He automates ICP scoring using Clay & AI workflows

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Show Notes

Stephen Bates, founder of Dubai-based Cabot Insights, explains how to implement AI in GTM without falling for workflow porn — the overly complex, flashy automations that look impressive on LinkedIn but rarely deliver ROI. Stephen shares his approach to automated ICP scoring using Clay and LLMs, where he builds systematic lead qualification workflows that score prospects against multiple criteria before any human touches the lead. He discusses how to use AI to research companies, extract signals, and generate qualification scores at scale, while keeping the workflows simple enough to maintain and debug. The conversation covers the foundational importance of ICP definition before building any automation, how to avoid the trap of over-engineering workflows, and why the best GTM systems prioritize simplicity and reliability over complexity. Stephen also shares insights from working with high-growth startups in the Middle East market, and discusses how the GTM engineering landscape is evolving as AI tools become more powerful and accessible.

Key Takeaways

• Most teams fail from poor ICP definition not lack of tools
• Automated ICP scoring with Clay and LLMs scales outbound
• Avoid 'workflow porn' — focus on outcomes not complexity
• GTM engineering agencies help startups skip the learning curve

Guest

Stephen Bates — Founder, Cabot Insights

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