GTM OpsKieran’s Substack - The AI Marketing Generalistby Kieran Flanagan
Stop outsourcing your marketing intelligence to AI. Do this instead.
Why I picked this
great simple visual
ai-writing-workflowspkm-workflowssecond-brainback-to-basics-gtmhuman-first-sales
“You can give two marketing leaders identical tools, identical models, and identical budgets, and they'll produce radically different outcomes based entirely on the quality of their judgment”
Key takeaways
- Marketing differentiation in the AI era comes from building a proprietary 'intelligence layer' - capturing judgment, learnings, and audience knowledge in systems you own, not outsourcing to generic AI models
- Marketing judgment (understanding customers, market dynamics, what breaks through noise) cannot be replaced by prompt engineering and is earned through practicing the craft, not derived from averaged AI training data
- David Ogilvy's practice of documenting every hard-won lesson in writing created institutional knowledge that outlasted his tenure - the same principle applies to building competitive moats against AI commoditization today
- Satya Nadella's warning about 'a frontier without an ecosystem' applies to marketing: feeding proprietary data/workflows into vendor AI models commoditizes your competitive edge across all users of those models
- The 'Marketing Intelligence Loop' framework positions judgment as input, proprietary intelligence layer as the system, creating compounding advantage versus competitors using identical off-the-shelf AI tools
Why this matters for operators: CMOs/marketing leaders building AI-native teams while maintaining competitive differentiation
I cover AI×GTM intelligence like this every Wednesday.
Get STEEPWORKS WeeklyMore picks
AI DevelopmentLenny's Podcast
Humans will keep inventing new reasons why we must stay in the loop with agents
- Human resistance to full AI autonomy is not purely technical—it's psychological and organizational; companies will rationalize keeping humans in decision loops even when agents are capable
- The 'human-in-the-loop' requirement may become a self-perpetuating narrative rather than a genuine necessity, driven by organizational risk aversion and change resistance
- Product leaders at scale (Notion) are observing this pattern, suggesting it's a widespread phenomenon across enterprise AI adoption, not isolated to specific use cases
ai-agent-adoptionhuman-in-the-loopai-governance
GTM Ops**RevOps Impact (Jeff Ignacio)
Comp plans for consumption pricing
- Consumption pricing fundamentally breaks traditional SaaS comp models—requires rethinking sales incentive structures around usage vs. contract value
- Four distinct contract structures exist (pay-as-you-go, uncommitted, committed, hybrid), each requiring different compensation mechanics and sales behaviors
- Enterprise consumption-based deals create tension: customers want flexibility, sales teams need predictability for quota attainment—comp design must bridge this gap
revenue-platform-consolidationconsumption-pricing-modelssales-comp-design
AI×GTMGTM OS: The Future GTM Operator
3 revenue motions your AI is only half wired into
- Model parity has arrived: OpenAI/Claude now trade evenly on core tasks, making 'better AI' a non-differentiator—the edge shifts to integration depth into existing revenue motions
- Waste is quantified: teams paying $17K-$37K/month for AI seats that never touch pipeline generation; real cost is opportunity cost of unused capacity, not subscription fees
- Lean teams have a structural advantage: cannot out-buy larger competitors on model access, but can out-embed them by wiring AI 1 revenue motion deep (pipeline → content → deals) with proprietary deal context competitors haven't seen
ai-sdr-adoptionrevenue-platform-consolidationback-to-basics-gtm
This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.