In this episode of Artificially Intelligent Marketing, Martin Broadhurst and Paul Avery reunite to explore how AI has transformed marketing over the past 18 months. They cover reasoning models, agentic automation, Microsoft Copilot’s evolution, open vs closed-source AI, and the rise of AI-powered hardware—sharing real-world insights and examples from their work.

Major Evolutions in AI for Marketers

• Reflection on the rapid progress of AI tools and models
• Overview of major shifts since the last episode
• How marketers are adapting to new AI capabilities

AI Reasoning Models

• Difference between chain-of-thought prompting and modern reasoning models
• Improvements in accuracy and reduced hallucinations
• Trade-offs between speed and reasoning depth
• Groq CEO’s insights on the value of ultra-fast inference

AI Tools Adoption and Platform Maturity

• Microsoft Copilot’s leap from basic to highly capable
• Key tools: Researcher agent, Analyst tool, and Copilot Studio
• Integration across Microsoft 365 (SharePoint, OneDrive, Teams)
• Comparisons with Google and OpenAI’s platforms
• Ongoing confusion over pricing and value

Model Selection: The “Model Roundabout”

• Recent advances in GPT, Claude, Gemini, and open-source models
• Balancing reasoning and instant modes
• Common use cases: coding, summarisation, planning, and copywriting
• Quirks such as GPT-5’s writing tone and output style
• Tips for reducing hallucinations and improving reliability

Open vs Closed Source AI Debate

• Rise and stall of open models like DeepSeek and Llama 4
• Meta’s shift from open development to proprietary AGI
• Open source’s future in experimentation rather than frontier innovation
• Market consolidation, privacy, and trust concerns

AI-Integrated Hardware and the Attention Economy

• Growth of wearable AI, e.g. Meta’s Ray-Ban smart glasses
• Privacy and social implications of constant recording
• Adoption driven by convenience and content habits
• Meta’s competing aims: productivity vs attention monetisation

Agentic Progress: AI Agents and Automation

• “Agentic AI” explained: systems acting autonomously to complete goals
• From document retrieval to full workflow automation
• Tools like Make.com, Zapier, and N8N enabling marketers
• Claude Code as an advanced example of self-directed agents
• Use cases: automated slide decks, proposals, and scheduled reporting

MCP (Model Context Protocol) Connectors

• Overview of MCP for connecting LLMs to CRMs and cloud tools
• Martin’s experience linking Claude to HubSpot and Google Workspace
• Examples of AI updating pipelines and deal notes automatically
• Benefits balanced against setup complexity

Current State of AI for Marketers

• Honest look at AI-generated content and “work slop”
• AI as a speed and productivity enhancer, not a replacement for experts
• Advances in visual and video generation:
  • Faster, more consistent imagery (Midjourney, DALL·E 3, Nano Banana)
  • Real-world use in proposals, events, and social media
• Emerging video models (Veo 3, Sora 2, Kling) offering realism and sound
• Reflection on low-quality AI output and the lasting importance of trusted brands