A while ago, Mike Rizzo—founder of MarketingOps.com and author of the landmark "State of the MO Pro" research—asked a pointed question [on LinkedIn]
Key Takeaways
The poll got tons of responses. At least 91% of people wanted to see how companies were measuring AI ROI but only 6% of those people were willing to share their own ROI. The numbers were thought-provoking. The comments beneath them were even more revealing.
According to Many Hornaday, “…it’s a systems problem. A lot of the problems that have existed in marketing for years (maybe decades) didn’t just get solved overnight with AI. If anything, AI has amplified a lot of our core issues.”
And Rutger Katz said, “Some of the measurement infrastructure needed to prove AI ROI is the same infrastructure needed to run a clean GTM operation without AI. If you can’t connect activity to pipeline today, AI didn’t create that problem. It just made it more visible and more expensive.”
What those comments described—from people actively working in Marketing Ops—was a profession in the middle of a real reckoning. AI has amplified a lot of the problems that already existed within our systems. Now, the ways people were managing to barely keep it together are finally falling apart.
AI tools are everywhere, with 88% of marketers already using AI to stay competitive in the market according to MarTech.org. But ask most MOps professionals whether their team is ahead of the curve, behind it, or somewhere in the middle, and the honest answer is: they don’t know.
That gap between activity and understanding is what we want to address.
The conversation about AI in Marketing Operations tends to fall into one of two camps. Either it’s breathless optimism—AI will transform everything—or it’s quiet skepticism — we’ve adopted tools but haven’t seen the payoff yet.
What’s missing from both is data.
When Mike asked whether MO pros were seeing ROI, many comments said some version of “there isn’t enough clarity around what AI has actually produced.”
That uncertainty isn’t a failure. It reflects a real structural problem: there’s no shared benchmark for what “good” AI adoption or results look like in Marketing Operations. There’s no standard for how to measure. And without that, even teams doing impressive work can’t tell if they’re leading or lagging.
This community approaches technology through the lens of systems architecture rather than impulse buying. For these professionals, integration capability is a primary driver—outranking price nearly two-to-one—because they understand that AI adoption is a systems-thinking challenge rather than a tactical implementation.
The poll results and comments pointed to a few recurring themes:
ROI measurement is inconsistent. A few respondents described clear wins. More described a general sense that things were faster or better, but they have no formal measurement framework to prove it.
Career implications are real and underappreciated. Employers are starting to ask about AI experience. MOps professionals who can’t speak fluently to their team’s AI adoption—and specifically to what they personally drove—will face a meaningful disadvantage.
The best teams have clarity. The gap between high-maturity and low-maturity AI adoption in Marketing Operations is widening, while adoption for the sake of adoption is coming to an end. C Suites are starting to ask where exactly is the ROI from all the AI investment, and the best MO pros need to have real numbers at the ready.
Here’s what most conversations about AI adoption miss: the goal isn’t to use AI. The goal is to use AI in ways that produce measurable value, and to be able to demonstrate that clearly.
Without a benchmark, you can’t do either reliably.
When a future employer asks about your team’s AI practice, a list of tools you’ve used is not the same as a clear picture of how your adoption compared to your industry peers and what you built from that position. When your CMO asks whether your MOps team is leveraging AI effectively, “we use it for a few things” is a very different answer than “here’s where we stand relative to similar organizations, and here’s our roadmap.”
That’s the gap the AI Advantage Assessment is designed to close.
MarketingOps.com and Crowley Consulting built the AI Advantage Assessment as our one and only 2026 Pulse Report.
It’s a detailed survey designed to give Marketing Operations leaders an honest benchmark of their AI adoption relative to similar teams — an actual comparison, with data from professionals doing similar work at similar scales.
Here’s what participation gets you:
Your individual results stay private. We won’t share your responses with anyone.
If you lead or work within a Marketing Operations function at any level, this survey was built for you. The data is most useful when it reflects the full range of where MOps teams are operating today: early-stage AI exploration, structured programs, and everything in between.
The survey is detailed by design. The results are only as useful as the inputs. Set aside 15 minutes and record your team’s actual current state as honestly as you can.
Beyond individual results, the aggregate data from this assessment will give the MarketingOps.com community something genuinely useful: a real picture of where the profession stands on AI adoption in 2026.
That’s data you can use in conversations with leadership. It’s context you can use to evaluate your team’s direction. And it’s a foundation for the next wave of conversations in our community about what AI in Marketing Operations actually looks like in practice.
The AI Advantage Assessment is open now. Members receive personalized results. Non-members receive the aggregate Pulse Report.
If you have questions about the assessment or want to learn more about how the results will be used, reach out to us directly at info@marketingops.com.
How can MO Pros measure the ROI of AI tools? Measuring AI ROI requires tracking specific outcomes like time saved on automation, increased campaign throughput, or reduced error rates in data orchestration. It moves beyond identifying tool capabilities to valuing the efficiency and scalability gains within the MOps stack.
What is the AI Advantage Assessment for Marketing Operations? The AI Advantage Assessment is a specialized survey developed by MarketingOps.com to help teams benchmark their AI maturity against industry peers. It provides a detailed report that evaluates infrastructure, adoption levels, and tactical next steps for improving AI performance.
Why do MOps teams need an AI benchmark? Without a standardized benchmark, it is difficult for MOps leaders to justify AI investments or explain their team’s maturity level to executive leadership. Benchmarking provides a objective baseline to compare performance and identify structural gaps in GTM operations.
For further insights into the rise of the GTM architect and the economics of professional community, explore Mike Rizzo’s leadership commentary at Fast Company.
MOps-Apalooza is designed to bring the Marketing Ops community together for two days of learning, networking, and practical conversations. This year, attendees can participate through one of two experiences: the MO Pro Member Experience or the Invited Guest (Free) Experience. While both tracks come together throughout the event for keynote
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