The Marketing Operations Strategist - Who owns AI for GTM? It should be you.

Hey! 👋

Everyone in GTM is talking about AI right now. Sales wants a call analyzer. Marketing wants a content bot. The CMO saw something at a conference and wants to know why we don't have it yet (sigh).

And if you're in ops, you're probably the one being handed that list of asks, often with no additional headcount, no clear prioritization criteria, and a very optimistic timeline. 😅

That chaos can be an opportunity. Every AI initiative your GTM team deploys -- regardless of who requested it or which vendor sold it -- will ultimately live or die based on the quality of your underlying data and process. Which means ops is the function that determines whether any of this actually works.

The teams that recognize this early and communicate it clearly to leadership are the ones that shift from being the "implementation team" to being the strategic partner driving the roadmap. But to get there, you need a framework for deciding what to build first and why.

Having a principled answer to "where should we start?" is how you demonstrate that ops owns the AI GTM roadmap, not just the implementation queue. Later in this edition, I’m going to give you a free template to get started. But let me set the stage first:

I've noticed two patterns that tend to derail GTM AI initiatives early on:

The first is the demo model. Whatever impressed someone at a conference or in a vendor pitch gets resourced…usually without any real evaluation of whether the org is actually ready to support it.

The second is the loudest-voice model: whoever pushed the hardest in last quarter's planning meeting wins. Neither of these produces a coherent strategy, and neither positions ops as anything more than an execution arm.

The more scalable approach is to evaluate every AI initiative against a consistent set of criteria. Over the past month, I've been building out our own GTM AI roadmap and have landed on six questions I ask for every use case before it goes on the priority list.

1. Does this meet users where they already live?

The highest-failure-rate AI projects I've seen are the ones that require a lot of unnatural behavior change. If your reps have to log into a new tool, learn a new interface, change how they update records -- adoption will suffer no matter how impressive the underlying technology is. The “carrot at the end of the stick” has to be pretty huge, to incentivize such change. Prioritize solutions that surface inside tools your team already uses daily.

2. Does this democratize visibility without creating new math?

One of the most undervalued AI use cases in GTM is taking information that currently lives in someone's head -- or requires a data pull to surface -- and making it available to the people who need it. Account health flags, pipeline summaries, intent scoring. The test I use: can a rep, a manager, or an exec get the answer they need without opening a second dashboard or running an additional query? If the AI output still requires a human to interpret it, the problem hasn't been solved.

3. Does this make manually collected data usable?

Call recordings, pipeline notes, Slack conversations, support tickets -- this is where the most valuable GTM intelligence lives, and it has historically been too messy and unstructured to use at scale. Prioritize use cases that unlock the manually collected data your team is already generating, because the data exists and the business value is immediate. This is where I've seen the highest ROI, consistently.

4. Does this make the GTM motion meaningfully more efficient?

Efficiency is not the same as automation. The question isn’t whether AI can do the task — it’s whether doing it faster or at greater scale produces a materially better outcome. We can automate custom reports to every AE til the cows come home, but seeing data alone doesn’t create material impact. Automating the filling in of a field in HubSpot is not a goal in itself. The “what now” actions or activations are what make the difference. Don’t be fooled into thinking that activity automatically translates to high ROI use cases.

5. Does this create internal visibility for the ops function?

Before you accuse me of being self-serving, hear me out. 😅 One of the most practical reasons to build AI solutions for internal stakeholders -- pipeline review prep, revenue reporting, forecast summaries -- is that it puts ops in the room at the moments that matter most to leadership. When ops is the team that enables AI for GTM, the function's strategic value becomes self-evident.

6. Does this reduce the operational burden on the ops team itself?

Ops cannot govern an expanding AI ecosystem while still manually running every process it built over the last five years. If you're still pulling every report by hand, that's a use case worth solving before you build a competitive intel bot for sales. Freeing up ops capacity is a requirement for the function to sustain itself.

More coming after the break, including a guide you can use at work… 📺

This section is sponsored by Zapier, a partner who supports this newsletter. 💌

✨ How to turn call transcripts into product intelligence in HubSpot

Most teams know what their prospects said on a call. Almost none of them have that information living anywhere useful in their CRM, which can be a huge miss when it comes to targeting and nurtures. Here's how we fixed that at Vector using Zapier + AI.

Now your GTM team can see which features a prospect actually cares about without listening to a single recording -- and you don't have to share your full CRM with the AI model to make it work.

🔗 Try it yourself with a free 14-day Zapier trial.

…back to our GTM AI programming. 📺

Map your current or proposed AI initiatives against these six questions. Assign each use case a score from zero to six based on how many it satisfies. Initiatives scoring four or higher belong at the top of the roadmap. Initiatives scoring two or lower -- regardless of who requested them -- should be explicitly deprioritized, with a clear rationale you can share.

I put the full scoring tracker, a pre-populated use case inventory, and an exec briefing template into a guide you can copy and start using today.

👉 GTM AI Roadmap Starter Kit

The scoring matters less than the conversation it creates. When ops shows up to an AI planning discussion with a structured evaluation model, the dynamic shifts. That distinction is how ops earns the strategic authority this opportunity is handing us.

This month has been all about hiring, team planning, continuing to build the fundamentals, and AI.

I’m planning on using this section to outline some of the experiments I’m doing and things I’m building. I’ll do some deeper dives, but to give you a sense of what I’m focused on now:

1. I’m building a Customer Success Platform in HubSpot. Some might think I’m crazy for this, but our CSP needs were not strong enough to invest in an expensive platform — at least, not at this stage. And so much product and sales data lives in HubSpot…so I’m setting up a Solutions/CS section on each company so our Solutions Managers can get important data at a glance, setting up Projects to track onboarding, and more.

2. I’m building AI digests for each function. At Vector, we are heavy Slack users — so I’m pulling data sources together and serving up digests using Claude Cowork. With Claude, I can pull in HubSpot, Slack, Pylon, Fathom, and even external data.

So far I’ve built:

- Sales Deal Pipeline Digest: deal progression, deals that need to be touched, deals that need to be updated or closed, and estimated pipeline closed by end of month).

- Solutions Pulse Digest: key tickets and issues with customers, customer health, customers who haven’t chatted with us in a while, and, of course, upcoming renewals. 💰

- GTM Competitive Intel Bot: new objections, competitor mentions (with quotes and links to recordings), and competitor news on the web (like new product comparison pages).

These have been really great — not only for my stakeholders, but for me to be able to quickly keep a pulse on any major issues or discoveries!

3. I am building a Signals House. There are a lot of signal houses out there (think ABM platforms) but we want to use our own product and signals to make the best algorithm we can. So we’re looking at using Clay, Spyfu, and other data sources to create a repeatable way of surfacing accounts that have fit, problem intent, and/or solution intent. More on this as we get further with it!

4. I am still doing all of the more boring RevOps stuff. 😅 So much hype around AI right now, but the reality is that if we don’t have our processes and data in check, AI is really hard to use effectively. So I’m looking at our products, renewals process, all of that fundamental stuff and making sure that we are consistent and serving the business well.

Btw, if you want to follow along with our wider GTM strategy and tactics, follow the Vector blog! You can subscribe to our newsletter too! 👻

This section is sponsored by Default, a partner who supports this newsletter. 💌

✨ How I set up our external cold calling agency with smart routing in >30 mins

Handoffs are where most GTM stacks get brittle. Meetings get re-created, ownership gets messy, and routing exceptions quietly break queue fairness -- especially when you're mixing an outside agency with your own reps.

We recently set up Default Handoff at Vector to automate demo request routing between our cold calling agency and our internal team, splitting correctly between owned accounts and non-owned round robin. Took less than 30 minutes to configure and has worked exactly as expected since day one.

What makes it different from a standard meeting router is that the handoff runs through the same workflow canvas you're already using for enrichment, routing, and lifecycle -- so there's no separate logic to maintain. Routing is a system decision, not a rep decision.

🔗 See how it works here.

New to marketing operations? On a team of one at your company? Shy/introverted? Wish you could ask a question to an experienced marketing operations professional, without them knowing who you are? Here’s your chance! Submit an anonymous question to me here and I’ll answer a new question in every issue.

Here’s my answer to a question from last week:

I’ve been there before! I got my start at a Salesforce agency and it was rough to get into my first in-house role. Here’s what I learned:

Overall, believe in yourself and make sure you take care of yourself throughout the process. I once got called “low energy” in an interview, and…they were right. I was trying to do too many interviews back to back, fighting an uphill battle, and I was exhausted. It will come across if you are exhausted as well, so try to create a situation where you can relax and not urgently have to look for a new job (best when you already have one!). Best of luck to you!

MarTech published a piece this week on CFOs taking control of GTM decisions -- not because they want to, but because marketing and sales can't agree on what the pipeline data means. When nobody can prove causality, finance defaults to cost control.

Why you should care: if ops isn't producing shared definitions, the budget conversation gets decided by whoever has the most authority instead of whoever has the best data. Clean attribution and consistent lifecycle definitions aren't nice-to-haves right now.

A CoSchedule survey of 911 marketing professionals found that only 3% of marketers identify as AI experts -- despite near-universal adoption. 37% call themselves "growing," 31% "intermediate."

Why you should care: almost everyone is using AI, almost nobody feels expert at it, and that gap is an authority opportunity for ops. The function that can govern, prioritize, and operationalize AI across GTM will fill it by default.

A sharp piece dropped this week on why martech consolidation business cases fall short -- vendors compare license fees to license fees, but the real cost of a martech stack runs 2.5x the license fee when implementation labor, adoption ramp, and ongoing maintenance are fully loaded.

Why you should care: if you're evaluating a consolidation this year, ask your vendor to show you the total cost model -- not just the license comparison.

HubSpot added campaign attribution. Teams can now manually associate contacts, deals, and tickets with campaigns -- and automate those associations via workflows. Offline interactions, partner leads, and third-party leads can now be properly tied to campaigns without hacks. 🙌

Why you should care: if you've been doing workarounds for attribution on anything that doesn't come through a HubSpot form, you already know exactly why this matters…lol.

What else have you heard about recently? Reply back to this email to send me any other word on the street. 👂

Gamma.ai: an AI tool to create presentations, websites and more using your existing tech stack.

I am still in the early stages of using Gamma, but I like what I’ve seen so far. As my Solutions team thinks about QBRs, and my Sales team scales, I start to think about how to make presentation creation as quick, easy, and…well…consistent as we can. With Gamma, you can use AI + HubSpot data to inform the slide information…I dream of a world with no more tedious copy + paste of things like product metrics. 🥲

They have a bunch of other integrations as well, which could help with referencing more data like Slack messages and Fathom meetings. I’m planning on trying them out for QBRs and Sales decks, and I’ll report my findings in here. 🕵️‍♂️

This section is sponsored by Knak, a partner who supports this newsletter. 💌

😬 If you're only measuring AI ROI by time saved, you're underselling it

88% of companies use AI regularly. Only 6% can demonstrate meaningful business impact. 😬

Time saved is the easiest number to capture, but it tells you nothing about whether the output got better or what the team can now do that it couldn't before. Report time savings to leadership and you get the cost-cutting conversation. Report quality, capacity, and learning -- and you get the investment conversation instead.

Knak breaks down all four dimensions worth tracking, with case studies from Forbes, Citrix, and OpenAI that show what the capacity argument actually looks like in practice. Worth a read before your next AI budget conversation.

🔗 Read the full breakdown here.

Meme of the week ⚙️

World's falling apart but here's your automated email sequence I guess 🫠 😂

If you have a moment, will you let me know if you enjoyed this edition of the newsletter? Thank you!

Thanks for reading,

❤️ Sara

P.S. Special thank you to our sponsors for supporting the creation of this newsletter. 🫶