I’m hosting a new kind of event for founders and GTM leaders. Instead of abstract presentations, we’ll offer real, interactive conversations and workshops with a few of my personal tech heroes: Scott Albro (former founder & CEO of Topo), Cristina Cordova, Chief Operating Officer at Linear, and Chris Degnan, first rep and former CRO of Snowflake. It will be live at LinkedIn in San Francisco so you be around your startup peers as we take on crucial topics in driving growth.

Here’s a preview of Scott’s session, which will be all about building your startup’s attention engine:

Together, we’re learn how to create demand, sharpen positioning, and build a repeatable GTM machine in competitive and volatile markets. We’ll also have a lot of fun. Anyone from the Scale community will be invited in.

Details below:

📆 Thursday, March 26

⏰ 1 to 6 pm PT

📍 LinkedIn Headquarters in San Francisco (222 2nd St, San Francisco, CA 94105)

Register here.

To reach one prospect, an SDR had to:

• Add the prospect in Outreach

• Pull first-party data from Snowflake, Salesforce, and Tableau

• Pull third-party data from ZoomInfo, Google, and LinkedIn

• Sort through 30,000+ Outreach sequences to choose the right fit

They repeated these steps dozens of times a day. Over time, reps got tired, skipped research, and reused the same sequences. This led to generic and inaccurate emails that prospects couldn’t relate to.

To fix this, Snowflake rebuilt its workflow using AI and orchestration to automate the most time-consuming parts. The results have been strong: more relevant emails now earn them a 7.6% reply rate (15x higher). What’s more, they’ve booked more than 2,000 meetings from just 55,000 prospects.

At Scale’s GTM AI Summit, Jeff Long, Senior Technical GTM Operations Manager at Snowflake, explained how his 300-person SDR team is using AI orchestration to send better outreach at scale, avoid burnout, and spend more time on pipeline-generating activities.

This article is based on Jeff Long’s presentation at Scale Venture Partners’ GTM AI Summit.

Use AI and orchestration to scale outreach

Many sales teams already use AI to quickly research prospects or draft cold emails. But if you’re just prompting your own LLM instance, you can only do this on one prospect at a time, which isn’t enough to help you manage hundreds of accounts.

Snowflake faced the same limitation. AI could help with individual steps, but it couldn’t support the volume their 300-person SDR team needed.

“We knew that AI could be incredibly powerful for our teams, but how do we make it scale without SDRs actually having to put prompts into a large language model and get these emails out?” says Jeff.

The answer was orchestration; specifically, with the integration platform Workato. Jeff describes Workato as “a layer that sits over your existing tech stack and connects tools that don’t natively talk to each other to perform different tasks one after the other, with the first action triggering each step automatically.”

Orchestration automates the sequence of tasks across your tools so the entire workflow runs on its own.

At Snowflake, this process starts in Outreach. When an SDR adds a prospect to a sequence, Workato pulls first- and third-party data automatically and sends it to Claude to draft the email.

Watch: Jeff describing how this workflow works

Validate your AI email drafts to avoid errors

One of the biggest lessons Snowflake’s SDR team learned was not to use the first draft of the email that AI generated. It might be accurate eight times out of 10, but there will always be drafts with irrelevant or inappropriate information. And when you’re prospecting thousands of people, those one-off mistakes add up to hundreds of emails that don’t resonate with the recipient or hurt your reputation.

Watch: Jeff on the importance of verifying emails

To avoid this, Snowflake added a validation layer that gives each email a 1-10 score and flags any draft below a score of seven for review. “The system reviews the draft for accuracy, tone, and sentiment, to make sure it’s right for the person that we’re sending it to,” Jeff says.

Once the rep has validated the email, Workato sends the final draft back into Outreach using XML.

Use XML to tag and extract key information consistently

AI outputs are often inconsistent. Even with good prompts, Claude or ChatGPT may add extra sentences, include headers you don’t need, or format things differently each time. This means you can’t use all the output, just certain parts, which adds back the time you saved from using AI in the first place.

Snowflake solved this by using XML to structure every AI-generated email before sending it back into Outreach. “XML is like a filing system with labeled folders organized into named sections that computers can easily read,” Jeff says. “It adds tags around the important parts of the email, so when you’re loading it back into Outreach, you get consistent outputs every time.”

With XML, the system extracts only the tagged fields—such as the subject line or body—and ignores everything else. This removes extra or unnecessary text and ensures the same structure every time. Instead of reps manually copying and pasting sections from an LLM response, the workflow handles it automatically and consistently across thousands of emails.

Here’s what an AI-generated email from this workflow looks like. It’s well-researched, relevant, and concise:

What Snowflake’s orchestration workflow looks like today

Snowflake’s orchestration workflow has expanded over time. In addition to drafting emails, the system now generates prospect briefs and call scripts for outbound calls, giving SDRs all the information they need in one place.

Here are examples of the brief and call script:

The entire end-to-end process—triggered by one action—replaces hours of manual research and writing for SDRs. Here’s what the advanced workflow looks like:

1. The SDR adds a prospect to a specific Outreach sequence, and then Workato takes over.

2. Workato pulls first-party data like product usage, account details, and internal insights an SDR would normally gather.

3. It pulls third-party data like company news, updates, and contact-level information.

4. It runs a web search through Bing to collect recent, relevant news.

5. It uses a Retrieval-Augmented Generation (RAG) workflow in Snowflake to identify relevant customer lookalikes to reference in the email.

6. Claude drafts the first email using all combined data.

7. The validation layer scores the email for accuracy, tone, and relevance, and flags anything that needs improvement.

8. Claude generates follow-up emails, a prospect brief, and a call script to support both email and phone outreach.

9. XML structures all outputs, and Workato loads them back into Outreach in a clean, consistent format.

This workflow originally took around 10 minutes when models were slower. Today, SDRs can add 20 prospects at once, and the system processes each one in two to three minutes, which is a huge time-saver.

Build specialized prompts to handle one task

A common mistake is asking one prompt to handle the entire workflow: research, summarization, email drafting, and follow-ups. Yes, the current AI models are powerful, but right now they work better when each prompt does one thing well and you orchestrate the tasks together.

So split the workflow into small, single-purpose prompts, each responsible for one clear task, and orchestrate them automatically with Workato or another tool of your choice. This improves the quality of each AI result and reduces how much manual work your team needs to do.

Test new workflows with a pilot team to earn their buy-in

Rolling out a new AI workflow is a major change that affects how reps work every day, so teams need time to understand it and trust it. If the workflow has too many problems at the start, reps will be reluctant to keep using it, and you’ll lose momentum.

Instead of introducing the new process to everyone at once, ease adoption by starting with a pilot team. A pilot helps you see where the workflow breaks, what needs to be adjusted, and what training the rest of the team will need before a full rollout.

Snowflake didn’t send the new workflow to all 300 SDRs on day one. Rather, they started with a pilot group of 10 reps. They created a Slack group, asked the reps to put half of their prospects into the new AI sequence and the other half into their usual sequence, and encouraged them to share what worked and what didn’t.

Because the group was small, reps felt comfortable pointing out problems, asking questions, and suggesting improvements. Their feedback helped Snowflake fix issues with the process and build a better product for reps, before rolling it out to the rest of the team.

AI prospecting is giving Snowflake’s SDRs 100s of hours back

Across most sales teams, SDRs spend a large part of their day on tasks that don’t directly create pipeline. They research accounts, check different tools for context, draft emails from scratch, and manage follow-ups. These tasks are necessary, but they take time away from the work that actually drives meetings and revenue, like cold calling or connecting with company executives.

Jeff explained that this is why he introduced AI and orchestration—to automate the tedious and time-consuming work that slows SDRs down. “We have no plans to get rid of our SDR teams or slow hiring. We want to automate some of their tasks so they have time to do all the other things they’re falling behind on,” Jeff says.

And with the manual work handled automatically, SDRs now spend more of their day on high-value outreach and also send far more relevant and personalized emails which has led to higher reply rates and more meetings booked.

AI hasn’t replaced SDRs and won’t anytime soon. But paired with orchestration, it makes SDR teams more productive and helps them hit targets faster.

More from the GTM team

• Craig Rosenberg on the State of GTM AI, published on Pavilion’s new GTM blog Topline (ICYMI, check out the full report here)

• Kristina McMillan on the evolution of AI SDRs, where they can be used to improve top-of-funnel efficiency, and what still needs the human touch, at least for now.

• Robert Koehler on using AI to coach reps on discovery, including the full prompts that dropped evaluation and reporting time from two to three weeks to two days

You can expect content from our events, including our summits, what’s working (and not) in AI for GTM teams, and near-real-time benchmarks. First up, Sydney Sloan, CMO at G2, walks us through search in the age of AI, from the basics of how it works to the strategic framework to help you capitalize on it.

Let us know what you’d like to see from us. Please share with a friend, or hit the unsubscribe button if you’d like to be removed from this list.

Want to switch your team’s focus from SEO to AEO (answer engine optimization)? Take a moment to consider how different those two media are.

Sure, both types of search are text-based. Yes, users still ask questions. But the way people ask questions has changed. Open your own search history to see: Do you really ask one question at a time anymore? Or do you ask a series? “It’s no longer question-and-answer,” says Sydney Sloan, CMO at G2. “The questions get really complex. It’s more of a chain of questions.” Further, you probably offer ChatGPT more context than you did Google—when searching for vendors, you tell it your tech stack, goals, and challenges.

Many marketers feel paralyzed because this upends the old playbook and because it all keeps changing. What does it mean for your marketing when buyers dictate to AI rather than type? Work in collaboration with AI chatbots? Aren’t exposed to your branding until late in the cycle?

That’s why Sydney took the stage at GTM 25 AI Summit to discuss what G2, the premier B2B review site, has learned about optimizing for AI search. G2’s own site is a major source of AI answers. Within, you’ll learn how you too can coax the new wave of answer engines to surface your answers when someone’s doing their research.

Watch the full session

AI is now core to the buying journey

LLMs set a land-speed record as the fastest-spreading work technology ever. Fifty percent of B2B software buyers say they’re more likely to start their search on LLMs rather than Google, according to a G2 survey of 2,000 companies. That’s up from 29% four months prior. Sydney feels confident it will continue to rise.

Here are the changes for your marketing team to consider:

AI wants complete answers in natural language

AI search tools browse the internet using the same old crawlers as search engines, but the similarities end there. Traditional search, if it can now be called that, produces a list of sources that probably include a link to your website. Whereas AI search aggregates one answer from many sources and may or may not link to your website. It probably also provides enough context that the user doesn’t even want to visit your website. This means that while there is some crossover value from all the authority your team has been building with SEO articles and FAQs, there are likely also major gaps.

“AI is searching for answers in natural language,” says Sydney. “So you’re going to want to think about the user-generated content where those answers may lie—in reviews on review sites. That’s where AI learns about what people like and don’t like, and how they compare products and weigh value.”

AI eats into your traffic

AI answer engines cannibalize your organic search traffic. That is their purpose: to save people the click and summarize long reads. That means fewer people will arrive at your website. But it also means that those who do are probably further along in their evaluation and better qualified.

“If you haven’t started thinking about how to optimize your speed from site visit to a meeting, you should be,” says Sydney. “Buyers arriving there are way further along in their research than before. You really want to understand that, and what the LLMs told them, which is based on user-generated content.”

AI answer engines seek consensus

If your brand and message are different everywhere they appear—if all the reviews and teardowns about your software say something different—you won’t have much influence on what the AI says. That’s because AI relies on consensus signal simplification, says Sydney. “When the AI says ‘thinking,’ it wants to give you the right answer, and so it’s looking across many sites for consistency or sources of higher authority. If everything people say about you matches, that’s likely to reinforce the narrative the AI search tool picks up on.”

Given all this, Sydney has arrived at three ways to switch your team’s strategy to generate AEO mentions.

Three ways to earn far more mentions in AEO

1. Build your brand where users live

Increasingly, LLMs are the front door to B2B buyer research. Marketers can expect less and less organic and paid traffic from other sources, but it’s not an all-or-nothing switch. This is simply about reallocating resources from your existing programs to what’s becoming the dominant way for buyers to shortlist vendors.

In G2’s survey, buyers say their number two source for unbiased information is software review sites. LLMs feel the same. AI search tools like ChatGPT and Claude rely heavily on forums, review sites, and self-publishing platforms like Medium. This creates a clear directive for marketers: If you want to be found in AI search, encourage more user-generated articles and reviews. “Only 5% of G2’s customers use an in-app review popup, yet they are all leaders. So, founders, don’t be nervous about putting questions in your product,” says Sydney. “Ask them for a review at moments of success. For example, the Vanta team asks for a review when someone gets their SOC 2 compliance. That’s ideal.”

The quality of those reviews matters, and you must earn real ones. G2 knows that when an individual comes back to update their review, that indicates real value, so G2 gives it higher priority. G2 isn’t alone in carefully ranking reviews by such signals, and AI follows its lead. All these systems are trying to approximate real, human-assessed value. For this reason, Sydney advocates running a “review refresh” campaign to ask your reviewers to make updates—and to encourage real honesty. A five-star review may actually be less believable than a slightly lower one.

“4.6 is the ideal review balance for believability,” says Sydney. “People want to see constructive feedback. Your role as marketer is to ensure you are addressing questions so other customers can see. It’s like when a hotel general manager replies to a questionable review. It makes you feel good about them and now you still might book to stay there.”

Get your founders involved in those conversations in a way that feels natural to them. People trust real people, and founders have credibility and star power. This takes a real time commitment, so prep them for that, and convince them to carve out time to contribute on their own to LinkedIn, X, Reddit, or elsewhere.

2. Retool your content strategy for greater relevance and consistency

As buyers do more of their research earlier, how do you engage them? In G2’s survey of 2,000 companies, most buyers said they can do their own research and only want to talk to salespeople during the evaluation. This means your content needs to reach those buyers earlier, which requires that you know them even better than you do today.

Sydney recommends revisiting your persona and their jobs to be done, and creating more tailored content to match those personas and jobs. Try to distribute more of it through user-generated content around the web. This isn’t a huge departure from content marketing today—building a relationship and demonstrating value in advance of an evaluation—but it does mean shifting the balance toward more of a distributed model, off your main properties.

“Today this means building out your AI personas and ideal profile, and ensuring they are constantly teaching themselves, updating themselves, and challenging themselves,” says Sydney. Her team automatically feeds Gong call summaries into a spreadsheet, which updates their AI knowledge base. “And then ask yourself, how do we integrate those personas into the content publishing workflow? Not a single piece of content should leave your company if it hasn’t been tested or validated against this persona.”

Build a persona for the “LLM” buyer, too. “LLMs are one of your new customers,” says Sydney. “Build content to train the LLMs separate from your buyers’ jobs to be done. I’ve seen people build whole shadow websites with hidden content for LLMs to index, in the language and tone they want it to pick up. Or maybe their content editor builds their own blog as a separate authority, answering those exact questions.”

Finally, ensure your content is consistent. For traditional search engines, you’d get dinged for too much repetition. In AI search, that’s actually the key to training the algorithm. You want to syndicate your reviews, key messages, and top content across many places to help nudge that consensus.

3. Get your PR game back on

Other sources LLMs value highly are traditional media outlets such as CNBC, Harvard Business Review, TechRadar, and other journalistic publications. This means that PR is back in fashion.

“For a while, marketers were like, ‘PR doesn’t play anymore, we’re just going to be on LinkedIn,’” says Sydney. “But now everything I do—and I don’t care what it is—I put out a press release about it. Because that gets indexed. I also went back and hired a PR agency and am investing there.”

In many ways, this is still the same old PR game as ever, only now it’s about getting indexed by LLMs to influence their answers. As always, search for the moments of unique, ownable drama. Sydney’s team saw the MIT study claiming that 95% of AI projects are failures and didn’t find it credible. They ran their own study with a counter-narrative and pitched it to VentureBeat and Forbes. “Publishing your own first-party research has never been more important,” she says.

While the PR game may not have changed, the publications have. Many have gone through decades of declining budgets and cyclical firings, so there are fewer writers covering larger beats. They tend to want to write about the biggest existing players in your space, so it may be more difficult to find an angle. But there are also plenty of pay-to-play outlets where this barrier doesn’t exist. A Forbes Councils subscription can cost as little as $1,600, and Fast Company has a similar program. “You write it and their editors still vet it, so they’ll go back and forth with you,” says Sydney. “And if you get a lot of engagement and your content is relevant, they’ll all also ask you to answer questions in other editorial content. When you share that on your social media, you look authoritative.” And of course, that may get indexed by AI crawlers.

Also pay attention to the AI citations you see when asking AI about your space. Take note of any writers or influencers with a preexisting following. Consider hiring them as a content strategist or expert contributor. “Clay did a great job of this. I didn’t know who Clay was a year and a half ago and now they’re everywhere,” says Sydney. “They hired like 75 influencers for a two-month period and completely drowned LinkedIn. Everywhere you looked, people were talking about Clay.”

The important thing is to start right now

If you aren’t yet building or executing a strategy for how you’re going to win in AEO, now is the time to begin. Don’t feel bad about what you’re starting from. Everyone is still trying to figure it out, and there isn’t a playbook yet.

What is certain is that you should be focusing more on user-generated content and ensuring that everything people say about you is consistent and indexed. Continuously capture all that and feed it back into your team’s own LLMs, and run content through those synthetic personas—including a persona for the LLM. Some teams are going as far as to create undiscoverable but indexable sections of their website just for LLMs. There are no particularly wrong answers, aside from waiting to see how things play out. Much like the early dot-com rush to secure domains or SEO rush to early search authority, those teams running active AEO experiments are the likeliest to figure it out and edge out the competition.

More from the GTM team

Check out all sessions from our AI GTM Summit on Substack or YouTube.

Use our marketing planning template to lay out your strategy for AEO and other initiatives in 2026.