a 10-year period and boost global GDP by 7%.

McKinsey shares this optimistic outlook. According to one of its studies, across 63 analyzed use cases, generative AI could increase revenues from $4.4 trillion to $2.6 trillion annually. The firm also noted that this estimate would roughly double if the impact of embedding AI into software currently used for activities beyond those analyzed were included in the forecast.

New use cases and tools are emerging almost daily. Below are some of the most compelling existing applications of generative AI in finance, healthcare, public administration, and manufacturing.

AI use cases in financial services and banking

The financial services industry is often quick to adopt technologies that improve processes and services, as even small gains in speed or efficiency can yield substantial returns. Across the sector, generative AI is being evaluated or already deployed in a wide range of processes – from enhancing loan and credit risk assessment to regulatory compliance, fraud detection, and customer service.

For example, the latest version of Visa’s Account Attack Intelligence (VAAI) Score uses generative AI to evaluate more than 180 risk attributes within milliseconds to predict the likelihood of bot-assisted “brute force” card fraud. The AI-powered VAAI Score includes six times as many fraud detection features as previous models. Visa is also developing a generative AI model to combat card-testing fraud and has reduced false positives by 85%.

Financial institutions are also leveraging generative AI to improve customer service and decision-making. Bank of America recently introduced its AI-powered virtual assistant Erica, which provides personalized financial guidance. Capital One has taken a similar approach with Eno, an AI-driven natural language SMS assistant.

Generative AI also helps financial services firms navigate complex regulatory environments. Compliance management software providers are integrating generative AI and machine learning into their platforms to analyze regulations, policies, and procedures, and to identify and assess compliance risks.

AI use cases in healthcare

Healthcare is one of the primary beneficiaries of AI. Applications range from drug development to patient care. AI is being used to automate administrative tasks, enhance medical imaging analysis, support diagnosis, and develop personalized treatment plans.

One of the most exciting use cases is drug discovery and development. Generative AI can accelerate the identification of compounds and the development of new medicines. A Boston Consulting Group study found that AI could reduce drug development costs and timelines by 25–50%, helping life-saving and life-changing treatments reach the market faster. Examples include:

• Researchers at MIT used AI to analyze more than 100 million chemical compounds, leading to the discovery of Halicin, an antibiotic effective against many drug-resistant bacterial strains.
• Insilico used its AI platform to generate and optimize INS018_055, a drug developed to treat idiopathic pulmonary fibrosis (IPF), a lung disease. The drug progressed from target identification to preclinical candidate nomination in just 18 months and is currently in clinical trials.
• Biotech company Recursion used AI to analyze biological imaging data and identify more than 20 new drug candidates for genetic and age-related diseases, some of which are now in clinical trials.

AI use cases in public administration

Public administration may become one of the largest users of AI due to the vast amounts of data it handles and the broad populations it serves.

In the United States, AI use cases have evolved so rapidly that a dedicated database was created to track them. It already contains more than 700 examples of how departments and agencies are using AI – such as analyzing urban heat islands to better protect residents from extreme weather, evaluating unstructured feedback from military veterans to improve care, and accelerating comparisons between new patent applications and existing patents.

In Argentina, the Ministry of Health uses AI to predict the spread of diseases such as dengue fever based on climate data and population movement. In Buenos Aires, the public prosecutor’s office partnered with the University of Buenos Aires’ AI lab to develop Prometea, a virtual AI assistant that helps the judiciary process cases more efficiently.

AI use cases in manufacturing

Manufacturing has already benefited significantly from AI and other advanced technologies. With generative AI, even greater efficiency and quality improvements are possible. AI is accelerating product design and development, improving quality control, and enhancing the precision of production planning and inventory management.

General Motors uses AI-based generative design to continuously improve vehicle components, with a focus on weight reduction. In collaboration with Autodesk, GM engineers evaluated more than 150 alternative designs for a seat bracket and identified a version that is easier to manufacture, 40% lighter, and 20% stronger.

Airbus achieved similar results using generative AI to design a stronger, lighter partition wall for the A320 aircraft. Based on algorithms inspired by natural growth patterns, the resulting “bionic partition” is 45% lighter than conventional designs while meeting strict load-bearing and crash safety requirements.

On the factory floor, generative AI is used to increase uptime and reduce service costs. AI models trained on equipment sensor data can identify patterns indicating potential failures. AI is also used to analyze maintenance history data to support troubleshooting and root cause analysis.

Preparing for the AI revolution

The question is not whether AI will be implemented in your organization, but when – if it hasn’t already. As you explore AI’s potential for your business, it is crucial to recognize the changes required to successfully deploy AI and maximize return on investment from your AI use cases.