From Work Slop to Agentic AI: Making Sense of the Latest Marketing AI Tools

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 modelsOverview of major shifts since the last episodeHow marketers are adapting to new AI capabilities

AI Reasoning Models

Difference between chain-of-thought prompting and modern reasoning modelsImprovements in accuracy and reduced hallucinationsTrade-offs between speed and reasoning depthGroq CEO’s insights on the value of ultra-fast inference

AI Tools Adoption and Platform Maturity

Microsoft Copilot’s leap from basic to highly capableKey tools: Researcher agent, Analyst tool, and Copilot StudioIntegration across Microsoft 365 (SharePoint, OneDrive, Teams)Comparisons with Google and OpenAI’s platformsOngoing confusion over pricing and value

Model Selection: The “Model Roundabout”

Recent advances in GPT, Claude, Gemini, and open-source modelsBalancing reasoning and instant modesCommon use cases: coding, summarisation, planning, and copywritingQuirks such as GPT-5’s writing tone and output styleTips for reducing hallucinations and improving reliability

Open vs Closed Source AI Debate

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

AI-Integrated Hardware and the Attention Economy

Growth of wearable AI, e.g. Meta’s Ray-Ban smart glassesPrivacy and social implications of constant recordingAdoption driven by convenience and content habitsMeta’s competing aims: productivity vs attention monetisation

Agentic Progress: AI Agents and Automation

“Agentic AI” explained: systems acting autonomously to complete goalsFrom document retrieval to full workflow automationTools like Make.com, Zapier, and N8N enabling marketersClaude Code as an advanced example of self-directed agentsUse cases: automated slide decks, proposals, and scheduled reporting

MCP (Model Context Protocol) Connectors

Overview of MCP for connecting LLMs to CRMs and cloud toolsMartin’s experience linking Claude to HubSpot and Google WorkspaceExamples of AI updating pipelines and deal notes automaticallyBenefits 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 expertsAdvances 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 soundReflection on low-quality AI output and the lasting importance of trusted brands