Igor Epshteyn is its CEO and President Coherent Solutions.
Modern large language models (LLMs) push automation and quality boundaries in business operations by transforming natural language into text, information, and code. They help employees free up more time and increase productivity.
This article provides my experience with the implications of the LLM, its development in a custom project, and the risks you need to be aware of to make informed decisions to keep up with competitors.
LLM application in all industries
LLMs are just one of many aspects of artificial intelligence. They allow you to drive transformational changes in your organization’s operational workflows and are adaptable to various areas, especially where decisions at various levels are based on fast and efficient analysis of huge amounts of data.
Health and Life Science
During an NMDP BioTherapies 2024 webinar, healthcare experts discussed the role of AI in successfully analyzing genetic data for stem cell donor matching— improving the speed and accuracy with which life-saving transplants find patients. For life sciences companies, LLMs are instrumental in powerful solutions that analyze various clinical cases for adverse effects. They help automate management pipelines for parsing PDF documents, extracting key information, and adding guardrails to make sure AI is applied responsibly.
Finance and Banking
For some financial institutions, AI has helped analyze transaction patterns and reduce false positive fraud alerts by 50%. This minimizes disruption from false alerts, allowing risk management teams to focus on real threats. Additionally, they provide virtual assistants with artificial intelligence, such as Bank of America’s Erica personalized financial advicefaster response time to customer inquiries and lower support costs.
Manufacture and Portable Fitness Equipment
Manufacturers can use LLM to analyze production data such as production metrics, performance and quality control results. They can use information to predict equipment failures and recommend timely maintenance programs. Similarly, makers of wearable fitness devices like Fitbit use advanced artificial intelligence to provide users with training information and tailored recommendations based on user data such as heart rate, activity patterns or sleep quality.
Technology
AI accelerates development cycles by helping engineers write code and automating testing and debugging. LLMs help them quickly analyze technical documentation to find relevant information. Additionally, organizations that rely on IT support and customer service teams can respond to customer inquiries faster, and according to IBM research, AI can help support cost reduction by 30%.
Open Source or Proprietary LLM?
When choosing an LLM for your project, you want to strike a balance between control and convenience.
Popular models such as OpenAI’s GPT-4, Cohere’s Command R, and Anthropic’s Claude v3.5 Sonnet support accessible cloud APIs. These models offer easy integration with your existing systems and are scalable enough to meet various business goals. They receive continuous updates from providers, ensuring access to the latest developments in artificial intelligence.
Finance and healthcare businesses may choose to host LLMs in the private cloud for improved data governance and compliance with privacy regulations. You can use the same models mentioned above for private data using Azure OpenAI and AWS Bedrock. In addition, Meta’s LLaMA 2 and 3, as well as Google’s Gemma and Mistral models, will allow you to tailor the environment to your business needs, combining greater data oversight with the flexibility of a cloud.
A traditional on-premise installation strengthens privacy compliance, data control and information security by keeping all data—including sensitive information—on your organization’s infrastructure. This setting requires a license for models such as GPT-3 or allows the option to use open source alternatives.
Understanding the hidden dangers
Don’t dismiss LLMs from your project solely because of potential risks. Instead, identify risk factors and implement effective AI safeguards.
Open source LLMs offer flexibility and customization, but come with security concerns. Without careful maintenance, they may expose sensitive data to cyber threats. Additionally, their training data is often not fully anonymized, increasing the risk of data exposure and potential intellectual property violations. As these models are maintained by a decentralized community rather than a dedicated team, the consistency of their performance may vary.
In contrast, proprietary LLMs typically offer strong security features, but still pose privacy and data control risks. Using these models involves sharing sensitive data with a third-party provider, which could result in regulatory penalties in the event of a breach.
LLMs also lack transparency about their training data and how the datasets are structured. Consider potential bias and fairness issues and consider a human-based approach where experts review and manage model results.
Promotion of Responsible IT
LLMs are most effective when used to streamline complex processes and drive innovation. To leverage these models responsibly, prioritize data governance—especially in highly regulated industries.
Equally important are transparency and ethical AI practices. By taking this approach, you can build trust with stakeholders and customers while encouraging sustainable growth for your organization.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Am I eligible?
