Show Notes: Barry Saunders, a digital expert at McKinsey, discusses his background in the firm and his experience in AI-related projects. He worked in the LEAP practice, which built platforms for video streaming, preventative maintenance, and optimization tools. He left McKinsey to become Chief Product Officer at an Australian fashion company and recently joined MXA, a strategic digital technology company in Australia. Barry suggests a two-by-two typology classification scheme for AI-related projects. The first quadrant focuses on understanding patterns of behavior, while the second quadrant focuses on predictive behavioral modeling, third is more about text orientated and understanding meaning. The fourth quadrant focuses on regenerative AI and content creation. Barry believes that combining these quadrants can lead to personalized content for different customers and valuable insights and can unlock interesting value.  AI Use Case Study Barry and his partner have been working on an AI toolkit to automate time-consuming work for management consultants. They developed a startup called First Things, which uses Gen AI to create classic McKinsey storylines from unstructured data. This tool has helped executives work through their strategies and report outcomes. They have also worked with clients on the AI journey, especially regulated industries. They have found that some tasks can be done more effectively with AI. One project they did was analyzing insurance policies for large-scale agricultural businesses, which are often complex and drift in meaning as language is updated. They created a tool that would analyze these policies, extract semantic meaning, and identify where drift took place, allowing them to align documents and simplify policies. One of the projects they are currently working on is simplifying lending policies for banks. In Australia, many lenders do home lending as their primary base, but the technical platforms used by banks and non-bank lenders are ancient and difficult to navigate. They are working on simplifying policies and offering home loans more simply. Building AI Tools The level of effort required to build a tool like this is not limited to building it. Many of the tools available are free, and there are many software as a service tools available that can perform similar tasks. To build a tool like this, one should be clear on what they are trying to do, such as simplifying a policy or comparing two different policies. The AI toolkit has proven to be effective in automating time-consuming work for management consultants and other clients. It is essential to be familiar with the tools and their capabilities to effectively utilize AI in various aspects of business operations. The legal space offers a vast array of tools for generating and analyzing contracts, including software as a service tools. To use these tools effectively, it is essential to be familiar with the large language model and the tool being used. Tuning these tools to get the desired response requires understanding the chain of logic and the outputs. To build a production-oriented tool, consider using large language model operations (LLM ops) or large language model operations (LLM ops) in a broader software architecture or workflow. Google, AWS, and Microsoft offer guides on how to integrate these tools into their software stack. It is crucial to be clear about the tasks and outputs of these tools, and to work with teams who are familiar with these systems.  Using AI Applications Barry discusses his work on AI applications, specifically RF cues and analyzing large documents. He built a proof of concept using a tool called mem.ai. He talks about a template he built to analyze questions in RFQs, which are often templated and consistent across government agencies. The system is particularly useful for handling open-ended questions and generating text about your company's services, processes, etc. This speeds the process of applications, and