Last Updated on November 11, 2026
Marketing automation has been part of the marketer’s toolkit for decades. We’ve used it to schedule social posts, trigger email campaigns, score leads, segment audiences, and move customers through predefined journeys. Much of that work follows rules: when something happens, take a particular action.
AI is expanding what we can automate.
Instead of defining every possible condition and response in advance, we can build workflows that interpret information, generate content, evaluate options, and make decisions within parameters we establish. That opens up opportunities to automate work that previously required a marketer to intervene at every step.
I’ve spent years working with marketing automation, and more recently I’ve been exploring how AI can improve the way my team operates. One of the biggest opportunities I see is moving beyond using AI for individual tasks and connecting those capabilities into repeatable workflows.
I’ve also been exploring this with N. Liberty White, founder of She Learns AI and an instructor in Udacity’s AI Marketer Nanodegree program. Liberty works with organizations and startups on their marketing processes and developed Udacity’s Automate Marketing Workflows with AI course. We compared experiences on where automation creates the most value, what makes a workflow worth building, and how to maintain quality and control as more work is delegated to AI.
The technology has become considerably more capable. The important question left to us is, how do we put it to work?
What is a marketing automation workflow, and what does AI change?
A marketing automation workflow is a sequence of connected tasks or actions that runs with limited manual intervention. A trigger starts the process, the system performs a series of steps, and predefined conditions determine what happens next.
Consider a familiar email marketing workflow. Someone downloads a guide, enters a nurture sequence, receives a follow-up email, and moves into a different segment depending on whether they engage.
Traditional marketing automation can already handle sophisticated branching logic, personalization, and lead scoring. That was all possible pre-AI.
What changes is the kind of work we can include within the workflow.
An AI-enabled workflow might interpret a customer’s open-ended question, identify which information is relevant, draft a response using an approved knowledge base, and flag uncertain answers for review. Instead of relying entirely on predefined categories and messages, the system can work with unstructured information and adapt its output to the context.
Liberty sees continuity between the old and new approaches. “In principle they’re the same,” she told me, referring to their shared purpose of reducing manual work and building capacity.
The difference is that today’s AI systems can take on more of the reasoning between steps. We can give them instructions, context, reference materials, and constraints, then allow them to determine how to complete parts of the process.
That doesn’t mean handing an AI system a vague goal and expecting it to run a marketing department. Effective workflows still need clear instructions, access to reliable information, appropriate permissions, and ways to evaluate their output.
In some respects, the setup becomes more important as the automation becomes more capable. A workflow that can make decisions also has more opportunities to make the wrong ones.
How to decide which marketing tasks are worth automating
Not every task that AI can perform should become an automated workflow.
Liberty’s approach starts with three questions: Does the work happen often enough to matter? Is there an established way it’s supposed to be done? And can you tell the difference between a good result and a bad one? If the answer to all three is yes, it’s worth examining further.
From there, she looks at risk and value. How much time is the task taking? How many people touch it? What happens when it’s delayed or done inconsistently? What does the AI need access to, and what happens if it gets something wrong? Does a human need to review the result every time?
Liberty pointed to recurring thought leadership content, ad development and testing, and standardized client responses as examples of work that often passes that test. But she’s also deliberate about the order of operations.
Her recommendation: map the work before you decide what to automate. That may sound obvious, but it matters because the first problem a team brings you is not always the problem you end up solving.
One organization came to Liberty knowing they had budget constraints and believing AI could probably help them with efficiencies and productivity — they just didn’t know where to begin. The initial assumption was that the team needed a better understanding of how to use the tools they already had.
Liberty’s first step wasn’t the tools. Before deciding what to automate, she wanted to understand how the work actually moved: who starts the process, who touches it next, where information comes from, where someone has to stop and ask another person for something, what requires judgment, and what information is sensitive.
As her team asked those questions, they found employees in different departments spending time doing similar client onboarding tasks from scratch, often in silos. There were also data sensitivity constraints, outside systems with their own limitations, and access restrictions that varied across the organization.
Spending a day workshopping with the team, followed by roughly two weeks mapping how the work actually happened, let them design around the real communication problem rather than the first AI idea that came to mind. The system they built was designed to reduce repeated communication across the client lifecycle, with the potential to save five to seven hours during the segmentation and onboarding process.
Where teams often get tripped up, Liberty says, is jumping straight to the automation before fully diagnosing the process, the bottlenecks, and the constraints around the work.
The same principle applies to more complex marketing work.
Example: Turning campaign planning into a repeatable workflow
When my team plans a campaign launch, we often work through a familiar sequence:
- Define the target audience or persona.
- Identify the problems and motivations that matter to that audience.
- Develop positioning that addresses those needs.
- Test the positioning through synthetic audience research.
- Generate creative concepts based on the positioning.
- Hand the concepts to designers for development.
We use AI to support parts of this process today, but we haven’t automated the entire sequence.
Instead, we often move between tools and documents manually. We might have positioning in a go-to-market document, upload it into an AI tool to generate concepts, then start another conversation to evaluate those concepts.
Each step can be useful independently, but there’s an opportunity to connect them.
Imagine a workflow that starts with an approved campaign brief, develops audience hypotheses, generates positioning options, evaluates those options against defined criteria, and produces a set of creative concepts for review, all without waiting for me to pass information between systems.
The important part isn’t simply having AI complete six tasks instead of one. It’s ensuring that the work at each stage informs what happens next.
If the audience research identifies a particular motivation, that finding should influence the positioning. If the positioning changes, the creative concepts should reflect the new direction.
This is where AI-enabled workflow automation becomes particularly interesting. We’re not just speeding up individual tasks. We’re enabling continuity and iteration throughout the process.
Of course, the workflow is only as good as the information and judgment built into it. Synthetic research can help explore and pressure-test ideas, but it isn’t a substitute for observing real customers. And a creative concept that satisfies an automated evaluation still needs human scrutiny before it goes into production.
Building reliable workflows starts with reusable instructions
One of the frustrations of using AI for marketing is how often we have to explain the same things.
We upload a brand guide, describe the audience, explain the product, specify the tone, and outline what good output looks like. Then we start a new conversation and do much of it again.
A more sustainable approach is to make that context reusable.
In her Udacity course, Liberty introduces Skills, which package instructions and reference materials so an AI system can draw on them when completing a task.
For example, a marketing team might develop three foundational skills:
- Brand Voice:: Guidelines, examples, and rules that help generated content reflect how the brand communicates.
- Product Accuracy:: Approved product information, differentiators, terminology, and claims that the system should use or verify.
- Platform Best Practices:: Requirements and conventions for different marketing channels and content formats.
These skills help address a familiar organizational problem: important knowledge often lives in people’s heads, scattered documents, or old conversations.
A reusable skill makes some of that knowledge accessible across workflows. It also reduces the need for every employee to recreate the same instructions.
Liberty’s course offers a useful rule of thumb: if you find yourself pasting the same instructions for the third time, consider turning them into a skill.
Connecting skills into a workflow
A skill isn’t necessarily a complete automation. It’s a capability that can be reused within one.
Liberty described how she works with client conversations and content. A transcript can become the source for several deliverables, including a YouTube script, blog outline, social posts, and newsletter content. Rather than treating each output as an entirely separate assignment, a workflow could extract the important ideas from the transcript, apply the relevant brand and channel instructions, and produce drafts for review.
The marketer is still making decisions throughout that process: Is this the most useful angle? Is the context right? Did the speaker actually say this? Is the claim accurate? Does this belong on LinkedIn, in an email, or nowhere at all? When you give the AI clearer parameters, it can take on more of the repetitive production work. That gives the marketer more room for the thinking, editing, and judgment that comes from experience.
The same approach could support my campaign-planning example.
A workflow might draw on approved audience research, product documentation, brand guidelines, and creative requirements. Each step would have access to the information it needs, without someone manually supplying that context every time.
There is a practical distinction here: a good workflow needs both a process and the knowledge required to execute it.
Connecting a series of prompts without solving for context, consistency, and quality control doesn’t necessarily produce a useful automation. It may simply produce mistakes more efficiently.
A simple check: Are the drafts more consistent than what you’d produce manually? Are they factually accurate? Do they sound like your organization? How much rewriting is still required? If you find yourself correcting everything, the workflow still needs work.
Where should humans stay involved in AI marketing workflows?
This is probably the question I spend the most time thinking about.
I’m increasingly comfortable letting AI make certain decisions that I would previously have made myself. For example, as I’ve gained experience with synthetic research and its limitations, I’ve become more willing to consider creative directions that AI proposes and evaluates.
But I still want a person making the final decision about what goes into market.
Part of that is creative judgment. AI can produce plausible copy that doesn’t sound particularly natural, or that misses something important about the audience.
The other part is evaluation. If we’re going to let AI make decisions independently, we need a reliable way to recognize when those decisions aren’t producing good results.
Until we have that mechanism in place, I want a manual review step.
Put review checkpoints where mistakes become expensive
For the campaign-planning workflow I described earlier, my ideal checkpoint would be after the concepts are developed but before they’re handed to the design team.
Once a designer starts working on a concept, we’re committing someone else’s time and resources. If I only review the finished creative and decide it isn’t right, we may have wasted that effort.
Worse, the problem might not be the design at all. It could be the positioning or creative brief that informed it.
Reviewing at the handoff gives us an opportunity to correct the direction before the next team invests in it.
Liberty frames it in terms of consequences: What would it mean to get this wrong? Is the output customer-facing? Does it make a claim? Does it involve sensitive information? Could it commit spend or create more work downstream? The higher the stakes, the more intentional you need to be about where the human checkpoint lives.
That doesn’t mean every automated step needs approval. It means identifying the points where a bad decision would create meaningful downstream consequences.
A polished AI output can still be wrong
I recently worked on a series of scripts for UGC videos we’re producing. Reading the scripts individually, many of them seemed perfectly reasonable.
But when I reviewed them together, I noticed a problem: several were making essentially the same argument.
We needed to develop multiple videos to speak to different audience motivations. If every script addressed the same pain point, we weren’t getting the creative variety we needed.
I also caught a more nuanced issue: scripts that focused too much on building individual agents missed out on communicating the program’s more impactful value prop: multi-agent orchestration. Someone unfamiliar with the course might not notice the difference. But it matters to the audience we’re trying to reach.
This is where reusable product knowledge can help. A Product Accuracy skill containing approved course differentiators could give the AI better context and provide criteria for checking the output.
It wouldn’t guarantee that every mistake gets caught. I’d still want a marketer who understands the product and audience reviewing the work. Automated quality checks can reduce the burden of review without eliminating the need for judgment.
Build guardrails into the workflow itself
Liberty makes another useful distinction between asking an AI system to behave safely and designing a workflow that limits what it can do.
Suppose an automated process generates email content. You could instruct the AI to request approval before sending anything.
A stronger safeguard would be to give the workflow permission to create drafts but not permission to send them.
That way, human approval isn’t merely an instruction the system is expected to follow. It’s part of the workflow’s architecture.
The same principle applies to publishing social content, modifying campaigns, or changing customer-facing information.
For internal research and analysis, a team may be comfortable allowing more autonomy. For work that reaches customers or commits significant resources, the permissions and approval process should reflect the greater risk.
How to measure whether an AI marketing workflow is working
It’s easy to become enthusiastic about the number of tasks you’ve automated, but the number of automations isn’t a particularly useful measure of business value.
Liberty recommends thinking about four distinct things.
Quality and consistency. The goal isn’t faster output; it’s better output. Liberty recently worked on a client newsletter where the AI could produce a draft quickly, but it was pulling in claims that needed correction and missing the emotional nuance the audience required. By the time the team finished rewriting it, the speed of the first draft didn’t mean much. That problem compounds when the content feeds other parts of the marketing process — the same weak positioning or inaccurate claims can carry through into scripts, social content, and email. A useful evaluation: Is the work accurate? Does it meet the brand standard? Does it actually speak to the audience? How much rewriting is still required?
Operational efficiency. Here, Liberty looks at what actually changed in the process. Did you remove unnecessary steps? Did you reduce the amount of searching, organizing, or decision-making someone has to do manually? One example from her own practice: a Priority Skill that takes the tasks connected to a project, looks at the timeframe and priorities, and helps organize what needs attention first. The value isn’t that AI makes the decision. It’s that it reduces the time and mental energy spent sorting through information before you can begin.
Human adoption. This is the area Liberty says organizations most often underestimate. She worked with a marketing team that ran recurring offers for its community and was looking to reduce manual steps in the process. The challenge was that the new workflow required more front-end work and asked people to let go of steps they were accustomed to doing. It also exposed a deeper issue: a lot of the institutional knowledge behind the process was concentrated in a few people. When some of them moved into other roles, parts of that knowledge went with them. Workflow design has to account for this. You have to ask what knowledge needs to be documented, how much behavior change the new process requires, and whether the people doing the work understand how the workflow helps them. The practical question: Are people actually using it? Are they comfortable enough to bring new ideas back?
Business performance. This should connect back to what the team was trying to improve in the first place. Liberty worked with a small edtech company that needed to develop and test more variations of its messaging with a small team, some of whom weren’t traditional marketers. AI helped them develop campaign variations more quickly, test different messaging, and learn from what was landing. The measure of success wasn’t volume of content produced. It was whether they could test more ideas, get them to market faster, and whether the people doing the work were becoming more capable. Liberty makes a point worth keeping: not every AI workflow needs to prove direct revenue. An editing workflow and a lead-follow-up workflow have different jobs. The measurement should match the reason the workflow exists.
At the team level, it’s also worth periodically asking: Who is actually using the workflows? Is the quality of the work maintaining or improving? Are people confident enough to bring new ideas and use cases to the table? Those questions tell you whether AI is becoming part of how the team works, or whether it’s still something a few people are experimenting with on the side.
I’d also establish a baseline before introducing the automation. Compare the time, cost, and quality of the existing process with the new one, rather than relying on the impression that everything feels faster.
And don’t overlook implementation costs.
Liberty has encountered organizations concerned about data privacy, employee adoption, content sounding robotic, and the expense of connecting different systems. Those are legitimate considerations.
A workflow that saves a few hours each month may not justify a complicated integration or significant ongoing software costs.
The goal is to make marketing operations better, not simply more automated.
How to build your first AI marketing automation workflow
If you’re new to this, I wouldn’t start by trying to automate an entire campaign.
Liberty’s framing is useful here: build the foundation, find the friction, build one system, and show the proof. That means understanding how the work gets done today and where it creates friction, then choosing one of those problems and building something useful around it. Before moving on, you want proof: What did you stop doing manually? What actually improved? Where did the workflow break? What would you change before building the next one?
Start with something that has two steps.
For example, suppose you check the same performance dashboard every morning. You could build a workflow that:
- Retrieves the latest data and identifies meaningful changes.
- Emails you a short summary of those changes.
That’s a useful starting point because the process is familiar, the output is easy to inspect, and you can compare the AI’s observations against the underlying data.
You can then improve it gradually. Perhaps the summary begins comparing performance with the previous week, highlighting unusual changes, or flagging metrics that need investigation.
Once you’re confident in the results, you can consider whether to connect the workflow to other tasks.
Before you build, I’d answer a few questions:
- What starts the workflow? A schedule, a new file, an event, or a manual request?
- What information does it need? Where will it retrieve that information, and can it access it reliably?
- What should it produce? Define the expected output and what acceptable quality looks like.
- What can it do without approval? Decide which actions are permitted and where a person needs to intervene.
- How will you know it worked? Identify both the technical checks and the practical outcome you’re trying to improve.
One final piece of advice: don’t give up too quickly when the setup gets difficult.
I’ve encountered plenty of situations where an AI tool tells me an integration isn’t supported or that a particular approach won’t work. Sometimes that’s a genuine limitation. Other times, changing the sequence of steps, using a different connection, or reframing the task gets you to a workable solution.
It’s worth pressing the tool to explain the constraint and explore alternatives rather than accepting the first answer as definitive.
At the same time, set reasonable usage limits, particularly when experimenting with API-based workflows or recurring tasks. You want room to learn without accidentally creating an expensive process.
The next step for marketers isn’t just using AI. It’s building with it.
Most marketers now have some experience using AI to write, research, brainstorm, or analyze information.
Those capabilities are valuable on their own. But there’s a larger opportunity in learning how to connect them, supply the right context, and build processes that can operate reliably without constant supervision.
That requires more than knowing how to write a good prompt. It requires understanding the work well enough to break it into steps, decide which judgments can be delegated, establish quality standards, and recognize where human oversight adds value.
It’s also a skill that becomes more useful as you build it. The first workflow may be small. The instructions, reference materials, and evaluation methods you develop can then support more ambitious applications.
Liberty’s Automate Marketing Workflows with AI course at Udacity explores this approach in practice, including how to create reusable marketing skills, set up recurring workflows, and build safeguards into automated processes. It’s part of Udacity’s AI Marketer Nanodegree program, which covers other core AI Marketing skills like GEO, applied AI operations, and AI fundamentals.
You don’t need to start with a complicated multi-agent system. Start with a repetitive task you understand well, automate a couple of steps, and learn from the results.
