Your Automation Is Running And Nobody Knows How It Works
Understanding Workflows: Many marketing teams struggle to explain their automation workflows, leading to inefficiencies and confusion.
AI Impact: Poorly understood automation risks amplifying errors as AI adoption increases, complicating marketing outcomes.
Migration Issues: Lack of documentation in automation can lead to lengthy migration processes and misaligned definitions across teams.
Governance Challenge: Automation that lacks ownership poses governance risks, especially when integrating AI systems into existing workflows.
Preparation Steps: Before adopting AI, document your lead-to-revenue process and ensure team members understand existing configurations.
Ask a marketing team to explain how their own marketing automation workflows work. Go on, I’ll wait. Not the high-level version, where someone describes what they're supposed to do, but the mechanics behind the machine..
Can you answer, what triggers this sequence? Or who built this routing rule, and why? How about, what happens when a contact matches two segments at once? It might not be surprising to hear that you get blank stares more than detailed answers. Put simply, there’s a better way.
Andrea Tarrell has spent her career at the intersection of marketing and revenue technology, and she's seen this play out more times than she can count.
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As President of Tech Services at Trilliad, she works with B2B organizations trying to get value from the platforms they've investing in. When migration conversations come up, there's a common thread among teams. "It's not uncommon in a migration scenario," she said, "for the team to say,
We actually don't even really know how this works. This was just running in the background, nobody's really sure how it was created, who built it, what it does.
This is not a small problem. And with AI adoption accelerating, it's about to become a much bigger one.
The Ghost in the Machine
Her experience summarizes the operational reality of many mature marketing organizations. Not because the teams running them are negligent, but because of the way automation gets built.
Accumulating logic, branching sequences, suppression lists, and lead routing rules the way old houses accumulate wiring. Functional, mostly, but understood by fewer people than anyone would like to admit.
Marketing automation has been mainstream for well over a decade. Most mature B2B marketing organizations have a version of a marketing automation workflow or program that's been running for years.
For a long time, it's worked. If a nurture sequence was misfiring, someone would eventually notice the conversion rate drop and go digging. But now, AI changes the trickle-down effects considerably.
When organizations start layering AI-powered scoring, enrichment, or agent-driven outreach on top of these systems, they're not just adding new capabilities.
They're amplifying whatever logic is already underneath. Clean inputs, thoughtful segmentation, and well-designed routing rules produce faster, more precise results. Fragmented data, inherited assumptions, and forgotten suppression logic get amplified too. This is a recipe for disaster.
Tarrell says it mildly,:
If the inputs are vague, if an unclear brief, muddled positioning, or missing proof, AI cannot rescue it.
Her quote was about campaign production, but the principle applies everywhere.
The Cost Of Running Automations On Auto-Pilot
There's a reason most teams don't know how their marketing automation works. Building it took significant effort, and the people who built it often moved on. The institutional knowledge walked out the door, and the systems kept running. Nobody had a strong enough reason to go back in.
The real cost of this isn't visible in dashboards. It shows up in migration timelines that stretch from three months to nearly two years. It shows up when a team needs to rebuild a qualification workflow and realizes they don't have a shared definition of what qualified means. It shows up in AI pilots that produce impressive demos but don't translate to pipeline because the underlying data is unreliable.
Tarrell's team at Trilliad conducted what they call a seller experience audit, evaluating Salesforce from the perspective of a sales rep going about their actual day. The findings tend to surprise people.
"How do they know where to look? How do they organize their day? Does the screen support mobile activity?" she asks
Basic usability questions that nobody had asked because everyone assumed the platform was working as intended.
Meanwhile, 44% of B2B organizations cite cross-team misalignment as their primary growth blocker, according to Trilliad's own research.
Only 22% say they've achieved real integration across Sales, Marketing, and Customer Success. The automation running in the background isn't creating that alignment. In many cases, it's quietly reinforcing the silos.
Why AI Adoption Exposes This Faster Than Anything Else
There's a reason AI pilots fail. Not because the models aren't capable, but because the systems they're connecting to aren't ready. You can give a language model excellent instructions and still get poor outputs. For instance, if the CRM data feeding it is incomplete, the audience definitions are murky, or the signals it's supposed to act on have been configured by someone who left two years ago—you’re not exactly equipped for success.
This is the pattern Tarrell sees repeatedly. Organizations that rush toward agentic AI, toward autonomous workflows and AI-driven outreach, without doing the foundational work to understand and clean what they already have.
The pilot looks promising and edge cases start appearing. But the team can't diagnose the problems because they don't have a clear picture of how the underlying systems work.
The goal is to make sure your AI is working based on solid, trustworthy information," Tarrell said. "And that takes time and intention.
That time and intention aren't glamorous. Documenting workflows in plain language, mapping what each automation is supposed to do and whether it's doing it, auditing data quality before connecting new tools, are all tedious tasks that don't show up in an AI transformation announcement. But teams that skip them are building on sand, and they'll find out eventually.
The teams that do the work, that actually map their lead-to-revenue process step by step in plain English before touching a new tool, move faster when they do implement. Tarrell's advice is pointed—get the workflows documented at the level of "what do we want to have happen, and what do people do versus what does the technology do." This clarity is the prerequisite for everything else.
The Harder Conversation About Ownership
There's another layer to this problem. When automation runs unexamined in the background for long enough, it stops belonging to anyone.
Marketing ops may have built it. But then, the original marketing ops lead left. The new person inherited it and learned just enough to keep it from breaking. The current people using your marketing automation is long removed from anyone who understood the original logic.
Nobody wants to touch it because there's a risk in breaking something you don't understand. The alternative, of course is to leave it and pretend it's working.
This is a governance problem as much as a technical one. And AI adoption is forcing the issue.
You cannot responsibly connect an AI agent to a system you don't understand. The risk profile changes significantly. Data gets processed, shared, acted on. Mistakes stop being "the conversion rate was a little low this quarter" and start being something more serious.
Tarrell frames the solution in terms of agility and curiosity, the two skills she says every marketer needs now. Agility to look at what's working and pivot quickly when it's not. Curiosity to keep asking why something is the way it is rather than accepting inherited logic as fixed.
"If you don't know the answer, continuing to chase that until you get clear on it," she said. "That's just going to be increasingly important."
Looking Ahead Over The Next 12 Months
If you run marketing for a B2B organization and you're planning AI adoption in the next twelve months, there are a few things worth doing before you start evaluating tools.
Get someone to map your current lead-to-revenue workflow in plain language. Not a system diagram, plain language. What happens when a lead comes in? What triggers a handoff to sales? What happens when they don't respond? Who owns each step? Where does the process break down most often?
Then go one level deeper. For each of those steps, find out whether anyone on your current team actually understands how it's configured. Not in theory. In practice. Who would fix it if it broke today?
The answer to that second question will tell you more about your AI readiness than any vendor assessment.
"The right foundation," Tarrell said, "is the one your team will actually use and build on."
That sounds obvious. Most organizations discover, usually at the worst possible moment, that their foundation is something they inherited and never examined.
What’s Next?
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