The Wave Theory of GTM AI Adoption

By Ryan Vanshur

Also published on the Guild Letter: Read the original issue.

Subscribe now Your team didn’t adopt AI because someone gave a presentation about it. They adopted AI because a rep had a call in 40 minutes with a VP of Operations at a $200M general contractor, and they had nothing. No context on the account. No intel on the stakeholders. No idea what the competitive situation looked like. So they opened an AI tool and asked for help. That’s how it starts. Not with a strategy. With a pain point. Endgame released their 2026 AI Benchmark Report earlier this year, and the data tells a story most people are going to miss. The report covers 31,000+ real AI interactions from hundreds of GTM professionals. Not survey data. Not “how do you feel about AI.” Actual behavioral data showing what people do when they sit down with an AI tool. The headline finding is that selling-oriented jobs make up 53% of all AI activity . That’s interesting. But it’s not the insight. The insight is buried in the breakdown. The Stat Everyone Will Overlook Five jobs-to-be-done emerged from the data: Most people will look at Team Enablement sitting at 36.3% and think that’s the story. It’s not. The stat that matters is 22.8% for Account Intelligence . And the reason it matters has nothing to do with account research. Account Intelligence is the entry point. It’s the job where teams learn to trust AI. Pre-meeting research. Stakeholder mapping. Competitive context. These are low-risk, high-reward tasks where the output is immediately verifiable. You ask AI about an account, and you can check the answer in 30 seconds. That verification loop is everything. It’s where skepticism goes to die. Once a rep has asked AI about 50 accounts and gotten useful answers 45 times, something shifts. The hallucination anxiety fades. The “I don’t trust it” objection evaporates. Not because someone convinced them. Because the evidence accumulated. And then the jobs-to-be-done start migrating downstream. The Wave AI adoption in GTM doesn’t happen all at once. It moves in waves, and Account Intelligence is the forcing function that breaks the first wave open. Here’s the pattern I’ve watched play out: Wave 1: Account Intelligence. The team has a specific, immediate pain point. Usually it’s pre-meeting prep or account context. A legal-tech rep needs the stakeholder map at a 30-attorney firm before a managing-partner call. A healthcare RCM rep needs the denial pattern history before a conversation with a billing director. A property management AE needs pre-renewal context on a multi-family operator who has gone quiet on the last two QBRs. A construction fintech rep needs the lien exposure on a general contractor before pitching credit risk. They solve it. AI becomes “that tool I use before calls.” Trust builds through repetition. Wave 2: Deal Acceleration. Once account research is a habit, reps start asking AI to help with the next step. “I know who the stakeholders are. Now help me figure out the deal strategy.” Content creation, value propositions, competitive positioning. The prompting behavior drifts from research toward action. Wave 3: Pipeline Monitoring. Leadership sees the reps using AI and starts asking different questions. “Can it tell me which deals are at risk? Can it flag the ones we’re not paying attention to?” Pipeline health scoring, deal monitoring, forecast inputs. The use case shifts from individual productivity to organizational visibility. Wave 4: Post-Sale and Renewal. The wave keeps moving. CS teams see what sales is doing and start pulling AI into onboarding, health scoring, expansion plays. The same trust-building loop that started with a rep researching an account before a call now runs across the entire customer lifecycle. We’re living this at Handle right now. We started with account intelligence. Reps using AI to research construction companies, understand their lien compliance exposure, map out the buying committee at a general contractor. Once that was working, the prompting behavior drifted toward deal advancement. “I know this GC has a lien deadline in 45 days. Help me build the urgency narrative.” Then pipeline monitoring. “Show me which deals haven’t had stakeholder engagement in two weeks.” Now it’s pushing into post-sale workflows. CS flagging renewal risks, catching gaps in product adoption, identifying expansion triggers. The wave is real. At least for us. And the Endgame data suggests it’s happening more broadly than people realize. The RevOps Multiplier There’s a second finding in the report that deserves its own conversation. RevOps represents 14% of users but drives 53% of Pipeline Inspection activity. That’s 4x their expected share. No other persona in the data shows that kind of disproportionate impact. Think about what that means. A small group of operators, the ones who understand both the data and the process, are the ones actually building organizational intelligence. They’re not just using AI. They’re building the infrastructure that makes AI useful for everyone else. This matches what I’ve seen across a decade of building GTM operations in vertical SaaS. At CourseKey, our RevOps function was three people managing the intelligence layer for a 47-person company. At Handle, I’m one person building AI systems that serve the entire revenue organization. The ratio is always lopsided because RevOps is an infrastructure role, not a user role. If you’re a RevOps leader reading this…the data says you’re the most important persona in the AI adoption story. You’re the one who turns individual tool usage into organizational capability. That 53% stat should be on your next slide when you’re asking for headcount or budget. What the Data Doesn’t Say (But Should) The report measures activity distribution. What it doesn’t measure is the sequence. And the sequence is where the strategic insight lives. Teams don’t wake up and decide to “adopt AI across the revenue org.” That’s not how behavior change works. They solve one pain point. Then another. Then someone notices the pattern and formalizes it. The wave moves because each solved problem creates trust for the next one. This has implications for how you roll out AI to your team: Stop planning top-down AI strategies. Start with the pain point your reps already have. It’s almost always account research or meeting prep. Solve that first. Do it well. Let the trust compound. Don’t buy a platform for all five jobs-to-be-done on day one. You’re going to overspend on capabilities nobody uses yet. Start with Account Intelligence. Expand as the wave moves through your org. The Endgame data shows that 37.5% of all activity is contextual understanding (Account Intelligence + Conversation Readiness). That’s your beachhead. Invest disproportionately in RevOps. The 14%-to-53% stat isn’t an anomaly. It’s a structural truth about how AI scales inside GTM organizations. Individual reps use AI. RevOps makes AI work. The 2x Context Ratio One more finding worth sitting with…teams spend 2x more AI time understanding accounts than producing deliverables . That’s not a productivity story. That’s a context story. The highest-value use of AI in GTM isn’t “write me an email.” It’s “help me understand what I’m walking into.” The data confirms what most experienced operators already feel. That context is the bottleneck, not content creation. Every vendor selling “AI-powered email writing” should look at this stat and reconsider their positioning. The market is telling you, through behavior rather than surveys, that understanding the account matters more than automating the outreach. The Scene When It’s Working You walk into Monday’s pipeline review. Before anyone speaks, the intelligence layer has already flagged three deals. Not because a rep raised a hand. Because the evidence changed over the weekend. One deal lost a champion who changed LinkedIn titles on Friday. Another has a lien deadline in 30 days that nobody had surfaced. A third hasn’t had multi-threaded engagement in three weeks despite being forecasted to close this month. Nobody ran a report. Nobody asked a rep for an update. The system caught it because the system was built to catch it. And it was built by the same team that started, six months ago, with a rep asking “tell me about this account before my call.” That’s the wave. It starts small. It compounds. And the teams that ride it early are going to be impossible to catch. Where is your team on the wave? Map your current AI usage to the four waves. Where did you start, and what job-to-be-done migrated next? Did it follow the pattern, or did your team skip a wave? Subscribe now Steal This: The AI Adoption Wave Audit Print this. Run it with your RevOps lead this week. What To Do Monday Where you scored is less interesting than what you do with the answer. Wave 1 Teams: make pre-meeting AI research a 3x-weekly habit before you add anything else to the stack. The trust loop closes faster than you think, and it has to close first. Wave 2 Teams: wire AI into deal strategy sessions, not just account briefs. The shift is from “research this account” to “what is the next move on this deal.” The reps who notice the difference are the ones who start asking AI questions you didn’t train them to ask. Wave 3 Teams: build signal-based scoring that flags at-risk deals without waiting for rep self-reports. The forecast gets honest the week you stop relying on commit numbers as the input layer. Wave 4 Teams: close the loop on expansion triggers before your CSMs catch them manually. The intelligence layer that runs across the lifecycle is what separates the teams who renew at 130%+ from the teams who renew at 95% and call it good. The wave compounds for teams that pick a next move and ship it this quarter. It stays theoretical for teams running their pipeline reviews the same way they did in 2023. If you are the operator actually building this in your org, the one who scored 9+ on the audit and already has a list of what to ship next, you are not alone. The Vertical GTM Guild is the community for operators building AI-native GTM in vertical SaaS. Compare notes with people running the same playbook. Skip the mistakes other operators have already paid for. Get the deeper breakdown on the four waves and the systems running underneath them. Join the Vertical GTM Guild → If you only do one thing after reading this… Print the cheat sheet, run the audit with your RevOps lead this week, and pick one next move from the list above. That’s the smallest first step. Everything else compounds from there.