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Author: Eric Keller

What CMOs Really Want From Marking Ops in 2026

Posted on by Eric Keller

2026 is right around the corner!

For Marketing Operations pros, having a solid plan that’s ready to go makes all the difference when the year kicks off.

But the challenge isn’t just building a solid plan; it’s building one that resonates with the priorities your CMO and leadership team actually care about.

So what should MOPs folks focus on when planning for the year ahead? Here’s what leadership will want to see at a high level.

1. Predictable Pipeline

Pipeline uncertainty is one of the biggest concerns for your leadership team. Most companies struggle with foundational questions around their buying personas, purchase timing, and the length of the buying process. And when these fundamentals aren’t nailed down, forecasting becomes guesswork.

When you have the right measurement structure in place (tracking everything from lead creation to MQL to opportunity to won), you can backtrack through your conversion rates by channel to determine exactly what’s needed to meet targets.

For example, if your MQL-to-opportunity conversion rate is approximately 10%, you need about 1,000 MQLs to close 100 deals. Similarly, if your lead-to-MQL conversion rate is approximately 25%, that means you need about 25,000 new leads to get there. This kind of structured thinking with tangible metrics is exactly what resonates with leadership. Your plan should include a process for improving (or implementing) this type of tracking throughout your entire funnel.


2. Attribution Clarity

Achieving a predictable pipeline is only possible when you know where your wins are actually coming from.

Attribution is a deep topic with complex models (we won’t go into that here), but at a high level, attribution clarity means understanding which channels generate your best net-new leads, which ones convert to MQLs and opportunities, and where your marketing dollars are working hardest.

The value becomes clear when you compare channels. If you’re spending the same amount on two different ad platforms but one generates ten times more opportunities, the budget decision becomes straightforward.

Attribution clarity removes the guesswork from allocation decisions, and that’s something every C-suite appreciates.


3. AI Governance

AI is on every executive’s radar right now, and governance has become a major concern.

AI governance is complex. But at its core, it’s about ensuring your organization uses AI safely, ethically, legally, and effectively, while staying in control of risks and outcomes. It is crucial to boost customer trust, prevent legal violations, keep your business audit-ready, and reduce the risk of operational chaos.

Many organizations are finding that security and governance reviews for AI tools take weeks or even months to complete (this is completely natural and expected, given how new this space is).

But this also presents an opportunity: MOPs professionals who proactively address AI governance in 2026 plans with have a head start and will definitely stand out. AI implementation the right way, with an emphasis not only on productivity gains but proper security, safety, and privacy, is key.


4. Efficiency Gains and Automation ROI

Every budget request eventually comes down to ROI. If you’re asking leadership for $50,000 for a new tool, for example, their response will likely be primarily focused on how you’ll make that money back. Leadership will normally have no problem spending budget if the ROI is clearly demonstrated in a robust plan of action.

So, as a MOPs team, efficiency gains and automation initiatives need to be framed in terms of return on investment. When you tie your initiatives directly to revenue impact, you make it easier for leadership to say yes.


5. Data Quality Enabling GTM Strategies

We’ve said this many times before, but it’s more important now than ever:

Clean data is the foundation of everything else on this list.

Without it, pipelines become unpredictable, attribution models become unreliable, and your go-to-market strategies are built on shaky ground.

For example: If you mislabel the source of your leads, your main MQL-driving channel is incorrectly tagged. This means you’ll end up investing in the wrong place, wondering why results aren’t materializing. Data quality isn’t a technical nice-to-have; it’s a strategic necessity.


6. MOPs as Risk Management Insurance

Frame your MOPs team as the CMO’s “risk management insurance”.

What do we mean by that?

CMOs focus on pipeline, performance, and strategy. The only reason any of that moves is because MOPs keeps the engine stable, connected, and compliant on a daily basis.

We are the people who prevent the silent failures: broken integrations, bad data flows, sync delays, deliverability issues, API limits, rogue automations, and privacy violations that can ruin a quarter before anyone even sees the warning signs.

And risk management is not only technical. MOPs protects the brand by making sure emails land in the inbox, consent and regional privacy rules are respected, and every system behaves predictably.

Without this foundation, campaigns fail, reporting collapses, and the business loses credibility.

MOPs is the safety net and the operational guardrail that keeps marketing performing and keeps the company out of trouble.

If you frame your 2026 initiatives around these six areas, you’ll have a plan that speaks directly to what leadership cares about.

That connection between your work and executive priorities is what makes the difference when budget conversations come around.

If you need help putting together your plan or reaching your MOPs goals for the year ahead, reach out to us. We’d love to chat!

Where Does AI Fit Into Your 2026 Budget?

Posted on by Eric Keller

If you’re currently planning your 2026 budget, AI is probably somewhere on the list.

And if it’s not, it should be. This past year has shown us that AI tools are more accessible than ever, use cases are clearer, and implementations are delivering measurable results.

The organizations planning for it now will be further along, while others will still be figuring out where to start.

But where exactly does AI fit into next year’s budget, and how do you make a case for it to leadership?

These are the questions we’ll be answering in today’s article. Let’s get into it.

One thing we want to make clear from the get-go is that AI budgeting isn’t about building a roadmap. It’s about identifying specific use cases, understanding the infrastructure they require, and being clear about both the costs and the gains.

  1. Know Where You Stand

If you’re not sure where your organization currently stands in regards to AI usage and implementation, start with our AI Assessment Tool.

After completing a short questionnaire, the tool will automatically generate a personalized report that details:

  • Where your company currently stands regarding AI adoption readiness
  • A customized roadmap your company can follow for successful AI integration.

Once you have that baseline, the rest of this guide will help you think through how to move forward.

  1. Identify Use Cases & Frame Them Around Gains

This is where you get specific. Using the AI Assessment report as a guide, look for AI use case opportunities that fall into two categories:

  • Operational efficiency: Workflow acceleration, automated processes, fewer hours spent on repetitive tasks, etc.
  • Customer experience: Improved marketing that drives more revenue, in the form of smarter personalization, faster response times, improved engagement, etc.

And don’t try to solve everything at once. Pick one or two high-impact opportunities where AI can make a measurable difference, then expand later as needed.

Once you have your use cases, leadership needs to understand the value. Before you talk cost, focus on tangible outcomes. Leadership approves AI projects when they see clear gains. Know which category of “gains” your use case falls into and frame your ask accordingly. The clearer you are about the outcome, the easier it is for leadership to say yes.

  1. Understand the Real Costs

We see AI costs are often underestimated. A single-user subscription to something like ChatGPT Enterprise might run $25/month, but that doesn’t enable robust automation. Automation requires tokens, and tokens come with an entirely different pricing structure. Each major LLM handles this a bit differently, but most of them will provide a rate per 1 million tokens. Whether you’re using OpenAI, Claude, Gemini, etc., check the rates that apply to you.

Aside from usage rates, there are other important factors to consider:

  • Implementation costs: Whether you build internally or work with a partner company, implementation takes time and money. If you pull internal resources, those team members won’t be doing their normal jobs while they build AI workflows (and if you’re working with an agency or consultant, factor that into your budget from the start).

  • Adjustment period: New processes cause temporary productivity dips you should plan for when building a timeline. If you implement a new workflow in June, don’t expect to see full gains until at least a few weeks later. We recently published an article centered around preparing your team (and tools) for AI that can help with this.

  1. Choose the Right Type of Solution

It’s important to remember that not all AI solutions are created equal. The type of solution you choose has real implications for cost, flexibility, and long-term viability. The nature of your AI use case opportunities (that you identified in tip #2 above) will guide which solution type is necessary. We’ll break these solutions into 2 categories as well:

  • Point solutions are products that do one specific AI-powered thing. They’re faster to deploy and often cheaper upfront. But customization is pretty limited, and you may outgrow the tool as your requirements evolve. These align with Levels 1-2 of the adoption framework from our AI Assessment tool: where individuals or teams are using AI tools but without deep integration into their processes.

  • Custom implementations are built specifically for your stack and workflows. They’re more flexible and tailored to your environment. But they require more investment upfront, a longer implementation timeline, and ongoing maintenance. These represent Levels 3-4 in the adoption framework: where AI is embedded into your operations with custom integrations designed for your specific needs.

There are some solutions that sit in the middle of these two categories. Our AI teammate for Marketing Ops, Otto, is built on a structured foundation, but it’s assembled bespoke for each client’s unique tech stack and business needs. It knows how to behave inside your unique platform, which steps to take, and what the limitations of certain API calls are.

As we head into 2026 at full speed, it’s important to think strategically about where AI fits into your operations and how that is reflected in your budget. Use our AI assessment tool to get you started, and follow these tips to build a framework that makes sense for your organization (and leadership team).

If you’re still not sure where to begin or want to talk through what this might look like for your specific situation, we’re here to help.

This is exactly the kind of conversation that’s worth ASAP,  rather than six months from now when you’re stuck playing catch-up.

How to Prepare Your Tools and Team for AI Agents

Posted on by Eric Keller

Our last article covered how marketers can build their own Marketing Ops assistant using MCP (an invaluable skill that will give you a head start going into 2026).

But building your own MCP is only half the battle.

In order to fully utilize AI agents and assistants, your systems and team members must actually be ready for them.

What do we mean by that?

Think about hiring someone new to join your team. It will likely take at least a few weeks for them to work effectively in your systems. AI tools are no different. They’re trained on general knowledge, but they have never seen your specific, unique setup. They need documentation and training data to make a real difference.

And your team members must be prepared as well. Specific processes will completely change. Time will be freed up and reallocated in different areas. And certain roles will benefit immediately from your AI agents, while other roles may not notice the initial impact. Expectations need to be calibrated from the get-go.

If this all seems like a lot, don’t worry.

We’ll walk you through both sides: getting your systems ready for AI, and getting your team ready for the shift.

What we're covering in this article:

Preparing Your Systems

Let’s start with the basics. These best practices are going to be essential in order for your AI agents to navigate your system effectively. Let’s run through them.

  • Naming Conventions: AI agents read program names, folder names, and asset names to understand context. If you have programs named “Test_v3_Final_REALLY_FINAL,” the AI won’t know what to do with them.
  • Templatization: Create consistent templates for each program type. AI agents need patterns to identify and follow.
  • Tokenization: The more you use tokens instead of hard-coded values, the more an AI agent can help you. Tokens are variable placeholders that AI can understand and manipulate.
  • Data Hygiene: Clean up duplicates, standardize values, and maintain field integrity. AI agents will perpetuate bad data if that’s what they find.
  • Clear Descriptions: Use description fields everywhere. On programs, smart campaigns, and assets. AI agents can read these much faster than humans can, and they provide critical context.

It’s also very important to make it crystal clear when something is legacy content.

You can achieve this by doing things like:

  • Create an “Archive” or “Legacy” folder
  • Add “Z_” to the beginning of old program names
  • Use description fields to mark deprecated templates

If you don’t clearly mark legacy content, your AI agents might start using outdated templates or get trapped following obsolete processes.

Creating Solid Documentation

With those best practices in place, let’s move on to documentation. This is going to be your AI agent’s training manual. This is a crucial step that will help your agents learn your unique system (just as you would train any new teammate coming on board).

#1 Process Steps

Explain each process as clearly as possible, step-by-step. Remember: You’ve probably looked at countless marketing automation instances in your career, and none of them follow the exact same process. They all have peculiarities that AI agents won’t magically understand. Specific documentation fixes this.

#2 Unspoken Rules

These are the “tribal knowledge” items that exist in our heads as a result of our experience and interactions with other folks at your company or in the industry. They are important rules, but ones that AI will never be able to intuit. Be sure to document as many of these as you can.

These could cover a wide array of things, for example:

  • “We always clone that one program from 2014 for this specific use case”.
  • “For partner webinars, use the Partner template, not the standard template”.
  • “If the campaign name contains ‘Exec’, it goes in the Executive Events folder”.
  • “We never use the green email template because it has formatting issues”.

#3 Make it AI-Friendly

AI models struggle with negative instructions. Meaning, instead of “Do not do X”, write it as “Instead of X, do Y”.

Here’s a real-world example to illustrate this further.

Bad: “Do not use the blue template for executive events”. Good: “For executive events, use the gold template instead of the blue template”.

And make sure you use explicit instructions when dealing with AI. Be specific about what should happen, not just what shouldn’t. The more explicit you are, the less room for erroneous interpretation there is.

#4 Get Video Transcriptions

A lot of internal documentation exists as videos or recorded training sessions. AI agents can’t watch videos (yet), but they can definitely process transcripts and screenshots.

Go through your resources and transcribe training videos (several AI services can do this for you. We like Descript), extract key screenshots, and convert visual walkthroughs to written steps.

#5 Document API Steps

Now we’re getting into some more advanced stuff, but it’s incredibly helpful for AI agents to have. Go ahead and document the specific API calls needed for each of your processes.

For example, this may look something like:

To update program tokens:

  1. POST to /rest/asset/v1/program/{id}/token.json
  2. Required parameters:
    • name: token name
    • value: new value
    • type: text/rich text/score/date
  3. Authentication: Bearer token in header
  4. Expected response: 200 OK with updated token details

AI training becomes much easier (and far more effective) when you have this level of documentation to feed it.

Preparing Your Team
(and Yourself)

We alluded to this in the intro: it’s really important for you and your team to calibrate expectations and prepare for what the introduction of AI agents actually means for both day-to-day work and long-term progress. Here are some important points that will get everyone aligned.

#1 Adjust Expectations

There’s a lot of hype out there surrounding what AI can do. And while AI can have a massive positive impact on productivity, it won’t happen instantly. Many think that as soon as AI agents are turned on, they will start building all your campaigns the very next day.

The reality is, every new technology requires process redesign and learning. In fact, productivity will likely decrease during this adjustment period before it increases.

Why is that? When you implement AI agents, several things start happening:

  • Your team needs to learn new workflows
  • You’ll discover edge cases and limitations
  • Processes need adjustment (for example, humans still need to update smart campaigns in Marketo)
  • There’s a learning curve in knowing when to use AI vs. doing it manually (very important)

This is all normal. Give your team a week or two to adapt, and set expectations with your managers and stakeholders about this adjustment period.

#2 Understand the Impact by Seniority

It’s also worth noting that AI productivity gains are not evenly distributed. Due to the nature of the tasks that AI is currently most adept at, the impact will vary by role. We can break this down into a few buckets based on seniority.

  • Junior Employees: Experience the highest productivity increase. AI agents help with basic tasks they’re still learning, allowing them to work faster and with more confidence.
  • Senior Employees: Experience lower (but still positive) productivity increase. They already know how to do things efficiently, so AI is more of an enhancement than a total transformation.

#3 Choose Your Collaboration Model

Now, let’s quickly go through the two main models we can use to define our human-AI collaboration system. These will help us structure our processes and specify where productivity gains can actually happen. We first heard about these models from author and Wharton School professor, Ethan Mollick, when he wrote about the models (and a concept called “The Jagged Frontier” which we’ll touch on shortly) in an article here.

The Centaur Model

This is named after the mythical creature that’s half human and half horse. The Centaur model defines clear boundaries between:

  • What you do
  • What AI does

And we never cross into each other’s territory.

For example: AI clones the program and updates tokens → Human reviews, updates smart campaigns, and does QA. There are clear handoff points.

This model is best for structured processes with clear steps that can be divided between human and AI capabilities.

The Cyborg Model

Conversely, like a cyborg that seamlessly blends human and machine, this model has no clear boundaries. You’re constantly experimenting with things like:

  • What can be handed off to AI?
  • Should I write this specific code or ask AI to write it?
  • Now that AI wrote it, should I refine it?

For example: You ask AI to write data analysis code, but you review and rewrite the statistics portions because you know AI can mess up statistical calculations.

This model is best for creative work, complex analysis, and scenarios where you’re still discovering the capabilities of your AI agents.

These models are more of a guide than a strict process. Most will gravitate more towards one or the other, depending on how they’re using AI. There’s no right answer. Try each for different scenarios and see what works best for you.

Two Critical Traps to Avoid

With all of that in mind, we want to leave you with two significant pitfalls to watch out for as your team adopts AI agents.

Trap 1: The Jagged Frontier

As we know by now, AI intelligence works completely differently from human intelligence. It can do certain complex tasks with incredibly impressive speed, but then fail something as simple as counting the “R’s” in “Strawberry”.

This inconsistency in AI capabilities is known as the “Jagged Frontier”, which we first heard from Ethan Mollick.

Think of AI capabilities as uneven, inconsistent, and “jagged”, where it excels at complex tasks but struggles with some simple ones.

Why does this matter? We have to challenge our assumptions about what AI actually can or can’t do. We may think that a simple task is easily achievable by AI, but then it struggles. And when we try to use our agents for tasks they can’t handle, productivity will inevitably tank. You could run into hallucinations, errors, broken campaigns, and so on.

To overcome this, we need to:

  • Continuously map the jagged frontier: Keep testing what AI can and can’t do
  • Don’t assume: Task difficulty for humans ≠ task difficulty for AI
  • Share learnings: When you discover a capability or limitation, document it for your team
  • Fail safely: Test AI outputs in non-critical scenarios first

You can read Ethan’s blog on this here, as well as the full study here. 

Trap 2: Automation Bias

This second trap is perhaps the more dangerous one. And we have a fascinating real-world example that illustrates how it works (known as the Paris subway story).

In short, Paris introduced semi-automated subway lines that were partially controlled by AI and partially by humans. The results were:

  • Fully automated lines: Very few accidents
  • Human-operated lines: Very few accidents
  • Semi-automated lines: Significantly MORE accidents

Why were the semi-automated lines seeing more accidents? Because when humans see a machine doing something right 99% of the time, they stop paying attention. They assume it will be right 100% of the time. So when that 1% error occurs, humans aren’t ready to catch it.

In other words, we’re generally pretty bad at being “the human in the loop”.

So in MOPs terms, if your AI Agent creates a campaign correctly 99 times, you’ll likely stop carefully reviewing the 100th+ one. But this is exactly where the error could slip through (maybe there’s a wrong token, broken link, incorrect audience, etc.).

We have to make sure that automation bias doesn’t make us complacent. We can’t trust machines too much.

To stay on top of this, follow systematic quality controls rather than solely relying on human review.

This could mean cross-checking with other LLMs (Have a second AI model review the first one’s work for obvious errors), as well as other systems like the ones listed below.

Automated quality measurements:

  • Track campaign completion rates
  • Monitor form submission rates
  • Check email deliverability scores
  • Alert on suspicious patterns

Statistical monitoring:

  • Compare performance to historical averages
  • Flag outliers automatically
  • Investigate when metrics drop below thresholds

The goal is to create AI safety nets that don’t rely solely on vigilant humans trying to catch every mistake.

When your systems are prepped, your documentation is ready, and your team’s expectations are calibrated, AI agents stop being a novelty and start becoming genuine productivity multipliers.

A little upfront investment in preparation will go a very long way in helping you move beyond the hype and get real benefits from these tools.

If you’re ready to make the leap but want expert guidance along the way, reach out to Revenue Pulse here! We’d love to help you get there.

How to Build Your Own Marketing Ops Assistant with MCP

Posted on by Eric Keller

AI agents are transforming Marketing Ops.

And as 2026 quickly approaches, those who not only use agents but also understand how they work and how to build them will have a massive head start.

It’s a very exciting time right now, where tools like MCP (Modern Context Protocol) now give Marketing Ops professionals the ability to create agents that work in their unique system and create real impact.

If you have no idea what MCP is, don’t worry. That’s exactly why we’re writing this. Because for MOPs folks, a good understanding of MCP can be a superpower.

A few weeks ago, RP’s own Lucas Gonçalves (VP of AI & Automation) had a great session at MOps-Apalooza 2025 centered around this exact topic of creating your own MOPs assistant using MCP. If you couldn’t attend in person or watch the session online, we’ve got you covered.

We’ve translated Lucas’s entire presentation into a streamlined guide that you can save and reference in the future.

Here’s what you’ll learn today:

  • What do we even mean by “AI Agent”?
  • What is MCP? Why should marketers care?
  • 3 approaches to building your first MCP
  • A step-by-step walkthrough of building MCP in n8n
  • 2 Marketo use cases with real implementations

Let’s get into it.

What Do We Mean by
“AI Agent”?

Before we dive into MCP, let’s gain a better understanding of what AI Agents are. The term gets thrown around quite a bit these days with wildly different meanings. Below, we’ll take a look at some common definitions and depth “levels” of AI Agent advancement. Then, we’ll highlight the definition that best suits our interpretation today.

  • Level 1: Point Solutions (The Google Cloud Definition) According to Google Cloud, “AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users.” By this definition, almost anything qualifies as an AI agent. A custom GPT that answers specific questions, or even a fine-tuned model that classifies your database into personas based on job titles.
  • Level 2: Agentic Workflows (The Salesforce AgentForce Definition) This level involves structured processes where AI is embedded into workflows. Here, the AI follows certain steps and makes decisions along the way, but it’s still following a predetermined process. Think of this as a workflow that diagnoses the level of AI maturity in a company: the AI routes to option “A” or “B”, but doesn’t define the entire path itself.
  • Level 3: Autonomous Agents (The AWS Definition) AWS defines an agent as “a software program that can interact with its environment, collect data, and use that data to perform tasks.” This is where the AI itself will define the next steps rather than following a predetermined process. An autonomous Marketing Operations agent can perform tasks in your HubSpot, Marketo, or Salesforce according to a goal without you scripting every single action.

When we refer to “AI Agents” in this guide, assume we are focusing on “Level 3: Autonomous Agents” because this is where MCP really shines (and autonomous agents are also just cooler).

What is MCP? And Why Does it Matter for Marketers?

Put simply, MCP (Model Context Protocol) is a universal HTTP protocol that defines how AI models communicate with tools.

To understand why MCP is such a significant breakthrough, let’s use an analogy that folks in Marketing Ops will resonate with.

In MOPs, we are constantly juggling several platforms. And every platform has its own API. Nothing speaks the same language, and we often spend a ton of time just making systems communicate with each other.

This exact integration problem exists in the AI space too.

Without MCP, every major AI Model (be it ChatGPT, Claude, Gemini, etc.) needs to have custom integrations to work with other tools. And if you want to switch AI providers or if one of your tools updates its API, you need to rebuild all the integrations again.

But now with MCP, you only have to build the integration once, then any AI model can use it. Think of MCP kind of like a USB port: it’s one standard with broad compatibility. If you want to switch from ChatGPT to Claude, for example, your integrations through MCP will keep working. This is particularly important, seeing as the major AI models are constantly trading blows, outdoing each other every few months with new capabilities and improvements constantly being released.

(If only there were a similar magic protocol that MOPs folks could use that allowed Marketo, Salesforce, and every other platform to talk to each other despite updates and tool changes)

So, if MCP is not specifically a Marketing Ops technology, why should marketers care?

The reality is, AI Agents aren’t going anywhere. New ones are constantly being created, and existing ones are improving rapidly. Almost every major player in the MarTech space has committed to adding agents to their platforms. So if you, as a marketer, can learn a universal AI language like MCP, you can use it to build your own agents within your unique systems. It gives you far more agency (pun intended) and opens up a world of possibilities that amplify what your team is capable of.

3 Approaches to Building Your First MCP

Depending on your technical comfort level, there are three main paths to building your first MCP, which we’ll briefly outline below. Note: Each option is linked to a corresponding tutorial page.

Option 1: Claude Desktop (Best for Beginners)

If you’re just starting out and want to understand the basics, Claude Desktop offers an excellent introduction. Anthropic provides a step-by-step guide on creating a local MCP server that lets Claude interact with files on your system. It’s essentially copy-and-paste code that helps you see how an AI model starts interacting with your local environment.

  • Pros: It’s Easy to follow and great for learning.
  • Cons: It has basic functionality and it’s local only.

Option 2: FastMCP with Python (For Developers) 

If you’re comfortable with Python, FastMCP is a library based on FastAPI that allows you to create servers you can host on AWS, Azure, or GCP. This approach gives you unlimited flexibility. You can make API requests, perform complex calculations, and implement any logic you can code.

  • Pros: This has maximum flexibility, it’s production-ready, and can be hosted for team access.
  • Cons: It requires Python knowledge and server management.

Option 3: n8n (The Sweet Spot for Most MOPs Pros) 

n8n is an iPaaS (integration platform as a service) solution that offers a low-code/no-code approach to building MCPs. It’s like Zapier or Make.com, but with native MCP support. This is the approach we’ll focus on because it’s powerful enough for real-world use cases but doesn’t demand deep coding knowledge.

  • Pros: It’s a visual workflow builder with no heavy coding required and quick to deploy.
  • Cons: It comes with platform costs and it’s less flexible than custom code.

Let’s dive deep into this 3rd option below!

Building Your MCP in n8n: Step-by-Step

Now for the good stuff. Here is a step-by-step guide on creating an MCP in n8n.

Step 1: Set Up Your MCP Server Trigger

  1. Create a new workflow in n8n.
  2. Find the MCP trigger (it’s not in the obvious place: go to “Other Ways” and click “MCP Server Trigger”).
  3. Customize your path (this is your endpoint URL, just like any API endpoint).
  4. Add authentication if needed to secure your MCP.

Step 2: Add Your Tools

Tools are the actions your AI agent can perform. In n8n, you can add several types:

  • Code Tools: Write custom logic, like calculating averages or performing data transformations.
  • n8n Workflows: For complex, multi-step processes (like importing leads: create job → poll job → queue job → check status until complete).
  • HTTP Request Tools: Basic API calls to any endpoint.
  • Pre-built n8n Nodes: For platforms with native support (like HubSpot or Salesforce), these are plug-and-play. *Note on Marketo: Unfortunately, n8n doesn’t have pre-built Marketo nodes, so you’ll need to use HTTP requests and build the API calls yourself.

Step 3: Connect Your Agent

Now you need to wire up the AI agent that will use these tools:

  1. Define the Trigger: Add a chat message trigger or your preferred input method.
  2. Connect Your Agent: Choose your LLM (OpenAI, Claude, etc.).
  3. Add the MCP Client: This connects your agent to the tools you defined.
  4. Optional but Recommended: Add memory to give your agent context across actions.

Step 4: Add System Instructions

This is where you teach your agent how to behave. If you’re in Marketo, you’ll need to include:

  • Authentication details (Client ID, Client Secret, Munchkin ID)
  • System messages explaining how to perform tasks
  • Rules and guidelines for decision-making

Pro Tip: In production, send authentication data separately rather than including it in the agent instructions. But right now, for learning purposes, including it directly is fine.

What makes this architecture so powerful is that you can have multiple AI agents accessing the same MCP. You don’t rebuild this tool for each agent. The tools live within the MCP, and different agents access those tools as needed. This separation is what makes MCPs scalable!

Now, let’s jump into some real-world examples that show MCP and AI Agents in action.

Use Case 1: Automated Lead Merging in Marketo

In this example, we’ll be building an AI agent that can merge duplicate leads in Marketo. The process Marketers go through to merge leads requires several steps:

  1. Finding the leads (you usually know the email, not the Lead ID)
  2. Determining merge rules (which lead wins?)
  3. Executing the merge


Building the MCP

For this use case, we need just three tools:

  • Tool 1: Get Access Token
    • Before we can do anything, we need to authenticate with Marketo’s REST API.
      • Node Type: HTTP Request
      • Purpose: Obtain the bearer token for subsequent API calls
  • Tool 2: Find Lead by Email
    • Since marketers work with emails, not Lead IDs, we need a way to look up leads.
      • Node Type: HTTP Request
      • Purpose: Query Marketo’s API to find leads matching an email address
      • Description: “Find leads in Marketo database by email address. Returns lead IDs and associated data.”
  • Tool 3: Merge Leads
    • The actual merge operation once we have the Lead IDs.
      • Node Type: HTTP Request
      • Purpose: Execute the merge operation via Marketo’s merge leads endpoint
      • Description: “Merge two Marketo leads using their lead IDs. Preserves data according to specified rules.”

*Notice how each tool has a clear, detailed description? This is crucial. These descriptions tell the LLM what each tool does and when to use it. Without good descriptions, the AI will try to call tools without understanding their purpose, leading to errors and failed operations.


Building the HTTP Nodes

Creating HTTP request tools in n8n is straightforward. It’s like building an API call in any iPaaS:

  1. Specify the endpoint URL
  2. Set the HTTP method (GET, POST, etc.)
  3. Add headers and parameters
  4. The MCP difference: Add placeholders where the AI will dynamically insert data. For example, instead of hardcoding an email address, you’d use a placeholder like {{email}} that the AI fills in based on the user’s request.

Now here’s where it gets interesting. When you tell the agent to “merge these leads,” you haven’t specified the merge rules. Should it keep the oldest lead? The newest? The one with the most data?

This logic for this goes in the system message. Here’s an example of the rules you might include:

“When merging leads:
– Always prioritize the oldest lead as the winning record
– After merging, verify only one lead remains with the specified email”

You could also specify things like:

  • “Prioritize leads with a specific field value”
  • “Keep the lead with the most complete data”
  • “Preserve the lead that was created via form fill over imported leads”

It’s important to document your merge rules clearly so the AI can follow your organization’s standards.


Watching It Work

With all that set up, here’s what happens when you send the command: “Merge the two leads with the email address [email protected]”

  1. AI Accesses MCP: The agent first calls the MCP to understand what tools are available
  2. Gets Authentication: Calls the “Get Access Token” tool
  3. Finds Leads: Calls “Find Lead by Email” with the provided email address
  4. Performs Merge: Calls “Merge Leads” with the returned Lead IDs, applying the rules from the system message
  5. Confirms Completion: Reports back to you what happened

The entire process takes just seconds (think of all that time you’d be spending manually navigating Marketo’s interface!)

Check Marketo and you’ll find just one lead remaining. All the merge rules you specified in the system message have been followed automatically.

Use Case 2: Cloning Programs and Updating Tokens

Our second example tackles another time-consuming task: creating new Marketo programs from templates. Every MOPs pro knows this pain:

  1. Find the right template program
  2. Clone it to the correct folder
  3. Update all the tokens with new values
  4. Update smart campaigns (which can’t be done via API)
  5. Do final QA

This process can take 15-30 minutes per program. What if an AI agent could handle steps 1-3?

Building the MCP

This use case requires four tools:

  • Tool 1: Get Access Token
    • Same as before: authentication is always step one.
  • Tool 2: Find Folder
    • We need to locate where the new program should be created
      • Node Type: HTTP Request
      • Purpose: Search Marketo’s folder structure to find the destination
      • Description: “Find a Marketo folder by name. Returns folder ID and path information.”
    • Tool 3: Clone Program
      • The core action—duplicating an existing program.
        • Node Type: HTTP Request
        • Purpose: Clone a Marketo program via the REST API
        • Description: “Clone a Marketo program to a specified folder. Creates an exact copy with a new name.”
      • Tool 4: Update Tokens
        • Customize the new program with unique token values.
          • Node Type: HTTP Request
          • Purpose: Update program tokens via the API
          • Description: “Update token values in a Marketo program. Accepts token names and new values.”


      Hardcoding vs. Dynamic Selection

      In this example, the template program ID is hardcoded in the system message. But you could make it more sophisticated by:

      • Adding a “Find Program by Name” tool
      • Creating a “Create Folder” tool if the destination doesn’t exist
      • Adding validation tools to check token formats

      Start simple with 3-4 tools and expand as needed. Even basic MCPs can save enormous amounts of time.

      Now for the system message, once again. Here is what your system message might include:

      “When cloning programs:

      1. The template program ID is [12345]

      2. Always clone to the folder matching the campaign name pattern

      3. Update these standard tokens after cloning:

         – {{my.CampaignName}}

         – {{my.CampaignDate}}

         – {{my.UTMCampaign}}

      4. Follow our naming convention: [Region]-[Date]-[CampaignType]-[Description]

      5. Do not activate any campaigns—human review is required”

      Watching It Work

      Command: “Create a program called MOPS-Apalooza-25 in the Events folder”

      The agent:

      1. Accesses MCP: Understands available tools
      2. Gets Token: Authenticates with Marketo
      3. Finds Folder: Locates the “Events” folder
      4. Clones Program: Creates a copy of the template
      5. Updates Tokens: Populates all tokens with appropriate values
      6. Reports Success: Confirms the program is ready

      Navigate to Marketo and you’ll now find:

      • New program in the correct folder
      • Properly named according to conventions
      • All tokens populated with correct values
      • Ready for a human to update smart campaigns and perform QA

      If you have a well-templatized Marketo instance with everything properly tokenized, you can see the transformative potential here; Reduce program creation from 30 minutes to 30 seconds, eliminate manual errors in updates, and free your team to focus on strategy instead of repetitive tasks.

      By now, you should have a solid foundational understanding of MCP, and how it transforms AI agents from abstract concepts into practical tools that solve real Marketing Ops problems.

      As a marketer, speaking this unique language will be a major asset going forward as AI agents (and other unforeseen applications of AI) become increasingly more prominent and impactful in the MOPs space.

      But having the technical know-how to build an MCP is only half the battle.

      In Part 2 of this article, we’ll tackle the second half: how to prepare your systems and teams to work with AI Agents. How can you audit your tools for AI-readiness? How can you create solid documentation for your team? How can your team transition smoothly into new processes while avoiding common pitfalls?

      We’ll answer all of these questions (and much more) in a new article coming soon titled: “How to Prepare Your Tools and Team for AI Agents”.


      Follow us here to be the first to know when it’s published!

      Some Key Trends from MOps-Apalooza 2025

      Posted on by Eric Keller

      Now that the dust has settled from MOps-Apalooza 2025 in Anaheim last week, we’ve had a few days to reflect on this year’s event. For starters, we had an absolute blast!

      The energy around the future of marketing operations has never been stronger, and we’re thankful to be part of this incredible community.

      MOPZA has a unique, intimate atmosphere. It’s the kind of event where you can be yourself, have honest conversations, and nerd out with fellow practitioners who face the same challenges as you every day.

      So today, we want to give you an overview of some pivotal trends we saw over the entire 3–day event. Here’s what stood out to us!

      AI Is Everywhere. But Strategic Application Is Still Evolving.

      AI was a massive recurring theme at MOps-Apalooza this year (unsurprisingly, given how prolific AI is in general right now). Nearly every session touched on AI in some capacity, covering everything from AI agents to workflow automation tactics.

      But the thing is, while interest in AI is prominent, the industry is still in a critical learning phase.

      For many organizations, there is still a gap between enthusiasm and strategic application. They are still working through how AI implementation can lead to measurable business value. And this is completely natural when it comes to any emerging technology; the learning curve is steep, and the best practices are still being established in real-time.

      With that said, there were some very valuable discussions about where AI truly adds strategic value. One standout session explored building AI-powered prospecting workflows with limited resources, focusing on practical questions like: Where should we automate traditionally? Where does AI make the most impact? How do we scale intelligently within our constraints?

      We’re happy to see that organizations are excited about AI, and it’s great that many are starting to move past “AI for AI’s sake” towards harder questions about ROI, resource allocation, and genuine business transformation.

      The Rise of Model Context Protocol (MCP)

      For those unfamiliar, MCP provides a standardized way to connect AI models with various data sources and tools. It essentially creates a bridge between AI capabilities and your MarTech stack.

      And there was definitely some buzz around MCP at the event this year. RP’s own Lucas Gonçalves (VP of AI & Automation) had a session on “How to build your own MOPs Assistant using MCP,” which had a great turnout!

      With MCP on the rise, and new platforms like OpenAI’s Agent Builder taking advantage of it (we wrote a piece on that here), there is a shift toward more integrated, intelligent tools that can help marketers do their best work ever. The real goal is for AI to be a fundamental layer of connectivity and automation instead of a bolt-on feature. And we think MCP is a big part of enabling that going forward.

      Data Architecture: Moving Toward Data Warehouse-First Strategies

      Another idea that we saw from Darrell Alfonso was the need to rethink data architecture. The traditional approach—where every platform maintains its own copy of customer data—is becoming increasingly untenable.

      The emerging idea is “data warehouse-first”. This means establishing a central repository that activates across channels rather than duplicating data across systems. This approach, exemplified by platforms like Marketing Cloud Account Engagement with Data Cloud, addresses fundamental challenges around data consistency, governance, and activation speed.

      Marketing operations is growing more complex, and data privacy regulations continue to tighten (especially with AI in the mix now). So we really think this architectural shift isn’t just a nice-to-have. It will become essential infrastructure for organizations.

      Professionalizing MOPs: Beyond Tool Certifications

      Another very thought-provoking session this year explored the future of marketing operations certifications.

      While industries like HR and project management have established professional certifications for skillsets, MOPs professionals have relied largely on tool-specific credentials. Marketo Certified Expert, Salesforce Administrator, and so on.

      The discussion centered on creating certifications that validate the practice of marketing operations itself, not just proficiency with individual platforms. This is a major step in the field’s evolution from tactical execution to strategic leadership.

      In line with this conversation, Amanda Song from Coursera traced her six-year journey from MOPs manager to director, which beautifully highlighted how MOPs roles increasingly demand business acumen, financial literacy, and strategic thinking. All skills that extend far beyond technical platform knowledge.

      Looking Ahead

      The trends from MOps-Apalooza 2025 paint a picture of a maturing discipline.

      We’re moving from hype to strategic implementation with AI, from siloed data to integrated architectures. The road ahead requires both technical sophistication and strategic vision. And based on the conversations in Anaheim, the MOPs community is ready for the challenge.

      While this year’s event could be the last official MOps-Apalooza, we’re confident this incredible community will continue to come together in new and exciting ways. The collaborative spirit that made this event special isn’t going anywhere, and we can’t wait to see how teams push boundaries in 2026!

      Why AI Agents Don’t Have to Be Scary

      Posted on by Eric Keller

      Halloween season is here! 

      And while we’ve all been enjoying the costume parties, spooky decorations, and horror movie marathons, we want to take this opportunity to address some lingering fears marketers have about AI Agents.

      While most teams are excited about AI’s potential, there are still a few concerns worth addressing.

      (Don’t worry, these aren’t nightmare-inducing concerns. Once you take a closer look, you’ll see there’s a lot to be hopeful about!)

      Fear #1: “Will AI Agents Replace Us?”

      This has been an ongoing concern for several years now, especially with rapid improvements to AI. And it’s totally understandable. 

      But there’s a compelling line of thought that AI Agents can enable businesses to achieve more, which necessitates larger teams to handle increased workloads. 

      In other words, AI can lead to more headcount, not less.

      We wrote an entire article about this a few months ago, which you can check out here: “Can AI Create More Jobs Than It Replaces?”

      In that piece, we discuss how we automated a client’s campaign operations in a way that reduced launch times so dramatically that demand surged. This required them to triple their team from 2 to 6 people. 

      Just as adding highway lanes attracts more drivers over time rather than reducing traffic, AI can act as an amplifier that creates opportunities for growth rather than simply replacing workers. 

      The key is to remain adaptable and view AI Agents as tools that reshape roles and unlock new possibilities. Agents can enable small teams to do more than they imagined, and help large teams be more efficient than ever before.

      Fear #2: “Yet Another Platform to Learn?”

      Marketing teams already manage a complex tech stack. Marketo, Salesforce, analytics platforms, project management tools, and so on.

      Adding yet another interface to this mix is off-putting for many. You need training, adoption, change management, and before you know it, that shiny new AI tool becomes forgotten.

      It’s a legitimate concern, which is why we designed Otto (an AI teammate for Marketing Ops) in a way that integrates with the tools you already use.

      We also wrote a piece on this titled, “Otto Meets You Where You Work”.

      We detailed how you can chat with Otto in Slack, tag it on Asana tasks, or integrate it with any other ticketing platform you use. There’s no need to learn a new platform or interface.

      And this natural integration reduces friction when it comes to the user experience and ultimately improves adoption. 

      Fear #3 “Will AI Agents Kill My Creativity?”

      It’s easy to understand where this fear comes from. 

      With AI able to do things like write copy, generate images, and plan campaigns, many creative marketing professionals are wondering: What’s left for me?

      If AI is handling more and more tasks, will our work lose its creative problem-solving element?

      But that’s not what AI Agents are about. Agents like Otto don’t take over the creative process; they clear the path for it.

      By handling the repetitive, operational work that eats away at your day, AI Agents give marketers back the space to think, explore, and experiment.

      Less time cloning programs, importing lists, updating email templates, and so on, means more time on strategy and creative problem-solving that will be hugely impactful in the long run.

      While these concerns are definitely valid and worth discussing, AI Agents don’t have to be scary. They can be dependable teammates who help you work faster, think bigger, and accomplish more. 

      As marketers living in this unprecedented era of AI, we will thrive by embracing change and finding new ways to collaborate with technology.

      And if you want to learn more about Otto, the AI teammate for Marketing Ops we’ve designed, go here.

      Happy Halloween! 🎃

      What OpenAI’s Agent Builder Means for Marketers

      Posted on by Eric Keller

      The last 12 months have been huge for AI Agents. 

      Last December, Salesforce showcased “Agentforce 2.0”. Then, back in March 2025 at Summit, Adobe announced new AI agents for its Experience Platform. Shortly after that, HubSpot introduced “Breeze Agents”. And just a few months ago at RP, we launched Otto – your AI teammate for Marketing Ops. 

      The next major leap for AI agents came just weeks ago, on October 6, 2025, when OpenAI unveiled its new Agent Builder at DevDay. 

      Today, we’re going to look at OpenAI’s Agent Builder, how it compares to the way we currently build agents with various iPaaS solutions, and what this all means for Marketers going forward.

      OpenAI’s Agent Builder: A Quick Primer

      At a high level, Agent Builder provides a visual canvas where users can create workflows with drag-and-drop nodes. You can compose logic, set guardrails, access a Connector Registry to see what connectors are active in your workflow, and even use RAG (Retrieval-Augmented Generation) capabilities for leveraging files and knowledge bases. 

      All of that is a great step forward, but it isn’t perfect yet. Agent Builder only supports a limited number of native connectors and relies heavily on MCP (Model Context Protocol), which is a standard for linking AI systems with other apps. 

      As it stands, HubSpot maintains an official MCP server, Salesforce is piloting one, and several third-party options are available for Marketo. But overall, the MCP ecosystem is quite young. While MCP in itself is a good solution, the fact that Agent Builder relies exclusively on such an underdeveloped protocol will pose some challenges.

      With that aside, what makes the Agent Builder particularly interesting – beyond the welcomed visual approach to building workflows – is its adaptability. Agents built with OpenAI’s platform can reason dynamically. Meaning, they can decide which step to take based on context. 

      While this opens up a world of possibilities when it comes to autonomous decision-making, dynamic reasoning isn’t always preferred, which is where the current iPaaS solutions come in.

      Current iPaaS Landscape

      For anyone unfamiliar with the concept, iPaaS stands for Integration Platform as a Service. These platforms act as “universal adapters” for software, connecting different apps together like Salesforce, Marketo, Slack, etc., so they can talk to each other and share data. 

      Today’s iPaaS solutions have matured significantly. Here’s a brief overview of some of the major ones and what they’re typically used for:

      • n8n: Open-source platform supporting AI agents that make autonomous decisions, multi-agent systems, and integration with various LLMs beyond OpenAI

      • Zapier: 8,000+ integrations with AI Agents using natural language, though following predetermined workflows rather than adaptive autonomy

      • Workato: Enterprise-grade with advanced error handling, retry logic, and monitoring capabilities for mission-critical operations

      • Power Automate: Deep Microsoft 365 integration with AI Builder, supporting both RPA and digital process automation

      When to Us What: Agent Builder vs. iPaaS

      The fundamental difference between Agent Builder and other iPaaS solutions lies in their approach to automation. 

      Agent Builder is a shift towards AI-native automation, where AI is determining the best path forward through fluid, conversational workflows. They’re ideal for tasks like intelligent customer support, content generation with context, exploratory data analysis, and other tasks where flexible reasoning is built in. 

      Some AI-focused iPaaS solutions can handle conversational workflows too, but unlike Agent Builder, they excel at providing granular control over predetermined processes. They are great when you need practical workhorses for structured, repeatable tasks such as syncing records, triggering campaigns, or managing approvals. 

      This is especially important when you want to automate processes that require a specific, enforced order of predetermined tests. 

      For example: 

      When importing a set of leads to Marketo, there’s a very specific order of API calls we must do. First, we have to create the job, then begin the job, then query the job status until it’s completed, then get the finalized import. The API calls must be in this order, and an iPaaS solution like n8n or Zapier allows us to enforce this order while OpenAI’s Agent Builder does not. 

      The Path Forward

      Over time, we think it’s likely that these categories will merge. OpenAI will expand its ecosystem, and iPaaS vendors will deepen their AI features. Marketers will find that the future of automation isn’t about choosing one over the other; it’s about combining autonomous intelligence with good infrastructure to get the best of both.

      The next generation of marketing automation won’t just run scripts. It will understand goals, adapt to given context, and collaborate with teams. And that’s exactly what inspired Otto, our own AI teammate for Marketing Ops.

      We designed Otto to integrate with the apps you already use, carefully assembling according to each client’s existing tech stack and governance model. It feels like working with another teammate in Slack, and it is ultimately designed to help more marketers do their best work.

      If you want to learn more about Otto, go here.

      We’re optimistic about AI Agents and where all this is headed. We can’t wait to see what major breakthroughs come next!

      Otto Meets You Where You Work

      Posted on by Eric Keller

      When marketing teams bring on a new tool, the biggest hurdle often isn’t the technology; it’s adoption.

      Asking marketers to log into yet another platform means learning a new interface and adjusting their workflows. 

      Before you know it, a shiny new AI tool becomes another browser tab that sits forgotten, competing for attention with the dozen other platforms that teams already struggle to manage.

      At Revenue Pulse, we’ve recognized this fundamental adoption barrier. So we designed Otto (your AI teammate for MOPs) to meet teams where they are.

      Otto’s Native Integration

      Instead of requiring teams to learn another interface, Otto adapts to your existing workflows and lives inside the applications you already use daily.

      For example:

      • Marketing Ops Specialists can chat with Otto directly in Slack, just like they would with any other teammate. Otto will respond with progress updates, ask follow-up questions, and provide any additional information as needed. Interactions feel natural and intuitive. 
      • Project Managers can simply tag Otto on an Asana task and ask it to “work on this”. Otto will respond right there in the comments section of the task with progress updates and additional information.

      In addition to Slack and Asana, Otto can run natively in any other ticketing system or communication platform that your team is accustomed to. 

      This natural integration reduces friction when it comes to user experience and ultimately improves adoption. There’s no training on a new interface, no passwords to remember, no bookmarks to save. Teams start working with Otto using the communication patterns they already know.

      Assembling Otto for Your Team

      Behind the scenes, what makes Otto powerful is the way we assemble it to your company’s specific tech stack, use cases, and business goals.

      That assembly process is where our marketing operations expertise comes in. We configure Otto not only to understand Marketo, but also to understand your specific Marketo instance. Your programs, your workflows, your governance, and everything else that makes up your unique digital environment.

      The result is a teammate who’s not only technically skilled but context-aware, so the output aligns with your strategy and processes.

      If you’re ready to see how Otto can be assembled to fit your tech stack, book a free call with us here!

       

      5 Marketing Ops Tasks That AI Can Do for You

      Posted on by Eric Keller

      Marketing Operations specialists are drowning in repetitive tasks.

      Managing lead databases and setting up campaigns takes countless hours of manual work – not to mention the constant stream of requests from stakeholders.

      And while these tasks are critical, they take away from strategic initiatives that can transform their organization’s marketing performance.

      This is where AI Agents are changing the game.

      Unlike traditional automation, AI Agents can understand context, make their own decisions, and execute complex, multi-step tasks that are normally reserved for humans.

      At Revenue Pulse, we’ve built “Otto”: your AI teammate for Marketing Operations.

      Otto seamlessly integrates with your Marketo instance, and lives in the apps you already use; you can chat with it in Slack and even tag it in Asana (or other ticketing systems), just like you would with any other teammate.

      Otto is constantly learning new skills too. And while it would be impossible to go through every single thing Otto can do right now, we’re excited to showcase 5 tasks that most MOPs pros would be more than happy to delegate.

      Let’s jump right in!

      1. Merge duplicates in Marketo

      In Slack, ask Otto to find a specific lead. Otto will search your Marketo instance for that lead and display any duplicates. Tell Otto to merge the records, and it will perform the merge for you automatically. You can then switch to Marketo to confirm there is now a single record for that lead. If needed, you can repeat the same steps for other leads without leaving Slack. 

      2. Create webinar programs in Marketo (from Asana)

      In Asana, simply comment “Otto, work on this” on a webinar creation ticket. Otto will then create a webinar program in Marketo from your template. It’ll place the program in the correct folder, leave Smart Campaigns deactivated, and fill the required tokens. You can see the program start to appear in Marketo as the build completes. Otto then posts a brief summary of what it created, so you know exactly what changed!

      3. Send test emails

      Once your webinar program is created, you can also ask Otto to send a sample of the follow-up email so you can review the exact content and formatting. After you confirm it looks right, tell Otto to activate the Smart Campaigns for attendees and no-shows. Otto turns those campaigns on and lets you know when everything is ready.

      4. Create Salesforce campaigns that sync with Marketo programs

      This time, let’s head back to Slack. When you ask Otto to create a program, it’ll build it in Marketo and update tokens. But it doesn’t end there. Otto will also create a matching Salesforce campaign with the required campaign member statuses. The statuses in Salesforce match the ones in Marketo, so both systems are aligned.

      5. Import lists to Marketo

      Send Otto a lead file (such as a CSV) and tell it which static list to import to. Otto will find the list, run the import, and report back with the results—including how many leads were processed and any failures that occurred. Just refresh your Marketo list to see the leads appear!

      These are just a few examples of the time-consuming and repetitive MOPs tasks that Otto can take off your plate! And we’re developing new features and capabilities for Otto every day. Follow us here to be the first to know when new skills become available.

      If you want to learn more about how Otto can help your team, book a personalized walk-through here.

      Meet Otto: An RP AI Service Solution [Webinar]

      Posted on by Eric Keller

      We’re incredibly excited for you to meet Otto – our new service solution that’s going to change Marketing and Campaign Ops forever.

      Join Joseph Peters, Darrell Alfonso, Lucas Gonçalves, and Andy Caron in the webinar above to see Otto in action!

      Click the button below to book your personalized walk-through of Otto today.

      Meet Otto