Why ChatGPT can’t solve business problems — Cameron Turner // Kin + Carta

Show Notes

• 01:36
  Cameron's data science journey

  Cameron began in inbound market research and founded Clickstream Technologies, later acquired by Microsoft for integration into Windows. Hes consistently emphasized aligning data analysis with business objectives for measurable impacts.
• 02:38
  ChatGPTs limitations in solving business problems

  ChatGPT can be valuable for specific business tasks, such as generating structured data, but it's not a one-size-fits-all solution for all business problems. It's essential to recognize that large language models provide predictions of the best answers and not definitive oracle solutions.
• 05:22
  The role of AI in solving specific business problems

  AI can indeed solve specific business problems, especially when questions are well-defined and data-driven. Having a clear hypothesis or question significantly enhances the likelihood of achieving measurable results in AI-driven solutions.
• 06:39
  The importance of cultural transformation before AI tool implementation

  AI can't replace the need for human involvement in situations involving ethical considerations. The misconception is that AI alone can foster a data-driven culture when it demands substantial cultural and process changes within an organization to maximize AI's business benefits.
• 08:59
  Examples of prioritizing cultural transformation and data governance over AI modeling

  Cameron helped a US food distributor centralize data while fostering a data-driven culture. The challenge was harmonizing data from multiple brands and optimizing data spending to create a single source of truth to generate value for each of their brands.
• 11:23
  Building a data ecosystem for business success

  A well-structured data ecosystem begins with harmonization and clear definitions, enabling easy access. This supports raw data, aggregation, integrated datasets, reporting, analytics, and AI for diverse business needs, guided by the business' insights.
• 13:17
  Leveraging AI and human expertise for maximum value

  Working with clients involves both building a data foundation and demonstrating AI's potential value through proof-of-concept initiatives. Solid data foundations are essential for managing data quality, accuracy, and model performance over time while aligning with specific use cases and scenarios.
• 15:46
  Managing AI expectations and the role of storytelling

  Storytelling is vital for addressing data quality and drift and acknowledging AI's limitations. Resetting expectations is necessary as AI can excel in some domains but may make errors, particularly when predicting human behavior in retail and consumer contexts.
• 18:18
  AI's role in individualized business insights

  AI's current level of automation empowers businesses to move beyond broad generalizations about their operations and markets. It enables individual-level insights and recommendations for customers, reshaping traditional practices like customer segmentation and mass marketing.
• 21:30
  Enhancing business operations through precise AI models

  Businesses are increasingly adopting precise models in their day-to-day operations. These models offer measurable outputs through AB testing, allowing companies to gauge the impact of predictions and recommendations on their overall operations effectively.
• 21:58
  The role of human expertise in ethical decision making with AI predictions

  Deciding what to do with predictive model results, especially in subjective and ethical contexts like loan approvals, often requires human domain expertise. AI lacks the understanding of contextual nuances in data, requiring human oversight to avoid ethical issues.
• 23:21
  Causation vs. correlation in AI analysis

  While GPT-4 can draw on its data corpus, it won't perform the detailed drill down necessary to understand complex relationships like zip code as a proxy for income. Distinguishing correlation from causation requires careful consideration and cannot solely rely on inference or historical data.