Show Notes: In this episode of Unleashed, Phil Bellaria shares a case example of building a Chat GPT using open-source large language models. The client was a large telecommunications company with an immense amount of unstructured data, including customer feedback, feedback from employees through surveys, and transcript transcripts from millions of phone conversations and text chats. The problem statement was to derive insights and understand the state of the business, identify trends and topics as quickly as possible. The process took place through 2018-2020. Working with a data scientist, and using Google's BERT methodology for natural language processing, the team coded an algorithm that identified topics and classifiers from the unstructured data, scored each topic and phrase on sentiment (positive or negative comments) and created a short summary of customer or employee comments related to each topic. The process of building and running the model was processing intensive, and the first step was testing and iterating the model on smaller samples of data. The company held employee surveys, which was processed through the test model, the data was reviewed by HR business partners and business leaders to check for accuracy. The model was trained on all the information in Wikipedia, but other specific information and words were added to refine it. Over six to eight months, the model was able to accurately represent what employees were saying.  Using AI to Improve Sales Pitches Phil discusses the use of AI in business applications and how it can be used to improve sales pitches. He explains that the problem was to understand why sales agents were not pitching a strategic product effectively. By feeding data from conversations with customers about the product, the algorithm was able to identify words and phrases associated with successful sales and non-successful sales. This information was then used to train sales agents on the right expressions and words to use when pitching the product.  Phil shares some phrases that work well and those that don't, such as promoting a streaming product by associating it with popular shows. He also discusses the challenges of building AI models and securing and protecting data. He also addresses the cost of building an AI model. Using AI for Next Best Customer Actions Phil shares one example of AI-related projects which used AI algorithms to predetermine the next best action for a customer that can be used in real time to learn the best approach in customer interaction. The AI engine uses reinforcement learning to improve the power of the recommendations. The process involved building the right APIs into existing systems and ensuring SLAs in terms of responsiveness. The algorithm itself uses sophisticated statistical modeling techniques, but the main challenge was integration and timeliness. Challenges Implementing AI Phil talks about the challenges of implementing this process. He emphasizes the importance of defining the business problem and getting the technical team involved early in the process. He talks about time spent translating the problem into technical applications, allowing technical personnel to use their skills to solve the problem.  He also shares a timeline for starting a recommendation algorithm. The process includes writing, pulling in data, creating a data environment, scoring, and algorithms. Another consideration is change management which involves limited pilots and controlled AB tests across the population, and time allotted to roll out and testing.  Phil discusses the power of AI in data analysis, stating that it can provide insights and interactions that are not always available before. The real power lies in bringing new agents to speed up the process and elevating the performance of middle-tier agents. The lower performing agents often wouldn't use the tool, so they don't see as much impact.  Timestamps: 00:02 Using AI to analyze unstructured dat