Talking AI: Leveraging LLMs for Cultural Insights with Ramona Daniel

In this Talking AI episode, Ray Poynter welcomes Ramona Daniel—an independent cultural insights consultant with over 25 years in research—to discuss how generative AI has transformed her workflow. Ramona recounts her journey from telephone interviewer at Ipsos to leading a 79-market media study at Group M, and finally to striking out on her own in 2024. Facing the challenges of solo consulting, she turned to LLMs to replicate the role of missing team members, ask critical questions, and uncover patterns in cross-cultural data.

What You'll Learn

• Ramona’s AI Onboarding Story
  • Why she began experimenting with ChatGPT, Claude, Gemini, DeepSeek, Copala, and Perplexity
  • How she evaluated outputs, compared platforms, and found which tools delivered the best insights
• Practical AI Experiments
  • Turning raw PCA outputs from jamovi into interpretable narratives via ChatGPT or Claude
  • Using Perplexity as a natural-language search engine instead of traditional keyword Google searches
  • Converting dense research papers into audio “podcasts” with NotebookLM to quickly grasp key themes and cultural implications
• Lessons Learned & Pitfalls to Avoid
  • Why off-the-shelf LLM outputs can feel “middle of the road” and how to apply critical thinking to interrogate them
  • Which features (e.g., ChatGPT’s voice mode, Claude’s voice, Gemini’s latest modules) fell short and why
  • The temptation to shortcut learning—how to ensure AI tools complement rather than replace your own expertise
• Opportunities & Threats for Cultural Research
  • How AI’s pattern-recognition capabilities can surface unseen cultural signals beyond English-only sources
  • Inherent biases in LLM training data and the importance of human validation when interpreting cultural context
• Actionable Tips for Beginners
  • Experiment liberally: press buttons, type queries, see what happens, and fail fast
  • Don’t lock into one platform—test multiple LLMs (ChatGPT, Claude, Gemini, etc.) to find which voice aligns with your needs
  • Shape AI workflows to fit your unique process, rather than expecting plug-and-play solutions

Key Takeaways

1. AI as a Collaborative Partner—Use LLMs to fill gaps in your network, challenge your thinking, and highlight hidden data patterns, but always apply critical judgment.
2. Multimodal & Multilingual Research—Tools like NotebookLM let you transform PDFs, research papers, and non-English sources into digestible formats, expanding your cultural horizon.
3. Continuous Experimentation—AI platforms evolve rapidly; regularly revisit and compare ChatGPT, Claude, Gemini, Perplexity, and emerging tools to stay ahead.