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, President of Data and Technology Services at Trilliad 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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Tarrell 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. She shares how it's not uncommon in a migration scenario, for the team to comment on the functionality of existing automations.
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 more due to the way automations get 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.
But, then if a nurture sequence misfires, someone eventually notices the conversion rate drop and they start digging. And 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.
”If the inputs are vague, if an unclear brief, muddled positioning, or missing proof, AI cannot rescue it.
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 isn't visible in dashboards. It shows up in migration timelines that stretch from three months to nearly two years.
Tarrell notes how it appears when a team needs to rebuild a qualification workflow and realizes they don't have a shared definition of what qualified means. It's also apparent in AI pilots that produce impressive demos but don't translate to pipeline because the underlying data is unreliable.
This problem compounds across teams. Trilliad's 2025 Sustainable Growth Study found that 44% of growth leaders identify the lack of coordination among marketing, sales, and customer success as their single biggest roadblock. The automation running in the background isn't creating they alignment teams need. And, in many cases, it's reinforcing the silos.
The data problem runs deeper than org charts. When data lives in disconnected systems, it can't be used across the full customer journey to create connected experiences or power coordinated efforts among growth teams.
The same study found 40% of organizations are still only using their data for acquisition, meaning a significant portion aren't activating it across the lifecycle at all.
For teams hoping to use AI to power tailored experiences, surface intelligent insights, or streamline operations end-to-end, this is a meaningful gap.
Especially when 44% of B2B organizations cite cross-team misalignment as their primary growth blocker, according to Trilliad's own research. And only 22% say they've achieved real integration across Sales, Marketing, and Customer Success.
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?
"How do they know where to look? How do they organize their day? Does the screen support mobile activity?" she asks.
These are basic usability questions that nobody had thought to ask because everyone assumed the platform was working as intended.
Why AI Adoption Exposes This Faster Than Anything Else
There's a reason AI pilots fail. You can't blame the models. It's often the systems that 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 rushing toward agentic AI, toward autonomous workflows and AI-driven outreach. But they've yet to tackle the the foundational work to understand and clean what they already have. Edge cases start appearing, and the team can't diagnose the problems because they lack a clear picture of how the underlying systems work.
Proving the ROI of AI extends beyond reporting. Often, the issue is due to data infrastructure. Trilliad's 2025 Sustainable Growth Study found that 42% of respondents say measuring their impact on revenue is their greatest challenge.
So, if growth teams can't prove the ROI of their existing campaigns, connecting AI spend to business outcomes becomes nearly impossible without the integrated data systems to support it.
The goal is to make sure your AI is working based on solid, trustworthy information," Tarrell said. "And that takes time and intention.
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, all are 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 invest time in mapping 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.
Your Marketing Ops person may have built it. But then, the original marketing 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, instead write it in the most simple form. What happens when a lead comes in? Then, what triggers a handoff to sales? And, whathappens when the lead doesn't respond? Who owns each step of the customer journey? And, 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 inherited, rather than owned, and they haven't looked at it clearly enough to know whether it's still working.
What’s Next?
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