10 Ways Clay's GTM Engineers Use AI to Accelerate Sales
A behind-the-scenes look at the AI toolstack and workflows a Clay rep uses every day
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Dear GTM Strategist,
We talk endlessly about AI in sales, but most of it is people theorizing about what teams should be doing. I wanted the opposite: someone selling at a fast-growing AI-native company to show me exactly what sits on their screen every day. So I asked Alex Lindahl, who was one of the 1st GTM Engineers at Clay, to share how the team uses AI in their day-to-day.
Alex is one of my favorite people to learn from on this topic. He has been on the founding GTM team of eight Series A startups, four of which became unicorns, and he spends his days building the systems the rest of us read about. We first teamed up on “Is GTM Engineering the way to scalable growth?” back in 2024, and this year we published the 2026 State of GTM Engineering report together - the first real benchmark on how this role works in practice. He writes the GTM Engineering Newsletter and also just launched a YouTube show on AI in GTM.
Alex walks you through Clay’s full AI sales stack, the exact tools their reps reach for, plus 10 concrete ways they put AI to work. Some of my highlights include:
generating on-brand decks from a single prompt,
spinning up custom apps for each customer,
clearing the Salesforce chores nobody wants to touch.
It is a rare look behind the curtain at a company most of us are trying to learn from. Copy the whole stack or steal one workflow. Over to you, Alex.
Day in the life
The alarm goes off at 6:47 am. Marcus doesn’t hit snooze. He’s a Clay sales rep, and he’s been in this game long enough to know that by the time he gets to his desk, the day is already in motion.
8:30 am: Before the First Sip of Coffee
There’s a Slack notification waiting. It lands every Monday at 8:30 am without fail. The message is from a Claude routine his team built in-house. Over the weekend, new users signed up at three of his target accounts. The routine found them, charted the signup velocity over time, enriched their profiles, and drafted the outreach hooks. Marcus reads the hooks. One of them is actually sharp. He makes a small edit, approves it, and keeps moving. This used to take him an hour. Now it takes four minutes.
9:15 am: The First Real Conversation of the Day
Marcus has a discovery call with the Chief Revenue Officer at a mid-market SaaS company. Before the call, Marcus fires up his Claude Skill that connects to Clay’s MCP. Marcus scans it on his walk from the kitchen to his desk. He already knows the company’s GTM tech stack, the VP’s last three public appearances, and where they’re likely bumping into friction. The call itself is easy, because Marcus isn’t winging it.
10:45 am: Building Something Bespoke
He’s in a POC with a fintech company. Their brand is dark navy, gold trim, serious, so they’re not going to be impressed by a generic slide deck. Marcus opens Lovable, pulls the call transcript from Granola’s MCP, and builds a mini app - their branding, their data from the test, their numbers. 20 minutes. No design team, no back-and-forth on colors. He sends the link before the VP’s next meeting starts.
11:30 am: The Legal Fog Lifts
The deal is moving, and then legal gets involved. The MSA redline comes in. Marcus would normally wait two days. Instead, his team routes it through Crosby - AI reviews the document first and flags what’s important, so the lawyer sees only what actually matters. Legal gets back in four hours instead of two days. The deal keeps moving.
12:20 pm: Lunch and a Question He Couldn’t Answer Yesterday
A CISO asked about Clay’s security documentation. Marcus types the question into Slack and tags Dust, which is connected to Notion, Google Drive, and Salesforce. The answer comes back in the thread - sourced, specific, ready to forward. He adds a line of context and sends it before finishing lunch.
This newsletter is sponsored by 1mind.
While mainstream B2B is still debating whether AI-native GTM is a real thing, Kyle Norton went and grew Owner from roughly $2M ARR to nearly $100M ARR in under four years by breaking almost every rule in the traditional CRO playbook.
45 days in, he cut half the sales team. He hired a VP-level RevOps leader when everyone said it was far too early. He audited his own hiring and found the polished case-study interviews were inversely correlated with who actually closed. And while every vendor was raving about “AI SDRs,” he called most of it a race to the bottom.
No wonder he’s one of the first names that comes up when people ask who’s actually getting results with AI-native GTM.
On July 29th, he sits down with 1mind’s Jonathan Kvarfordt for the 1mind Executive Playbook Series. He’s covering the roster he hires for in 2026 (two of the roles didn’t exist three years ago), the org formations that win in the AI era, why he manages throughput instead of activity, and the anti-patterns that are stalling “AI-forward” teams.
Live Q&A at the end, and there may be some skill md files and other goodies for the people who show up. Recording goes out afterward, but the questions are only answered once.
If you run a sales org and your forecast still lies to you - block the hour.
2:00 pm: The Slide Deck Problem, Solved
A QBR prep for a global account. Clay’s design team built their full style guide into Claude Design. Marcus prompts it and the slides come back - on-brand, structured, with the workflow baked in. He adjusts one section. The rest is ready to present.
3:15 pm: A Call That Matters
Marcus gets on a call with a founder. He does his best work here: present, asking real questions, listening the way you only can when you’re not thinking about what you have to do next. When it’s done, the call (routed to Gong) is scored on the 3Ts (Tailor, Teach, Take control). He reads the feedback and files it away. A second notification: recommended Salesforce updates based on the call - deal stage, next steps, contact role, company notes, all populated. Marcus hits approve with no need to enter Salesforce.
4:30 pm: An Event That Would Have Been Missed
There’s a Clay customer dinner next month in Chicago. Marcus covers a territory with a dozen companies headquartered there. He doesn’t have to figure out who to invite - Clay pulled the right executives (VP level and above, active) and the list surfaced in Salesforce. Marcus reviewed the names, approved the outreach, and added a personal note to two of them. The rest were handled.
5:45 pm: What Actually Changed
Marcus closes his laptop and thinks about what his day looked like two years ago. He was an AE and an SE. Simultaneously, he built demos, did research, and managed all of the sales admin overhead. The actual relationship work, the part only a human can do, got squeezed in around the edges. Now the overhead is handled with his judgment in the loop. The AI doesn’t close deals. Marcus closes deals. But the AI gives him almost a full day back each week to focus more time on moving deals forward.
Bonus use case from Clay’s top rep, Solange Levy
GTM Engineers on the team are system thinkers and tinkerers. One of my new favorite use cases comes from Solange Levy, who is Clay’s top rep. Her background is not what you’d expect either. She came into Clay from the private equity world with no sales background.
She built workflow to solve a problem every seller knows: deal context is scattered across Slack DMs, customer channels, texts, email, calls, and the CRM. After a full day of calls, reconstructing what happened and deciding what to do next meant piecing it all back together by hand.
Solange is using Codex to aggregate and keep all context for every deal in one place. Everything from internal and customer conversations, CRM data, web research, and custom signals built in Clay is aggregated, analyzed, and then turned into a daily action plan.
This is how it works. Each account gets a persistent workspace and a dedicated subagent. The subagent reviews primary sources, keeps the account’s deal folder current, and contributes to a daily refresh. A coordinating agent then aggregates every account update and surfaces the single most actionable next step to move each deal forward. She can also work directly inside any account to prep for a call, map the buying committee, build a tailored asset, or plan how to multithread. There’s no need to start from scratch.
Big to-dos are easy to remember. But it’s the deals that are usually won through smaller, well-timed actions. For example, sharing the right asset, answering a lingering question, giving a prospect a reason to re-engage. The daily summary keeps momentum across every deal, which is especially valuable when an account is still in an education phase rather than active buying.
Reps at other enterprises can replicate the system because the operating model is transferable: a persistent, consistently organized workspace for every account, agents grounded in primary sources with the supporting evidence preserved, context refreshed on a useful cadence, and a coordinating agent that turns updates into priorities. The two principles that you want to keep in mind are that you should separate recommendation from execution (AI can nail the strategy, but sellers need the underlying evidence to verify before acting). The next step is to make this multiplayer so AEs, BDRs, SEs, and sales leaders all work from the same shared context and institutional memory.
The stack behind the workday
Clay - Data enrichment, workflows, orchestration, agents, context layer.
Claude - Custom skills that connect to Clay’s MCP, Granola, and others
Claude Design - Collateral and slide deck generation with our built-in design system
OpenAI Codex - Operating system for building a personal context layer for deal execution
Granola - Call capture and generated summaries.
Lovable - Vibe-coded customer ROI, POC, and other bespoke apps.
Crosby - AI legal with counsel in the loop.
Dust - On-demand knowledge base, plugged into everything for rapid answers on account, product, company knowledge.
Superhuman - Faster inbox management.
Slack - Where all of it surfaces: approvals, briefs, scoring, updates.
None of these tools replaced Marcus. They just gave him his day back.
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