CMO Says AI’s Efficiency Gains Are Hurting Your Marketing Budget
Discover why AI-driven productivity gains often fail to impact revenue, and how marketing leaders can reinvest capacity into strategy, creativity, and brand growth.
Diego Lomanto
CMO at WRITER
Transforming Marketing: Agentic AI in marketing has the potential to transform enterprise operations and close operational gaps.
GTM Strategy: Success in AI for marketing relies on covering the full GTM surface with consistent engagement.
Operational Efficiency: AI agents can enhance marketing efficiency but require strategic reinvestment to impact revenue significantly.
Human Oversight: AI lacks instinct for creative decisions; humans must maintain strategic direction and brand distinction.
AI System Integration: Connecting AI tools to existing systems ensures seamless workflows and comprehensive marketing capabilities.
Diego Lomanto has spent two decades in enterprise technology GTM. He currently serves as the CMO at WRITER, an organization that builds enterprise AI for Fortune 500 companies.
We sat down with Diego to learn from his experience with agentic AI in marketing. He told us why AI efficiency gains aren't impacting the bottom line in many organizations — and how to fix it.
The frontlines of transformation
I'm Diego Lomanto, CMO at WRITER. We build enterprise AI for Fortune 500 companies and help some of the biggest, most recognizable brands in the world — like Vanguard, Clorox, Mars, and KPMG — transform their marketing operations at scale.
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I've been in enterprise technology GTM for about 20 years. Before WRITER, I was at UiPath through one of the defining growth moments in enterprise software. I joined when annual revenue was around $50 million and helped scale it past $1 billion through IPO. Before that, I joined Workmarket at Series A, helped define their market position as an enterprise workforce management solution, and helped drive the acquisition by ADP.
Across both experiences, I consistently saw that winning companies close the gap between technical possibility and operational reality within large organizations. That gap has been my obsession for most of my career.
When I joined WRITER in 2024, I saw the enormous potential of agentic AI to completely disrupt marketing as we know it, and I knew WRITER would power that transformation. WRITER is built for business users, the people closest to the work who know what's broken, and it makes it easy for non-technical people like my team and me to harness agentic AI, rebuild workflows, and transform how we work without needing an engineering team at every turn. That's why my team is the first test case for everything WRITER does.
I believe marketing is the proving ground for AI, but right now, no playbook shows anyone what "good" even looks like. It's incredible to be on the frontlines of this transformation, building frameworks and use cases that shape hundreds, thousands of other marketing teams.
The full GTM surface
My team covers the full GTM surface, including demand gen, product marketing, brand, content, communications, field, customer marketing, and analyst relations for WRITER.
We sell to enterprise buyers inside Fortune 500 companies, which means any given campaign must simultaneously satisfy a CMO who cares about brand integrity, a CIO who needs security and governance answers, and a business team lead who just wants to see meaningful output faster. Every channel decision, every message, every piece of content we put out operates across all three.
This is also the most competitive landscape I've seen in 20 years of doing this. AI is compressing what used to be multi-year technology cycles into months. Enterprises aren't buying software the way they used to, locked into three-year deals. They're evaluating constantly.
That means we have to be in the market all the time, earning attention from sophisticated buyers who are getting pitched by every AI vendor. You can't coast on a campaign. You have to show up in a way that's consistently, recognizably yours.
We've worked hard to build that space. Protecting it is an ongoing job.
How AI agents create operational efficiency
The biggest operational change we made with AI was pulling humans out of a majority of the execution work and replacing them with governed AI agents that autonomously run those workflows at scale.
By execution work, I mean the things that consume most of a marketing team's day: drafting, editing, formatting, routing for approvals, and adapting content across channels. They’re not the most glamorous use cases, but they were eating the cognitive capacity we needed for strategy, creativity, and the work that moves the business.
We started with the highest-impact workflows, not the easiest ones. There's a gravitational pull early in any AI rollout toward what's simplest to automate. That's useful for building comfort, but it doesn't move business metrics. We used an impact-by-frequency model: What work happens constantly and follows a predictable pattern? That's where we started. Our team is now producing 2.2x the volume with only 1.25x headcount growth — with AI handling execution work driving roughly 60-70% of that gain.
How AI gains can hamstring your marketing budget
There's a counterintuitive problem with these efficiency gains.
AI also hasn't solved the measurement problem, which many marketing leaders hoped it would by now. Only about 22% of marketers have the infrastructure to prove marketing's direct business impact.
This goes back to the “productivity trap.” The moment you frame AI as an efficiency play, you hand your CFO the justification to cut your budget. The real question for your CFO isn't "How do we spend less?" It's "How do we rebalance Bucket 2 spend into Bucket 1, where every million converts to twenty million in pipeline?
All marketing spend needs to either drive return or enable it. Once you make that case, clarify that productivity is just the foundation, and real value compounds when you achieve visibility, differentiation, and agility. That’s the language your CFO speaks.
Why efficiency gains must be followed by strategic reinvestment
There's a metric that I'm more proud of than our efficiency gains, but it's harder to put in a slide. When your team isn't grinding through execution, they think differently. They take bigger creative swings. They build things that have been sitting in the backlog for months.
Our Marketing AI ROI Calculator came directly out of this. One person finally had the time and headspace to build something that had been a back-burner idea for too long. It's now one of our strongest demand generation assets.
This type of innovation is important — it's how you close the gap between expectation and reality in terms of bottom-line revenue impact.
Most teams see genuine productivity gains, celebrate, and move on. Then six months later, they wonder why efficiency isn't showing up in the pipeline. Time savings were meaningful, but teams filled them with more execution work.
When AI doesn't change strategic attention, and reclaimed capacity becomes more content production instead of sharper positioning or stronger creativity, it doesn't change business outcomes — strategic reinvestment never happened.
Why humans always own the direction, even as AI takes on more execution
Humans set the direction, and AI executes at scale.
Our general mental model is that humans set the direction, and AI executes at scale.
AI handles anything that follows a pattern: content adaptation, workflow execution, compliance checks, performance reporting, routing, and approvals. I draw the line at "valuable friction" points, where human expertise, judgment, and taste make a brand distinctive.
Most CMOs underestimate the risk of brand erosion at scale. Great brand work isn't just consistent; it's occasionally surprising. It makes choices that feel unexpectedly human and earn attention precisely because they're unpredictable. AI excels at learning patterns and executing within them reliably, but it doesn't have the instinct to know when breaking the pattern is exactly the right move. Humans must maintain creative courage.
No proven playbook exists for where the line sits, and the target constantly moves. AI can now reliably handle tasks that required human judgment 18 months ago. Part of the job involves continuously reassessing where that threshold sits.
Drawing the line incorrectly in either direction creates real problems.
Why AI agents must be connected to your systems
Critically, none of this works if your AI tools aren't connected to your CRM, CMS, data warehouse, and marketing automation platform. If your team still switches tabs to pull context from Salesforce or HubSpot, you're not acting agentically. Real transformation happens when you give AI access to the systems where work lives, so the entire marketing engine moves together, not just isolated parts.
When that's in place, you get something fundamentally different from what most teams are building.
You can be highly visible, producing content at scale across every channel that matters. You can encode your differentiation — your voice, perspective, and identity — into every output. And you can adapt in real time, changing direction globally without layers of process slowing you down.
It becomes a marketing engine that's always on, always in market.
Why mandating AI adoption fails without engagement
You can't mandate your way to AI adoption. I tried. It failed. Usage numbers didn't move.
What shifted the culture was something simpler: a hackathon and a meme. I got the team together, made it fun, and made it feel like we were all on the ground floor of something together. We used a dead-simple framework with two axes, impact and frequency.
High impact, high frequency? That's your first agent. Low impact, low frequency? Park it, don't waste the energy. From there, we created sticky notes and placed them on a wall with endless daily use cases that drain time without adding strategic value.
It's fun. It's energizing. And it gives your team something to rally around, not a mandate from the top, but a shared map they built together.
I also made the early adopters "famous." Not with a formal awards program. Shoutouts in Slack when someone built something that worked. A quick internal video series where team members showed what they'd built and what it unlocked. We’ve taken this to LinkedIn and used these videos to inspire marketers outside our own organization. The people who were ahead shared openly. The people who were more hesitant saw what was possible for someone who looked like them, doing work like theirs, and wanted in.
And then I created an internal meme: “Can an agent do that?” Anytime someone came to me with an idea, a process to improve, or a tool to buy, I asked that simple question. Consistently, repeatedly, and if you ask my team, annoyingly.
But it worked. Before bringing anything to me, the team knew they would have to answer that question, and it changed our behavior by creating new habits.
Why CMOs must now remove themselves from the work
You don’t need to be the last reviewer on everything. Frankly, no one on your team wants that either. You’re just slowing the whole process down.
For most of my career, I believed my value as a marketing leader came from staying close to the work: reviewing, approving, and being in the loop on what went out. Gatekeeping was care.
I've come to understand that the CMO's role has fundamentally shifted from gatekeeper to architect. You don't need to be the last reviewer on everything. Frankly, no one on your team wants that either. You're just slowing the whole process down.
You need to build the right system in the first place. That's a different job, and it required letting go of a way of working and, honestly, an identity I'd built over two decades. I try to be direct about that when I talk with other marketing leaders because I don't think I was the only one who felt that shift as a loss before I felt it as a gain.
Why CMOs must get their hands dirty and find champions
Here's my advice:
First and most critically: Stop treating AI adoption as an IT project and take personal responsibility for it. I've seen the biggest unlock when the CMO gets genuinely hands-on with AI. Don't delegate transformation to a committee or wait for IT to hand you a solution. Instead, sit down, build something, and understand from the inside what's hard and what isn't.
When you do that, a few things happen. Your team sees that leadership is serious. You earn credibility to make real calls about what to automate and what to protect. And you give your team the most important thing: permission to question how everything has always been done. That shift from "This is how we do it" to "Does this need to work this way" is everything.
Second, don't stop at productivity. It's the most accessible win, so almost everyone stops there. But productivity is the floor. Durable advantage comes from reinvesting that capacity into differentiation: sharper brand, more distinctive creative, the campaigns you never had bandwidth to think about before. If AI doesn't eventually show up in how recognizable and trusted your brand is, you've left the most valuable part untouched.
Finally, find and activate your champions. Bring skeptics in early. Celebrate those who build something new, not just those who use it most. Envy, it turns out, is a world-class adoption strategy.
Follow along
You can follow Diego Lomanto's work on LinkedIn and X.
More expert interviews to come on The CMO Club!
