Milena Traikovich helps businesses drive effective campaigns for nurturing high-quality leads. As our Demand Gen expert, she brings extensive experience in analytics, performance optimization, and lead generation initiatives. Today, we’re diving into the often-unseen friction that delays digital campaign launches. We’ll explore why the real bottleneck is human coordination, not technology, and how new AI capabilities are poised to act as a “technical translator” between teams. Our discussion will cover the practical steps for making design systems AI-ready, a realistic approach for integrating AI with legacy martech stacks, and how a new metric, “time-to-first-experience,” is set to redefine marketing ROI.
The article claims the primary bottleneck for new web pages is coordination, not technology, as teams are measured on different goals. Could you share a specific anecdote of how this misalignment stalls a launch and explain how an AI “technical translator” could resolve that specific friction?
Absolutely, this happens all the time. I remember a specific launch for a Q1 campaign where the marketing team had a beautifully approved pricing page design sitting in Figma. They were measured on generating pipeline, and this page was critical. But when they handed it to the development team, it stalled for three weeks. The developers weren’t being malicious; their primary metric was site uptime and stability, and they were in the middle of a critical security patch. Design’s goal was quality and brand consistency, and they’d used a few non-standard components that would require custom work. It was a classic stalemate where everyone was doing their job correctly according to their own goals, but the project went nowhere. An AI “technical translator” completely changes this dynamic. Instead of a Figma file being thrown over the wall, the marketer describes the goal in plain English: “Create a pricing page with three tiers and ROI calculators.” The AI, already trained on the company’s approved component library, generates the exact technical configuration. It doesn’t get creative; it translates the marketing intent into the precise language the content and data systems require, bypassing the entire interpretation and negotiation phase that burned three weeks of precious time.
You mention developers will shift from repetitive mapping to building “AI-ready design systems.” Can you describe, step-by-step, what makes a design system AI-ready, and what are the first practical actions a development team should take to begin this transition?
This is a critical shift in mindset for development teams. An “AI-ready” design system isn’t just a collection of visual components; it’s a machine-readable rulebook. The first step is a thorough audit and standardization of every single component, from a simple button to a complex interactive block. You have to lock down every variable. The second step is to parameterize those components explicitly for AI. For a testimonial block, you’d define fields like customer_logo, quote_text, and author_name so an AI agent knows exactly what data to pull and where to put it. The third, and perhaps most important, step is to build governance directly into the system. This means codifying brand and compliance rules—like accessibility standards or pre-approved color variations—as technical constraints that the AI cannot violate. Finally, you need to ensure the entire system is accessible via APIs, so the AI can programmatically query and assemble these components. A great first action for a team is to start small. Don’t try to boil the ocean. Pick one high-value, frequently used component, like a hero banner, and take it through this entire process. Prove that you can get the AI to reliably generate on-brand, production-ready variations of that one piece. This small win builds momentum and demonstrates the value of this new way of working.
The text highlights a significant challenge: legacy systems with limited APIs. For a large enterprise, what is a realistic approach to auditing their current martech stack and building the necessary integrations for AI automation without halting current operations? Please provide some key milestones.
For a large enterprise, you can’t just unplug everything. The approach must be gradual and strategic. The first milestone is simply a comprehensive audit to create a map of your entire martech ecosystem. It’s often a messy discovery process, but you have to be brutally honest about which systems have modern APIs, which have limited or outdated ones, and which are essentially black boxes. This audit helps you triage. The second milestone is ruthless prioritization. You don’t connect everything at once. Identify the single most painful, time-consuming data pathway that stalls your campaigns. Often, it’s the connection between the CMS, the CDP, and the analytics platform. Focus all your initial effort there. For the systems with limited APIs, the third milestone is to build a “wrapper” or a middleware layer. Instead of re-architecting a 10-year-old system, you build a modern API that sits on top of it, translating the AI agent’s modern requests into a language the legacy tool can understand. This avoids disrupting operations while still enabling automation. Finally, once that first connection is built, you run a pilot project on a single, low-risk campaign to prove the end-to-end workflow before scaling it out.
The article introduces “time-to-first-experience” as a critical metric, noting that coordination consumes roughly 60% of this time. How can a marketing leader begin to measure this metric effectively, and can you share an example of the ROI they might see on their DXP investment once it’s reduced?
Measuring it is simpler than it sounds, but it requires discipline. It starts with formally logging two dates for every single campaign: the date the official brief is approved and the date the very first version of the web experience goes live. When you track this, the results are often shocking. What everyone feels is a two-week process is, in reality, taking two or three months. The data exposes that huge chunk of time—often around 60% of the entire timeline—where a project is just sitting idle in a queue, waiting for a technical handoff or a compliance review. The ROI here is transformative, especially for expensive Digital Experience Platforms (DXPs). I saw a company that had a seven-figure investment in a DXP with powerful personalization and A/B testing features. But because it took them a full quarter to launch a single new landing page, they could never gather enough behavioral data to make those features work. The platform was essentially dormant. By introducing AI to automate the launch process and slash that time-to-first-experience, they went from launching one page a quarter to testing ten variations a month. Suddenly, their DXP came to life. The ROI wasn’t just “we’re faster”; it was the complete activation of a massive prior investment that was otherwise failing to deliver any value.
What is your forecast for how these AI agents will reshape the roles and daily workflows of brand, legal, and marketing teams in the next few years?
I foresee a fundamental shift from manual execution and gatekeeping to strategic governance. For marketers, the daily grind of chasing down developers and managing project tickets will evaporate. Their time will be reallocated to what they do best: understanding the customer, defining campaign strategy, and analyzing performance data to form new hypotheses. They’ll feel an incredible sense of agency, moving at the speed of their ideas. For brand and legal teams, their roles will become more powerful and scalable. Instead of being a bottleneck that has to manually review every single asset variation, they will become the architects of the guardrails. A brand manager’s job will be to encode brand guidelines as technical constraints and pre-approve component templates that the AI can use. A legal expert will codify privacy and compliance requirements as unbreakable rules within the system. Their expertise will be embedded into the workflow from the start, not bolted on at the end, making them enablers of speed rather than obstacles. The entire workflow will become less about a sequence of handoffs and more about a collaborative, real-time partnership with an AI agent.
