Make Your Martech Stack AI-Ready

CMOs are racing to layer AI onto legacy martech stacks to improve efficiency and productivity, but that speed can backfire. If the underlying martech stack is fragmented and unoptimized for AI, a negative outcome becomes highly likely. In fact, this is the most common scenario. 78 percent of 366 marketing leaders recently surveyed said their organization’s martech stack doesn’t support business goals, “despite years of significant investment.”

It’s understandable that marketing leaders feel pressure to move fast and show ROI on the company’s AI investments. But unless they take time to unify data and streamline their processes, AI can end up delivering incorrect or misaligned outputs faster than humans in the loop can fix them. To avoid this scenario and build real value over the long term with AI, CMOs and digital leaders need to work on their martech stack and data resources first.

Martech Fragmentation Was Already a Problem

The Marketing Data Report also revealed the existing problems already impeding martech activation. The main issue is disconnected data and systems. Leaders cited data quality and consistency issues as the top obstacle to data unification. The second largest obstacle is the lack of communication between sales and marketing systems. Fragmentation is a longstanding issue across many industries, and the time to resolve it is now, before AI magnifies and exaggerates data and communication gaps.

Processes can also impede efficiency. 37 percent of marketing leaders said rigid planning cycles and slow approval workflows hinder their team’s responsiveness. A lack of real-time visibility into performance data also affects responsiveness as well as customer experience (CX). Only 19 percent of leaders said their organizations use real-time insights for CX personalization, with the top personalization challenges being data volume and quality management and a lack of team and platform integration. AI can help organizations gather and analyze real-time data, but the data needs to be standardized and not siloed for AI to work properly.

The fragmentation and CX findings align with McKinsey data showing that although almost 90 percent of CMOs are working with AI, fewer than 10 percent say AI has already created value in their end-to-end workflows. McKinsey forecasts that agentic AI has the potential to eventually run 60 percent of core marketing workflow tasks. That means organizations that don’t realize AI ROI face long-term competitiveness and cost challenges.

Why Can’t AI Just Fix the Stack?

Agentic AI is an impressive, powerful technology, but like any technology, it needs the right inputs and processes to deliver the best results. When agents can only access inconsistent, incomplete, or out-of-date information, they’ll deliver results that aren’t accurate or timely. Worse, agents accelerate the pace of production. That can force human team members to do more review and remediation work, faster, to keep operations and CX on track.

Although the most logical strategy for building long-term ROI with AI would be to start with data and stack optimization, leaders have been under a lot of pressure to adopt AI quickly for fear of being left behind. 64 percent of CEOs surveyed by IBM in 2025 said that fear of missing out “drives investment in some technologies before they have a clear understanding of the value they bring to the organization.”

Some stack optimization needs are universal, like data and platform unification across teams. For example, teams often spend time manually correlating and sharing data across CRMs, DAMs, and data dashboards that were designed before the advent of AI. This slows workflows and creates bottlenecks AI can make worse.

Unification isn't the only requirement. The Marketing Data Report also identified industry-specific challenges reported by marketing leaders, including:

• Approvals that happen too slowly to keep up with the market in banking and financial services.
• Incomplete and disconnected personalization in retail and consumer goods.
• Siloed and parallel marketing and product data in tech and software.
• Campaign data that arrives too late to be useful in manufacturing.
• Conflicts between market trend data and brand integrity in fashion and luxury.
• Reactive churn prediction in telecommunications and media.

Sixty-eight percent of leaders said they know their data is siloed, and just 25 percent said they use real-time unified data and cross-channel insights to make decisions. Leaders understand the gap and the stakes for not closing it. Deciding how to close it is the next step.

Data Unification, System Integration, and Process Redesign

Standardizing and unifying data across platforms and teams is the foundation for AI ROI, so it’s where most organizations will need to start. Stopping after that’s done will only deliver partial results, though. That’s because workflows designed for legacy systems don’t make the best use of what agentic AI can do.

For example, if your AI agent can use brand and segment data to draft campaign briefs, you’ll save time. But if those AI-generated briefs then languish inside your existing review process, they’re not as useful as if they went into a workflow designed to accelerate review, approval, and launch. Similarly, if your agents collect and analyze real-time data but it doesn’t get used, that’s missed ROI.

The leaders who restructure their processes most effectively for AI will bake those insights into the step in the workflow where they can inform decisions, like product search data at the personalization stage and channel traffic data at the spend allocation stage. Staging AI-generated insights this way also helps teams act faster on their data, rather than letting it sit and get stale first.

When teams can make data-driven decisions at speed, the reporting process needs to change as well. Calendar-based manual reporting is too slow and backward-looking to measure AI-assisted workflow performance. Agents can measure and report results in real time, so the agentic system and the human team learn as they go and pivot as quickly as they need to for optimal results.

To achieve this outcome, leaders need to collaborate with IT and other teams to rebuild their workflows with agentic orchestration at the core, after the data and systems are unified. The industry-specific workflow challenges mentioned earlier offer a guide to planning for redesign.

These steps take time, but they’re the clear path to AI ROI over the long term, and the longer an organization waits, the more challenging it will be to catch up with competitors later. CMOs who create the most value with AI will be the ones that sequence the foundation upgrade correctly and follow through so AI can work the way leaders expect it to.

Scott Houchin is the chief marketing officer for eClerx. His responsibilities include marketing strategy, industry and service marketing, and lead generation for the organization. During his 16-plus years at eClerx, Houchin has grown the digital business across multiple industry sectors and global sales expansion in Asia and Europe. In 2015, he spearheaded eClerx’s acquisition of CLX Europe, an Italian creative agency, and continues to serve as CEO of the subsidiary. With over 25+ years of experience, Scott has previously held client side marketing leadership positions at Yahoo, Dell, and Gateway before joining eClerx.