Talking AI in Market Research

In this Talking AI episode, co-founders Ray and Will Poynter break down the rise of Deep Research—an AI workflow that combines live web search, long-context reasoning and citation-linked summarisation. What You'll Learn What “Deep Research” Means Why multiple vendors (ChatGPT, Gemini, Perplexity) use the same term How it differs from standard chat prompts or Canvas sessions Speed vs. Depth Trade-offs 178 web hits & 30 sources in < 10 min: when the wait is worth it When iterative Canvas-style prompting is still faster Source Quality & Hallucination Control Forcing reputable domains, spotting blocked sites (e.g. BBC, pay-walled press) Using the final summary first, then drilling into citations Practical Use-Cases Entering new verticals (e.g., canned-coffee market in Japan, TfL transport data) Generating monthly polling digests, executive briefings, podcast scripts Limitations & Work-arounds Verbosity, missing premium sources, daily search caps on free tiers Future outlook: personalised memory, task-based scheduled research, dashboard feeds Key Takeaways Deep Research = AI research assistant on steroids—ideal for zero-to-sixty topic ramp-ups. Quality in, quality out—specify sources and always sanity-check citations. Iterate smartly—use summaries to steer; don’t wade through 20 pages blind. Free to start—Perplexity’s free tier offers one robust run per day; ChatGPT-4o currently leads on depth and reasoning but costs more.

In this deep-dive episode of Talking AI, co-founders Ray and Will Poynter unpack the concept of Vibe Coding—a term coined by Andrej Karpathy to describe using AI to generate executable code. They separate hype from reality, explore real-world productivity studies, and forecast how AI will reshape both professional and hobbyist coding. What You'll Learn Defining Vibe Coding: Understand the broad definition of AI-generated code and why it's more than just hype. Impact on Professional Developers: Shift from boilerplate implementation to AI oversight, security, and system design New skill sets for debugging AI-generated code versus human-written code AI Pair Programming: Why “AI Pair Programming” is a more positive framing than Vibe Coding Best practices for collaborating with AI as a coding partner Tools & Platforms: Recommendations for beginners (Google Colab + ChatGPT/Gemini) Enterprise-grade environments (Codex, Cursor, Windsurfer) Prototyping vs. Production: How AI accelerates prototyping Why human review remains essential for production-quality code Visual & UX Design with AI: AI's growing competence in HTML/CSS layout and styling Limitations in non-code visual design (e.g., PowerPoint) Key Takeaways AI isn't replacing all coders—it augments them. Security, compliance, and originality become critical new frontiers for human engineers. AI Pair Programming fosters a collaborative workflow: AI generates and engineers review, refine, and integrate. Choose your tools wisely—start with Colab for learning; explore Codex or enterprise agents for dedicated development. Whether you're a seasoned developer, an insights professional experimenting with Python/R, or simply curious about the future of software creation, this episode equips you with actionable insights and tool recommendations to ride the next wave of AI-powered coding.

In-Depth Exploration of AI, Behavioral Science, and LLMs In this compelling episode of Talking AI, host Ray Poynter sits down with Elina Halonen, a pioneering behavioral strategy consultant and founder of Prismatic Strategy. With almost 20 years of experience in consumer insights and over a decade specializing in behavioral science, Elina offers a unique lens on how human behavior intersects with artificial intelligence. What You'll Discover: Behavioral Strategy & AI: How decades of expertise in human behavior are applied to unlock the full potential of AI and large language models (LLMs). LLMs & Cognitive Science: An exploration of how machine learning models mimic human thought processes, and the implications for AI adoption. Ethics and Bias in AI: A deep dive into the ethical challenges, representation issues, and bias that influence AI’s role in society. Cultural & Linguistic Influences: Insights into how language, cultural nuances, and communication shape AI behavior and trust. Practical Applications: Real-world examples of using AI as a collaborative thinking partner to enhance productivity, creativity, and decision-making. Join us for a thought-provoking discussion that bridges behavioral science with cutting-edge AI trends. Whether you're a tech enthusiast, a professional in digital transformation, or simply curious about the future of AI, this episode is packed with SEO-rich insights to keep you at the forefront of innovation.

Join us on Talking AI as host Ray teams up with guest Paul Marsden to explore the rapidly evolving world of artificial intelligence and digital innovation. This episode is packed with insights on how AI and machine learning are transforming industries, enhancing business strategies, and driving global digital transformation. In This Episode: AI Evolution: Learn about the latest trends in AI and how they are reshaping industries. Machine Learning & Data: Discover practical applications of machine learning and data-driven decision making. Future Tech Insights: Gain expert predictions on emerging technologies and digital innovation. Ethics & Regulation: Understand the ethical challenges and regulatory issues surrounding AI development. Strategic Innovation: Explore actionable strategies for leveraging AI to gain a competitive edge. Whether you're a tech enthusiast, business leader, or industry professional, this in-depth discussion provides valuable insights into the future of technology and innovation. Enhance your understanding of AI and prepare for the digital changes ahead. About the Guest: Dr Paul Marsden is a consumer psychologist known for his research on emerging technology and mental health. He featured in the award-winning film on teens and tech, I Am Gen Z, and currently researches how AI shapes wellbeing, creativity, and performance. Paul lectures in consumer psychology and positive psychology - the science of happiness - at UAL, and advises global brands through Brand Genetics on positive innovation, using insights from positive psychology to develop products and experiences that enhance wellbeing, performance and flourishing. He co-founded Brainjuicer (now System1 PLC), one of the first agencies to apply AI in market research, and is chartered by the British Psychological Society (BPS).

In this episode of Talking AI, hosts Ray Poynter and Will Poynter from ResearchWiseAI sit down with Andrew Jeavons, co-founder of Signoi, to delve into the world of AI personas and how they’re reshaping market research. Discover why companies are using personas to bring segmentation data to life, the importance of privacy safeguards in synthetic research, and how AI can quickly (and cost-effectively) assist with concept testing—all without sacrificing data integrity. Andrew also sheds light on quantitative semiotics, the interplay between AI and image analysis, and the ethical dilemmas posed by digital twins. You’ll hear about real-world use cases for persona-driven insights, the challenges of ensuring bias-free outputs, and a look at how RAG-based retrieval is transforming the way brands update their AI with new findings over time. If you’re curious about how generative AI, synthetic data, and personalized consumer simulations can drive innovation while respecting anonymity and data security, this is an episode you won’t want to miss. Tune in to stay ahead of the curve in AI, personas, and the evolving landscape of consumer research. Be sure to check out our related episodes on RAG and synthetic data for even more insights into the future of artificial intelligence.

In this episode of Talking AI, Ray and Will Poynter—co-founders of ResearchWise AI—take a deep dive into the world of custom GPTs. They explore how these tailored AI assistants allow users to add unique instructions, integrate proprietary knowledge bases, and access specialized tools without requiring extensive coding skills. Ray and Will discuss how platforms like OpenAI’s Custom GPTs and Hugging Face Assistants let both novices and experts rapidly prototype AI-driven solutions for tasks ranging from HR and compliance to market research and client engagement. A key focus in the conversation is how custom GPTs differ from conventional AI chatbots. Ray and Will emphasize the possibility of enhancing a standard AI model with company policies, proprietary datasets, and multi-agent frameworks for more powerful interactions. While custom GPTs provide a swift, no-code entry point, they also address practical concerns such as data privacy, usage caps, and the need for hosting accounts. Drawing on their own experience with projects like “Virtual Ray,” Ray and Will show how creating an AI prototype in minutes can accelerate the product development cycle—yet, over time, more robust, coded solutions may be necessary for advanced or highly specialized tasks. For market researchers, the duo highlights exciting applications. From testing discussion guides with synthetic personas to creating interactive reporting tools, custom GPTs can make research findings more accessible and engaging. Ray and Will also touch on the broader AI ecosystem, mentioning how commercial software and big tech players are increasingly integrating custom GPT capabilities to attract users and streamline creative workflows. Lastly, they note that while custom GPTs can quickly fill gaps in knowledge or workflow, they come with limitations. Building a fully bespoke system allows for deeper control, stronger security, and the ability to implement complex chain-of-thought or multi-agent reasoning. Yet for many businesses, starting with a low-code or no-code solution is a strategic first step to validate ideas and scale later. Whether you’re a market researcher curious about speeding up insights generation, an HR professional looking to centralize internal knowledge, or an AI enthusiast seeking to prototype new apps, this conversation breaks down the value, practicality, and potential pitfalls of custom GPTs. Tune in to hear Ray and Will share insider tips, real-world examples, and a vision for how custom GPTs can reshape the future of AI-driven decision-making—only on Talking AI.

In this episode of Talking AI, Ray and Will Poynter, co-founders of ResearchWiseAI, discuss the practical uses, challenges, and future of synthetic data in market research. Synthetic data is defined as data created rather than collected, with Ray clarifying three main categories currently relevant to the market research industry: Augmented Synthetic Data: The most common approach in market research, augmenting existing survey data by adding synthetic cases to fill gaps, improving upon traditional weighting methods. It is valuable for reducing cost and time, particularly addressing the last 10-20% of data collection that typically incurs the greatest expense. Personas: Qualitative or quantitative synthetic entities representing customer segments or groups, such as loyalists or trialists. These are interactive personas that help brand managers generate insights, ideas, and strategies through simulated dialogue and creative brainstorming. Fully Synthetic Data: Entire datasets created synthetically, bypassing traditional data collection entirely. Though not yet widespread due to concerns about efficacy and trust, this method offers significant potential for privacy protection and rapid analysis. Ray and Will highlight practical advantages of synthetic data, such as faster turnaround times, reduced costs, and enhanced data security and privacy. Synthetic data originated partly to address privacy issues—like adding noise to census data—and continues to offer strong security benefits by replacing sensitive personal information. However, concerns about synthetic data persist. Ray emphasizes the primary industry worries include questions about accuracy, validation methods, and reliability across different contexts. The lack of standardized validation techniques to assess synthetic data accuracy remains a critical hurdle. Ray advises that validation should ultimately focus on whether synthetic data supports effective business decisions. Discussing future trends, Ray and Will predict significant growth for augmented synthetic data and interactive personas, driven by increased industry acceptance and regulatory clarity. They foresee augmented data increasingly replacing traditional weighting, while personas evolve into dynamic tools allowing brands to simulate interactions with target audiences in real-time. While fully synthetic data may face limitations, especially if overused without fresh data collection, Ray suggests it could eventually eliminate traditional surveys by directly leveraging AI’s deep understanding of consumer behavior and business questions. However, this approach might become obsolete if AI systems reach a point where they directly generate insights without needing traditional survey structures at all. To conclude, Ray and Will encourage careful, validated adoption of synthetic data approaches, underscoring their potential to transform market research by speeding processes, enhancing privacy, and generating richer, more actionable insights. Tune in next week for another episode of Talking AI.

In this episode, Ray and Will discuss the basics of RAG (Retrieval Augmented Generation). What is RAG? What is a RAG AI good for? And why are some people dismissive of it.

In our fourth installment, Ray and Will discuss how to embrace generative AI to boost your qualitative analysis. Ray shares his experience with the latest tools and techniques coupled with his 45 years experience in the field of qualitative analysis.

What is Google's answer to ChatGPT? Ray and Will discuss Google Gemini and its implications for the market research AI landscape.

Ray and Will from ResearchWiseAI talk about agents—what they are, how they work, and where they fit into market research. We explain the difference between autonomous agents, like a Roomba or Grammarly, and reactive agents powered by advanced LLMs such as ChatGPT Operator. We also discuss how agents can handle tasks such as dynamic data collection, cleaning, and analysis by autonomously selecting the right tools for the job. Tune in to learn more about how AI agents are evolving and how they might eventually assist with day-to-day research tasks.

This is the first episode in a series we are calling Talking AI. In this episode, Ray and Will discuss how and why you might want to download AI models on to your computer.