Your AI is not helping if it makes me do the work

I’m frustrated. I don’t want my martech tool to give me suggestions. I want it to help me execute the work I need to get done in my production environment.

I already have enough to do. Speed to market is table stakes. What I need is operational velocity — the time between deciding something needs to happen and watching it actually happen in production. Why is this so hard for martech vendors to understand? 

They keep selling me tools with AI bolted on that give me the same thing I can get from ChatGPT. I need results, not another dashboard telling me what to do next.

From suggestion theater to real execution

Every vendor demo follows the same script. They show me an AI chat interface. I type in a request. The AI generates a beautifully written campaign brief, segmentation strategy or personalized email copy. Everyone in the room nods with big smiles.

Then comes the part they gloss over. I still have to build the segment manually, deploy the campaign myself, update the CDP by hand and configure the automation workflows one step at a time. The AI didn’t execute anything.

Up to 81% are now piloting or implementing AI agents, per Gartner’s 2025 survey of 413 marketing technology leaders. Yet 45% say these agents fail to deliver on promised business performance.

I’m asking for autonomous execution in production. A system that sees “run a 15% discount test on cart abandoners who viewed product X in the last 7 days” and just does it — builds the segment, creates the journey, deploys the campaign, monitors performance, kills the loser, scales the winner and logs the ROI. Zero human touches after I define the intent. Here’s what that looks like in practice:

• If I want to test three headlines, I define the variations and the success metric, and the system runs the test immediately. Traffic is split, performance is measured, a winner is declared and I’m notified — without dashboards, manual switches or reporting.
• If I need offers to vary by location, I set the rules once. West Coast visitors see free shipping, East Coast visitors get expedited delivery and international visitors see local pricing. The system applies those rules automatically to every visit, without ongoing intervention.
• If I’m launching a campaign and need a landing page, I describe the structure — a video-led hero, a three-column feature comparison, testimonials and a CTA. The system builds the page, connects the right content and shows me the live result, not a wireframe or suggestion.

The system performs the work within the parameters I’ve defined, rather than instructing me on how to do it, while I remain the bottleneck.

Dig deeper: How AI decisioning will change your marketing

Why vendors choose the easy path

Most martech platforms were built years before AI existed. Rather than rebuilding from scratch, vendors rushed to add AI as a feature layer on top of legacy architectures, according to research on bolt-on versus AI-native systems.

These bolt-on solutions inherit yesterday’s assumptions: data silos, rigid schemas, slow batch processing and UI layers never designed for real-time guidance. They create another screen for recommendations, requiring manual implementation of every suggestion.

Adding a chatbot is cheaper, faster and easier to market than rebuilding core infrastructure. And there’s an incentive misalignment: vendors make more money selling platforms that require my team to execute than ones that actually do the job. 

Most martech sales cycles target mid-level managers who need to look productive with decks and recommendations. Actual operators, like me, who care about outcomes, get ignored because we ask uncomfortable questions, such as “show me where it changed a P&L line item without me doing the work.”

The structural barriers no one mentions

Even when I want to demand better, structural barriers make accurate execution difficult. Vendors avoid liability by keeping AI in suggestion mode. Executing work in production carries risk. For example, if an agent breaks a customer journey, sends the wrong message to a million people or violates privacy regulations, the vendor could be liable. Suggestions put the risk back on me. When agents underperform, they blame my team’s governance maturity rather than their product’s inability to execute reliably.

My production environment likely lacks what vendors don’t mention in sales calls — real-time data synchronization across CRM, marketing automation, CDP and analytics. Field-level data hygiene and standardized schemas. Identity resolution that works consistently. API stability and governance frameworks for autonomous actions.

According to the same Gartner research, 50% of martech leaders report their organizations lack the stack readiness required for AI agent deployment. Without these prerequisites, agents hallucinate, make decisions on stale data or require constant manual intervention.

Governance creates another vacuum. Agents require policy, oversight and monitoring as prerequisites, yet most organizations write governance policies only after problems arise. 

• Who has authority when an agent makes a wrong decision? 
• How do you audit autonomous actions? 
• What happens when AI conflicts with compliance requirements?

Half of martech leaders cite a lack of skilled resources as a primary blocker. I’ve effectively paid to become the vendor’s QA department, debugging their agent’s integration failures.

Dig deeper: 6 common agentic AI pitfalls and how to avoid them

How to tell execution from theater

When vendors pitch AI capabilities, ask this simple question: “Can your AI execute this task in production or does it just tell me how to do it?”

If they stumble, push for specifics. Ask for proof of execution authority, error-handling mechanisms and references from customers using AI for actual operational work rather than recommendations.

Look for tools that explicitly advertise AI-executed workflows, safe action layers, production environment interaction and governance frameworks. If a vendor can’t explain rollback procedures, validation pipelines or action gating, it’s not execution.

Ask about API documentation before asking about AI. The critical question is whether the API allows changes in production — updating a live landing page, launching an experiment or adjusting a campaign budget — or only retrieves data. Read-only connections signal insight tools, not execution systems.

Most vendors don’t pass this test today. A few are getting close.

What this means for the industry

Some organizations are already building execution agents through orchestration platforms or treating AI as a backend worker that operates systems programmatically. This is where the industry needs to go — AI that does the work, not AI that offers advice.

With budget constraints and limited capacity, tools that turn AI suggestions into manual tasks don’t solve real problems. They add work while vendors collect subscription revenue and claim to be AI-powered.

The shift from suggestion engines to execution engines will separate winners from also-rans. Vendors that build for orchestration instead of consultation — measuring completion, supporting rollback and auditability and granting operational authority within defined boundaries — will earn real investment.

Operators are done paying for tools that create more work instead of eliminating it. Execution, not recommendations, is what makes AI in martech matter.

Dig deeper: How to overcome AI challenges in martech to maximize ROI

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