Kitchen Side: AI Search Tactics, Telemetry and Team Structure

Listen to the Podcast

In this Kitchen Side episode of The Long Game Podcast, Alex and David are joined by Nick Lafferty from Profound to unpack how teams are navigating AI search visibility amid shifting metrics, attribution challenges, and unclear best practices.

They discuss how companies choose which prompts to track, why case studies in AI search are hard to define and share, where brand and citations fit into AI-generated answers, and what organizational bottlenecks are preventing teams from acting on AI search insights.

Key Takeaways

• Prompt selection matters, but most teams underestimate how much customer language and internal feedback should shape what they track in AI search.
• AI search case studies are difficult to standardize because visibility depends heavily on prompt framing, attribution models, and competitive sensitivity.
• Revenue and self-reported attribution remain the most reliable signals as clicks, impressions, and rankings become less dependable.
• Problem-based prompts frequently surface brand recommendations, even when users don’t explicitly ask for tools or products.
• Citation share acts as an influence layer, shaping future AI responses even when a brand isn’t directly recommended in the output.
• Brand-building activities upstream of content can meaningfully impact AI visibility by associating a company with specific problem spaces.
• AI search ownership is increasingly cross-functional, spanning growth, SEO, PR, comms, and product marketing rather than a single team.
• Internal resourcing and approval processes are major bottlenecks, especially for off-site efforts like Reddit and YouTube.

Show Links

• Visit Profound on LinkedIn
• Connect with Nick Lafferty on LinkedIn
• Connect with David Khim on LinkedIn and Twitter
• Connect with Alex Birkett on LinkedIn and Twitter
• Connect with Omniscient Digital on LinkedIn or Twitter

Time Stamps

• [00:00] – Introducing AI search telemetry and prompt tracking challenges
• [04:10] – Why AI search case studies are hard to define and share
• [09:30] – How teams should decide which prompts to track
• [16:40] – The blurring line between keywords, prompts, and user behavior
• [23:50] – Problem-based prompts and unexpected brand recommendations
• [30:20] – Citation share as an influence and leading indicator
• [38:10] – Brand, PR, and off-site signals in AI visibility
• [45:30] – Organizational bottlenecks limiting AI search execution
• [52:10] – YouTube, Reddit, and emerging off-page visibility channels