Tech VP Of Marketing Breaks Down The AI-marketing Workflows Powering Her Organization
Learn how a marketing VP uses AI to scale campaigns, automate workflows, and boost pipeline, while balancing speed with human judgment, brand, and trust.
AI Advantages: AI reshapes marketing with faster content creation and improved product development, but increases market noise.
Marketing Fundamentals: Trust, clarity, and relevance remain crucial in evolving AI-driven marketing environments.
Lean Teams: AI allows small teams at Sequel.io to boost efficiency without increasing headcount drastically.
Content Quality: AI's volume increase risks lowering content quality; distinctiveness and judgment are essential.
Curiosity Crucial: High 'figure-it-out' factor is more valuable than technical skills for adopting AI in marketing.
Kathleen Booth is VP of Marketing at Sequel.io, where she focuses on AI-first, brand-forward marketing. She's also the creator of the Code Meets Creed newsletter, where she explores how to become AI-first without becoming AI-only.
We sat down with Kathleen to get a sense of where AI is an asset to marketing teams — as well as where it's a risk. Along the way, she broke down her most useful AI-marketing workflows. Here's what she said.
Trust, Clarity, and Relevance Will Always Win
I didn’t start my career thinking about marketing, as we define it today. I started as a founder.
Want more from The CMO?
Sign up for a free membership to complete reading this article:
Have an account? Log In
I built and ran Quintain Marketing for over a decade, working with companies across wildly different industries. That experience shaped how I think about growth more than anything else. When you’re that close to the work, you see what drives results, and I consistently observed this pattern: Marketing mechanics change constantly, but the fundamentals don’t. Trust, clarity, and relevance will always win.
After exiting the agency, I stepped into leadership roles inside companies, including Pavilion, where I had a front-row seat to how thousands of go-to-market leaders were navigating change. That’s where I realized the old playbooks were breaking. Funnels were getting noisier, channels were saturating, and buyers were harder to reach — and even harder to convince.
Now, at Sequel.io, I’m in the middle of a second reset. I came in with a mandate to build marketing in an AI-first, brand-forward way, from the ground up. The company is growing quickly — we 3x'd revenue last year — and we have strong product-market fit with a highly differentiated product. So, I'm lucky, and the wind is in my marketing sails.
At Sequel, AI isn’t just another tool or channel. It’s reshaping how we do our work, how we create content, and how buyers evaluate us. It’s compressing the sales cycle, lowering the cost of product development, and helping us ship new product releases at an insane pace. But AI is also flooding the market with more noise than ever, which creates a paradox.
AI isn’t just another tool or channel. It’s reshaping how we do our work, how we create content, and how buyers evaluate us.
The more content we can produce, the more valuable taste, judgment, and human connection become. I’ve been exploring this theme in my Substack newsletter, Code Meets Creed. Not just how to use AI, but how to lead through it. How to build teams that are AI-first without becoming AI-only. How to hold onto brand, creativity, and ethics in a moment that rewards speed and scale.
If there’s a throughline in my career, it’s this: I’ve gone from executing marketing to leading it to rethinking what it means in an AI-driven world. I don’t think we’re at the end of that transformation. I think we’re still very early, and we’re all figuring it out in real time.
A Lean, High-leverage Marketing Team
I lead marketing and business development at Sequel.io, and it’s a deliberately lean, high-leverage team.
We’re a venture-backed, Series-A-stage company, so the organization isn’t large. It includes a handful of full-time marketers, two BDRs, and a mix of fractional and agency support. Our scope, however, is broad. We own brand, demand, product marketing, field and partner marketing, and outbound. Functions have no clean separation; it’s one system that generates 100% of the company's pipeline.
We’ve made a conscious shift away from paid channels and toward what currently builds trust and pipeline for us: field events, executive dinners, outbound, partnerships, and organic social. Search is evolving for us, especially as AI changes discovery. We’re also investing heavily in turning our own product into a channel by using engagement data to drive our go-to-market motion.
Complexity lies not in scale, but in ambition. We’re repositioning the company from a webinar platform to an AI-powered engagement layer for the website. At the same time, we’re building an AI-first marketing organization. This means rethinking how work gets done, where humans add the most value, and how to operationalize judgment and taste alongside automation.
What we’re building is emblematic of where marketing organizations are heading. We’re seeing pipeline double, even triple, without a corresponding increase in spend or headcount. AI provides that leverage, not as a replacement for people, but as a force multiplier. This shows up in metrics that didn’t matter as much before, especially ARR per FTE. To me, this is becoming one of the clearest signals of marketing effectiveness in this next era.
How AI Lead To Three Big Changes In Marketing
AI has changed how we work in many ways. Here are the three biggest examples that come to mind:
Redefining the Content-marketing Process
The biggest shift wasn't adopting AI tools; it was changing how work flows. We moved to a "bookend" model for content. AI sits in the middle, but humans own the edges. Every piece of content starts with a point of view from a real person. Then AI synthesizes that thinking and generates a first draft. Before anything goes out, a human shapes, refines, and pressure tests it.
That quickly changed the marketer's role. Less time staring at a blank page, more time thinking, editing, and making judgment calls. The output is faster, but it also feels more intentional because it’s anchored in a real perspective from the start.
Exposing Issues in ICPs and Messaging
Another big change involved structuring our company beliefs. We created training documents that serve as single sources of truth for our personas, ICP, messaging, and brand voice. It sounds basic, but it’s been one of the highest leverage things we’ve done.
AI exposed their previous fuzziness. If your inputs aren’t clear, the outputs fall apart.
Now, that shared foundation accomplishes several things at once. It drives consistency across everything we market. It makes onboarding faster because new hires have a clear starting point. And it allows the entire team to move faster with AI because we all operate from the same definitions.
Rethinking Bandwidth
The third shift involves how we think about bandwidth. Marketing has always been constrained. There’s never enough time or people, but the default response used to be prioritizing less or asking for more headcount. Now the first question is different: "Can this be automated?"
We started looking at every workflow through a jobs-to-be-done lens. What are the discrete steps, and which actually require human judgment versus pattern recognition or synthesis? AI doesn’t replace the work, but it reshapes where humans spend their time.
That shift is subtle, but it changes a lot. It pushes us to protect the parts of the job that require taste, context, and creativity, and aggressively automate the rest.
How AI can power dynamic competitive intelligence
We built one of our most useful workflows around competitive intelligence. It used to be manual and inconsistent. Someone would periodically check competitor websites, skim press releases, and maybe update a slide or two. By the time it reached sales conversations, it was already outdated.
Now it’s a weekly, AI-powered system that runs end-to-end.
First, AI continuously monitors. It pulls in updates across competitor websites, product pages, pricing changes, job postings, funding news, customer announcements, and content. Instead of someone hunting for signals, AI brings the signals to us.
From there, AI synthesizes what matters. Not just what changed, but why it matters. Is a competitor moving upmarket? Are they shifting positioning? Launching into a new category? Hiring for a capability that signals a roadmap change?
That becomes a weekly competitive intelligence brief for the entire company. It’s readable, opinionated, and focused on implications, not just information.
But the more important part occurs behind the scenes. That same output feeds structured competitor knowledge files. Each competitor has a living profile that updates weekly. Positioning, strengths and weaknesses, product capabilities, pricing signals, target personas, and recent moves.Trust, clarity, and relevance will always win.
Those files then power everything else, including self-updating battle cards, sales decks that stay current without manual rework, and objection-handling frameworks that reflect what is happening in the market, not what was true six months ago. Even messaging and positioning work on the marketing side pulls from the same source.
So competitive intel becomes a dynamic system instead of a static artifact. AI handles the monitoring and synthesis at scale. Humans review, refine, and decide what to act on.
The result is that the entire company operates with a more current, shared understanding of the market. Sales walks into conversations better prepared without needing a separate enablement push every time something changes.
Why Marketers Must Make a Shift to Systems
Campaign execution is ripe for reimagining in an AI-first way.
Most teams still run campaigns as they did a few years ago as linear, channel-specific, slow to launch and adapt.
That model breaks in an AI-augmented world.
We started redesigning campaigns as systems instead of one-off launches. Every campaign begins with a core point of view, but from there, AI helps us generate a full set of assets across channels almost immediately. Email, outbound sequences, landing pages, social variations, event tie-ins. Not perfect, but enough to get to market fast.
The bigger shift happens after launch. Instead of setting a campaign live and waiting for results, we continuously feed performance data back into the system. AI helps synthesize that and suggests where to adjust — from messaging and targeting to sequencing and timing — so the campaign evolves in near real time.
We’ve seen two clear results. First, speed. Campaigns that used to take weeks to plan and launch now happen in days. Second, learning velocity. We run more tests, get feedback faster, and iterate while the campaign is still live instead of doing a postmortem after it’s over.
It’s not fully autonomous. Humans still decide what to test and change, but the system is tighter. And it forces a different mindset — you no longer launch campaigns; you manage living systems that get better over time.
When AI Informs Data, Humans Handle Judgment
Don’t think about it as what AI does versus what humans do. It’s more about where judgment matters and where it doesn’t.
AI is now integral to anything that benefits from synthesis, pattern recognition, or speed. We use it to analyze engagement data across our webinars and website, surface signals at the account and contact level, and help prioritize where sales should focus. It supports research, competitive analysis, SEO structure, campaign planning, and generating first drafts across content and outbound.
It also increasingly shapes our approach to timing and relevance: what topics are resonating, which accounts are heating up, and where we should lean in. That kind of signal processing used to take days. Now it’s nearly real-time.
But the decisions that matter remain with humans. This includes positioning, narrative, and brand voice: what we choose to say, and just as importantly, what we choose not to say. Those require context that doesn’t live in a dataset. They require taste.
The same is true for prioritization at the highest level: where we place bets, which markets we go after, and how we balance short-term pipeline with long-term brand. AI can inform those decisions, but it shouldn’t make them.
Anything involving trust also remains human. Relationships with customers, executive conversations, community building — the nuance in those interactions matters. You can’t outsource that without eroding the very thing you’re trying to build.
So the line isn’t fixed, but the principle is consistent. If the work involves processing information, scaling output, or identifying patterns, AI should do most of it. If the work involves judgment, taste, or trust, it remains human.
If the work involves processing information, scaling output, or identifying patterns, AI should do most of it. If the work involves judgment, taste, or trust, it remains human.
The Downsides of Using AI in Marketing
AI's most obvious benefit is output and efficiency.
We run more campaigns, produce more content, release more products, and support sales faster without adding headcount. This translates into a meaningful increase in pipeline coverage with relatively flat spend. The exact numbers are still evolving, but we've seen a lift that allows us to double pipeline before feeling pressure to double the team. That wasn’t possible before.
AI also improves cycle times. Tasks that used to take days now take hours. Campaigns get to market faster. Sales follow-up is tighter. We respond to market changes in near real time instead of working off last month’s insights. Our BDR team provides a great example; they used to spend every Monday prepping for outbound. This was a full day every week. Now, they come in on Monday and start outbounding right away. That's a 20% efficiency gain, which is huge.
But there are trade-offs.
The biggest tradeoff is that it raises the bar for quality. When everyone can produce more, average quality declines. We see more generic content, more sameness. If we aren't careful, we can scale mediocrity very quickly.
AI also shifts bottlenecks. Production is no longer the constraint; judgment is. Editing, taste, and decision-making now disproportionately consume time. Not everyone naturally adapts to that shift.
We also risk false confidence. AI can make something sound right even when it’s not. Without strong domain knowledge or clear points of view, we easily ship work that feels polished but lacks substance.
The net impact is positive, but it presents challenges. We gain leverage, speed, and scale, but we also take on the responsibility of being more intentional about what good looks like and more disciplined about protecting it.
Why AI Challenges The Current Mindset
AI forced me to let go of the notion that more content equals more results. For most of my career, I implicitly believed that if I produced enough, something would break through. Volume was a proxy for momentum across blogs, emails, campaigns, etc. But this approach reflects broken decision-making frameworks that many marketing teams still rely on.
AI quickly broke that assumption. Now, anyone can produce at scale. The marginal value of another piece of content has dropped to near zero if it doesn’t say something original or meaningful.
The advantage shifted. It’s no longer about who can create the most. It’s about who can create something worth paying attention to.
This forced a change in how we operate. We spend less time asking, “How do we produce more?” and more time asking, “Is this distinct? Does it reflect a real point of view? Would anyone care if this didn’t exist?”
AI didn’t kill content as a channel; it just removed the illusion that volume was the answer.
AI Struggles Where It Matters Most
AI has fallen short in the areas that define great marketing. I expected AI to help more with differentiation. It hasn’t. If anything, it’s done the opposite. When everyone uses similar tools trained on similar data, the output starts to converge. You get content that’s technically correct but bland. It answers the question, but it doesn’t make you care.
Then there’s brand. AI can mimic a voice if you train it well enough, but it doesn’t originate one. It doesn’t have taste or conviction. Those things come from lived experience, from making decisions and living with the consequences.
A good example is when we tried using AI to rewrite our homepage. We fed it everything. Our positioning, product details, ICP, competitor context, and even examples of messaging we liked. On paper, it had all the ingredients needed to produce something strong. And the output was… good.
It said the right things about our product and category, and it would hold up just fine compared to many competitors.
But that was the problem — it sounded like a lot of our competitors' homepages. There was no edge to it. No tension. No point of view that made you stop and think, “This is different.” It explained what we do, but it didn't make you feel why it mattered or create meaningful separation from competitors.
We ended up using parts of it as a starting point, but the final version required significant human intervention. We rewrote headlines, sharpened the narrative, introduced stronger opinions, and became much more deliberate about what we emphasized and what we omitted.
That experience changed how I think about AI in brand work. It's very good at achieving competence. It's not great at achieving distinctiveness. And in a market where everyone has access to the same tools, “good enough” is exactly what makes you invisible.
How AI Shapes the Future Trajectory of Marketing Teams
AI hasn’t changed our team's shape as much as its trajectory. A year or two ago, as pipeline goals increased, we would have added headcount: a product marketer, a lifecycle marketer, a field events owner. Marketing teams have historically scaled this way.
We didn’t do that. Instead, we’ve supplemented our current team with AI, pushing the limits of what we can achieve without adding people. This has forced us to become more disciplined about what truly requires a human and where AI can extend our team.
The result is a smaller team operating at a level that previously required significantly more headcount.
The future direction is even more interesting. Our next hire is unlikely to be a traditional functional marketer. It will probably be a go-to-market engineer — someone who can work across the entire marketing organization to identify opportunities for AI to automate, integrate, and create leverage. That’s a very different profile.
This is a much higher-leverage hire than adding another individual contributor in a single channel or function. If executed well, that role unlocks efficiency across everything. It creates capacity, allowing us to bring in specialists later, where AI truly can’t replace human expertise.
So the team doesn’t necessarily get bigger first; it becomes more highly automated and AI-enabled.
Why One Trait Beats Technical Know-how in AI Adoption
Marketing teams need more people with what I’ve always called a high "figure-it-out" factor. These people hit a roadblock and don’t stop there. They go deeper, test and break things, and pull threads until something works. That trait matters more in an AI world than any specific skill set because tools are changing too fast to master.
What matters is curiosity — a real desire to understand how something works and what it could become. The people on our team who thrive with AI aren’t necessarily the most technical. They’re the ones who open a new tool and immediately start experimenting. They don’t wait for permission or a perfect use case; they just try.
That creates leverage and prevents the biggest risk I see right now: a failure of imagination. The organizations that win won’t be the ones with the best tools. They’ll be the ones with the most curious people.
Why Marketing Leaders Must Focus on Foundations First
Here's my advice:
First, get your foundations right before you chase the tools. It’s tempting to start with use cases and experiments, new platforms, new prompts, or new workflows. But if your ICP is fuzzy, your positioning is unclear, or your team doesn’t have a shared understanding of your brand, AI will just amplify that inconsistency.
Start there.
Second, hire and reward curiosity. People who create leverage with AI don’t wait for playbooks. They test, break, and figure things out on their own. That mindset compounds faster than any single tool.
Third, protect what shouldn't be automated. We risk optimizing for speed and losing what makes marketing work. This includes original thinking, taste, and human connection. AI can scale output, but it can’t replace judgment or trust. If you don’t actively defend those, they erode. So, use AI aggressively where it creates leverage, but be disciplined about where it doesn't belong.
And lastly, accept that this isn't a one-time shift. It’s an ongoing rewrite of how marketing works. Leaders who do well won't get it perfect. They will keep adapting without losing their point of view.
Follow Along
You can connect with Kathleen Booth on LinkedIn and follow her work at Sequel.io. You can also check out her Substack and personal website.
More expert interviews to come on The CMO Club!
