I already have enough to do. Speed to market is the stakes. What I need is operational speed: the time between deciding something needs to be done and seeing it happen in production. Why is this so difficult for martech vendors to understand?
They keep selling me tools with AI plugged in that give me the same thing I can get through ChatGPT. I need results, not another dashboard telling me what to do next.
From suggestion theater to real performance
Each 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 briefing, segmentation strategy or personalized email copy. Everyone in the room nods with a big smile.
Then comes the part they gloss over. I still have to manually build the segment, deploy the campaign myself, manually update the CDP and configure the automation workflows step by step. The AI did nothing.
Up to 81% are now testing or deploying AI agents Gartner’s 2025 survey of 413 marketing technology leaders. Yet 45% say these agents are failing to deliver on promised business performance.
I ask for autonomous implementation in production. A system that “runs a 15% discount test on cart abandoners who viewed product No human touches after I define the intention. This is what it looks like in practice:
- If I want to test three headlines, I define the variants 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 that vary by location, I set the rules once. Visitors to the West Coast will see free shipping, visitors to the East Coast will see expedited delivery, and international visitors will see local prices. The system automatically applies these rules to each visit, without constant intervention.
- When I launch 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, links the correct content and shows me the live result, not a wireframe or suggestion.
The system performs the work within the parameters I defined, rather than instructing me how to do it while I remain the bottleneck.
Dig Deeper: How AI Decisions Will Change Your Marketing
Why suppliers take the easy way out
Most martech platforms were built years before the existence of AI. Instead of rebuilding from scratch, vendors rushed to add AI as a feature layer on top of existing architectures, according to research on complementary versus AI-native systems.
These off-the-shelf solutions inherit yesterday’s assumptions: data silos, rigid schedules, slow batch processing, and UI layers that were never designed for real-time guidance. They create a new screen for recommendations, which requires each suggestion to be implemented manually.
Adding a chatbot is cheaper, faster and easier to market than rebuilding the core infrastructure. And there’s a misalignment of incentives: Vendors make more money selling platforms that my team needs than platforms that actually do the work.
Most martech sales cycles are focused on mid-level managers who need to look productive with decks and recommendations. Actual operators, like me, who care about results are ignored because we ask uncomfortable questions like “show me where a P&L line item was changed without me doing the work.”
The structural barriers that no one mentions
Even if I want to set better standards, structural barriers make accurate execution difficult. Suppliers avoid liability by keeping AI in suggestion mode. Carrying out work in production involves risks. For example, if an agent breaks the customer journey, sends the wrong message to a million people, or violates privacy rules, the supplier could be liable. Suggestions put the risk back on me. When agents underperform, they blame the maturity of my team’s governance, not the inability of their product to execute reliably.
My production environment is probably missing what vendors don’t mention in sales calls: real-time data synchronization via CRM, marketing automation, CDP, and analytics. Field-level data hygiene and standardized schedules. Identity resolution that works consistently. API stability and governance frameworks for autonomous actions.
According to the same Gartner study, 50% of martech leaders report that their organizations lack the stack readiness needed to deploy AI agents. Without these requirements, agents hallucinate, make decisions based on outdated data, or require constant manual intervention.
Governance creates a new vacuum. Agents need policy, oversight and monitoring as a prerequisite, but most organizations write their governance policies only after problems arise.
- Who has the authority when an officer makes a wrong decision?
- How do you control autonomous actions?
- What happens if AI conflicts with compliance requirements?
Half of martech leaders cite a lack of skilled resources as the main blocker. I effectively paid to become the vendor’s QA department, fixing their agent’s integration errors.
Dig Deeper: 6 Common Pitfalls for Agentic AI and How to Avoid Them
How to distinguish the performance from the theater
When vendors pitch AI capabilities, ask this simple question: “Can your AI perform this task in production or does it just tell me how to do it?”
If they stumble, ask for details. Ask for proof of executive authority, error handling mechanisms, and references from customers using AI for actual operational work rather than recommendations.
Look for tools that explicitly promote AI-powered workflows, secure action layers, interaction with the production environment, and governance frameworks. If a vendor cannot explain rollback procedures, validation pipelines, or action gating, there is no execution.
Before asking about AI, ask for API documentation. 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 just retrieves data. Read-only connections indicate insight tools, not execution systems.
Most suppliers today fail this test. A few come close.
What this means for the sector
Some organizations are already building execution agents through orchestration platforms or treating AI as a backend worker that controls systems programmatically. This is where the industry needs to go: AI that does the work, not AI that gives advice.
With budget constraints and limited capacity, tools that turn AI suggestions into manual tasks don’t solve real problems. They add work as vendors collect subscription revenue and claim to be AI-powered.
The shift from suggestion engines to execution engines will separate the winners from the also-runs. Suppliers who build for orchestration rather than 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 AI in martech matters.
I’m frustrated. I don’t want any suggestions. I want execution. And I’m not the only one.
Dig deeper: How to overcome AI challenges in martech to maximize ROI
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Contributing authors are invited to create content for MarTech and are chosen for their expertise and contribution to the martech community. Our contributors work under the supervision of the editors and contributions are checked for quality and relevance to our readers. MarTech is owned by Semrush. The contributor was not asked to make any direct or indirect mentions of it Semrush. The opinions they express are their own.
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