Enhancing Businesses with Large Language Models and ChatGPT
Dec 5, 2023
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Guests discuss the efficiency and growth potential of AI capabilities like ChatGPT and LLMs. They explore leveraging AI for better customer service and decision support, addressing challenges of data security and workforce adaptation. They also discuss balancing AI tools with human expertise and provide tips on implementing AI with a forward-thinking mindset.
Large language models like ChatGPT and LLMs offer opportunities for efficiency, growth mindset, and better customer outcomes.
Implementing large language models requires starting with the problem businesses want to solve, getting stakeholders excited, and balancing them with human expertise.
Deep dives
Adopting Large Language Models for Business Communications
Large language models like Chagibiti are revolutionizing business communications, and businesses are utilizing them in various ways. Sam, the CEO of CuriousThingsAI, started using large language models like BERT and Chagibiti for their voice AI product, automating outbound phone calls. Victor, a Product Analytics Executive at Quantium, highlighted four focus areas for using large language models: customer service, decision support, interpreting information, and understanding feedback. Jackie, the Head of Finance at NIB Travel, mentioned using large language models for improving customer service, converting unstructured text data, and enhancing business intelligence. These models offer opportunities for efficiency, growth mindset, and better customer outcomes, but businesses need to consider challenges like data security, resistance to change, and addressing foundational issues before implementing large language models.
Industries and Tips for Implementing Large Language Models
Industries like healthcare and financial services are particularly inclined to use large language models for customer support and workforce optimization. Implementing large language models requires starting with the problem businesses want to solve, experimenting with the tools, and getting stakeholders excited about the potential value. Sam suggests exploring the API playground and breaking down complex reasoning chains to improve results. Jackie emphasizes starting with non-serious experimentation to understand the capabilities of large language models and finding business problems where they can be the best fit. Victor stresses the importance of getting stakeholders excited, demonstrating value through feasibility experiments, and leveraging the strengths and unique knowledge of the business. It is crucial to balance the use of large language models with human expertise and view the technology as a tool for productivity and enhancement.
Overcoming Resistance and Leveraging Large Language Models
Resistance to change and cautious attitudes towards adopting large language models are important barriers to overcome. By demonstrating the potential impact on customer and shareholder value, businesses can encourage stakeholders to embrace the technology. Addressing foundational issues and focusing on low-hanging fruit areas, where large language models can provide immediate benefits, can also drive adoption. Consideration of data security, along with other risks and challenges, should be a part of the implementation process. It is crucial to separate genuine implementation challenges from the potential of large language models and have a growth mindset towards embracing transformative technologies. Overall, large language models should be viewed as tools that augment human capabilities rather than replace them.
Actuaries Institute Fellow Meg Yang brings together Victor Bajanov, Jacky Poon and Sam Zheng to discuss the potent capabilities of AI in driving businesses' efficiency and growth.
In this podcast, they discuss:
the efficiency and growth ChatGPT and LLMs can offer;
how industry leaders can leverage AI for better customer service and decision support;
the challenges of data security and workforce adaptation;
how to balance AI tools with human expertise; and
tips on implementing AI with a forward-thinking mindset