Introduction
Generative AI has become a catalyst for modern businesses. It’s not just about automating processes; it’s about elevating the entire experience. Last year, businesses witnessed the potential and magical power of Generative AI for businesses across various industries. In 2024, it’s not just about adopting new technology; industry leaders are nurturing innovation, fostering valuable business use cases, and implementing strategically to drive business success from this transformative technology.
Based on recent developments, Generative AI performance is now expected to match median human performance. Various research estimates that Generative AI could add trillions of dollars in value to the global economy.
Generative AI’s ability to understand natural language and process large language text and human feedback through reinforcement learning greatly explains its automation potential for a variety of activities and tasks. Previous generations of automation technology had the most impact on lower-wage tasks such as data entry, basic bookkeeping, general repetitive customer support queries, assembly line tasks in manufacturing, etc. However, Generative AI’s impact is likely to transform the work of higher-wage knowledge workers, such as complex data analysis, automated code generation, analysis of medical imaging, tailor-made content creation, drug discovery, knowledge search in large unstructured data, formulating business strategies, and many more.
Gen AI Use-Cases Across Industries
The adoption of Generative AI is also likely to be faster in developed countries, where wages are higher and the feasibility of adopting automation occurs earlier.
Including Banking, High Tech, Pharmaceutical, Manufacturing, Healthcare, Insurance, Telecommunication, etc., all major industries are exploring the valuable Generative AI use-cases in their businesses.
Generative AI can cut the significant time a sales representative or customer support person spends responding to a customer by processing multimodal inputs from text, images, speech, etc., and providing real-time assistance with personalized next steps. It has been the most promising use case in the retail, travel, and CPG industries so far, along with some more following use cases:
- Generative AI can process a long list of customer comments on any popular product or recipe and find the most popular ingredient or tips.
- It can generate a summary of all customer support tickets to find the most common query from your customers.
- It helps generate personalized product descriptions based on customer preferences, behavior, and purchase history.
- It automates customer care functions, sales follow-ups, etc., and could increase productivity from 20 to 40% of current function costs.
Generative AI in Finance
Banks and financial institutes have invested highly in digital transformation for decades and have benefited significantly from AI applications in areas such as marketing and customer operations. One of the European banks used Generative AI to develop an environmental, social, and governance (ESG) virtual assistant to search and extract useful information from long, unstructured documents and here are a few more use cases:
- Extract data and named entities from invoices, receipts, bank statements, and other large financial documents for automated data entry and financial analysis.
- Generate synthetic financial data for risk simulations, model training, and testing different financial use cases.
- Help banks, telecommunications, and other financial companies make tailor-made customer contracts and reduce human intervention by 30-40%.
However, it is a heavily regulated industry with a substantial number of risk, compliance, and legal needs. FIS (a financial company) research reveals that most banks are still nervous about adopting this Generative AI technology, hold divided opinions, and believe that regulation would increase trust.
Generative AI for Pharmaceutical and Life Sciences
Pharmaceutical, life sciences, and chemicals are other major industries that started using Generative AI in Drug Discovery use cases. These companies spend roughly 15-25% of their revenues and years to develop new drugs. Generative AI foundation models help generate candidate molecules and accelerate the drug development process along with a few more use cases:
- Make search faster in large unstructured scientific literature and complex queries for relevant information to accelerate drug discovery.
- Generate realistic data and analyze medical images to detect diseases faster for clinical trials.
- Analyze handwritten doctor’s notes, extract patient information from medical records, and automate insurance claims.
Considering the pace of Generative AI adoption in 2024, businesses must act quickly to prepare valuable use cases and address both the opportunities and risks because good things will come to those businesses that don’t wait! CrossML Pvt Ltd can be your ultimatе partner in implementing thе full potential of Gеnеrativе AI to push your business towards unmatchеd value and еxponеntial growth.
