Observed Signal · Mar 18, 2026 · Thought Leadership · Source: https://martech.org/feed/ · Impact: 2/5 · Sentiment: Positive
Mastering Language and Signals: B2B's Go-To-Market Edge
The article argues that in B2B purchasing the vendor that shapes buyers’ language and understands causal reasons for purchase wins before formal evaluation begins. Citing Daniel Kahneman’s System 1/System 2 framework and industry research (6sense’s 2025 Buyer Experience Report, Forrester), it claims most enterprise deals are decided before the funnel and that marketing lost strategic ownership by ceding customer research and measurement to product, sales and finance. The author introduces a 4S Framework (state, scale, system, signal), saying durable go-to-market advantage comes from owning 'state' (category language) and 'signal' (why revenue happens). With AI automating execution, the piece recommends marketing reclaim strategic research and measurement that tie programs to revenue.
Strategic analysis highlighting the need for marketing to reclaim customer research and causal measurement; signals implications for MarTech investment and measurement practices as AI automates executional work.
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Key Takeaways & Evidence Grounding
- Article asserts most B2B enterprise deals are decided before formal vendor evaluation begins.
- Cites 6sense’s 2025 Buyer Experience Report claiming the winning vendor is on the buyer shortlist 95% of the time before the first sales conversation.
- Introduces the 4S Framework identifying four sources of GTM advantage: state, scale, system and signal; marketing directly controls state and signal.
- Uses Gong as an example of owning category language (reframing call recording as 'revenue intelligence').
- Argues AI is automating executional System 2 work, increasing the strategic importance of owning buyer language and causal measurement.
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AI Scaling the Wrong Part of Go-to-Market
The article argues that marketers and sales teams have focused AI on increasing volume—more emails, more prospecting—rather than on the preparatory work that enables human-led, relationship-driven selling. Citing industry research (Salesforce, 6sense, Gartner, ZoomInfo), the author says buyers often self-initiate and prefer human validation of AI insights; deals are commonly decided by familiarity and prior relationships. The recommended approach is to use AI to automate research, signal synthesis, data enrichment, prioritization and prediction (the “prep”), then allow humans to handle storytelling, judgment, and sending personalized outreach. The author positions 1:1 ABM and “signal-based experiences” as the highest-value GTM motion that benefits from machines doing the heavy prep while humans preserve trust and nuance.
AI Filter Alters B2B GTM Strategies
The article argues that LLMs and AI assistants have inserted an "AI filter" between B2B brands and buyers: buyers increasingly ask AI tools (ChatGPT, Perplexity, Gemini, Microsoft Copilot) before traditional search. LLM outputs draw on a weighted training corpus and favor brands that appear across a dozen high‑value surfaces (editorial outlets, Reddit, Wikipedia/Wikidata, LinkedIn, review sites, developer platforms, structured FAQ pages, etc.). The author outlines common visibility gaps (Wikipedia, Reddit, review language, knowledge graphs, developer surfaces), a five‑prompt audit to check AI surface presence, and a prioritized 90‑day activation plan (quick wins, structural fixes, compounding actions). The post is by Megan Kessler, Founder & CEO of Dark Horse Strategies, and links to an open-source AI GTM Visibility Framework on GitHub.
CMOs Can Shape AI to Boost B2B Buying Power
As B2B buying increasingly becomes machine-mediated, AI systems are conducting early vendor research and determining shortlists by reading structured signals (schema, integrations, certifications, pricing). The article argues CMOs can elevate marketing from promotion to infrastructure by shaping how AI evaluates vendors. Recommended actions include auditing structured metadata and schema markup, aligning marketing claims with verifiable documentation, participating in enterprise AI governance, tracking brand presence in AI-generated recommendations (an "AI shortlist share" metric), and standardizing category language across product, sales, and marketing. The piece frames marketing as responsible for consistent, machine-readable representation of product truth and buyer relevance to influence vendor selection before sales engagement.
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