For GTM folks, Agents + LLMs isolated within their respective tools have a fatal flaw... They have no context, no awareness, or understanding of your customer, or your GTM. Take HubSpot's release of the Claude connector. "Finally, AI that understands our business!" Except it doesn't. It understands what's in your CRM - but it's a walled garden. It has no context as to 'WHY'. Nevertheless, it'll happily spit out answers to whatever you ask (PSA: validate the responses, folks). –– Here's the examples I'm seeing folks use with HubSpot + Claude: ↘ "Show me pipeline for Q4" → Gets CRM data, misses usage decline ↘ "Which deals are at risk?" → Checks pipeline stage, ignores champion departure ↘ "Who should I target?" → Pulls contact lists, doesn't know who your best-performing segments are Context without unified intelligence is just faster access to fragmented data. –– These AI chat bolt-ons are just creating more noise, and more chaos. This is how we should be approaching AI-native GTM: ↳ Systems that learn: Every customer interaction, win, and loss improves future decisions ↳ Unified intelligence: Infrastructure embeds AI throughout, connecting teams, tools and agentic workflows ↳ Signal-driven execution: Relevant, evidence-backed signals surface risk and opportunity across the customer lifecycle ↳ Context orchestration: Leaner GTM teams making decisions enabled by real-time context orchestration A core principle of AI-native GTM is Context Orchestration. So, here's how we engineer context at Revenue Labs: 1️⃣ Raw data at source Connect directly to usage analytics, call transcripts, support sentiment, web activity, stakeholder changes... Bypass CRM data quality entirely. 2️⃣ GTM Knowledge Graph Your ICP, personas, customer journey, value map, sales process etc. into a single intelligence layer. This is where AI learns how you operate. 3️⃣ GTM Performance Analysis Real-time analysis across the customer lifecycle reveals what factors actually drive outcomes in your business. Context = Raw Data + Knowledge Graph + GTM Analysis Now when your Agent or LLM runs, it understands: ↳ Your ideal customer profile (based on actual win patterns) ↳ Decision-maker dynamics (from your successful deals) ↳ Message-market fit (from conversion analysis) ↳ Why you win deals (from win/loss) ↳ Expansion triggers (from usage and engagement data) ↳ Churn reasons (based on departed customers) So now you can ask questions that move the needle: ↗ "Which segments drive the highest LTV based on our data?" ↗ "Score these accounts on expansion potential using our success patterns" ↗ "Show me churn risks using our historical indicators" ↗ "Where's the friction in our sales process and what's causing it?" ↗ "Which accounts match our best customer profile and show buying intent?" It's about building GTM systems that understand your business, learn from every interaction, and compound intelligence over time. Follow for more on building AI-native GTM.

[diagram](<https://media.licdn.com/dms/image/v2/D4E22AQEhwrzytGCcrQ/feedshare-shrink_2048_1536/B4EZhlJ2bTHgAo-/0/1754043738748?e=2147483647&v=beta&t=u3854qHxquvm4NextCxJh2Z5JS-mA8hJwR-k6GbtNNE>)