61% of CMOs Call Local Marketing Too Complex – Here’s the Fix

Nearly two-thirds of senior marketing leaders at multi-location brands admit their local marketing efforts are too complex. That’s according to a survey by Uberall, which found that 61% of CMOs and VPs describe managing local visibility, listings, reviews, and content across dozens or hundreds of locations as “complex” or “very complex.” The root cause is a fragmented tech stack , layers of disjointed AI tools and marketing platforms that create an unclean infrastructure, making it nearly impossible to track overall ROI.

The solution, according to Uberall, is not more tools. It’s an AI orchestration layer that brings order to the chaos. An overwhelming 99% of senior marketers say such a layer would be valuable. The goal is to move from exploratory AI experiments to ROI-driven operations that restore search visibility, streamline local marketing, and deliver attributable revenue.

Step 1: Appoint Your Chief Marketing Orchestrator

Value doesn’t come from plugging data into a large language model. 89% of leaders say their tech investments haven’t fully delivered, with integration complexity as the top reason. Instead, value comes from plugging all multi-location marketing data into an orchestration layer that performs context engineering , structuring every location’s data and signals for any search system customers use.

This requires a new leadership role: the Chief Marketing Orchestrator (CMO) . This person decides which tasks need human sign-off, who owns AI discoverability at brand and location levels, and where to relieve teams from operational workload so they can focus on revenue-driving activities. It’s not just a technology story; it’s a leadership story.

At a time when every marketer is urged to own AI, often no one owns the outcome. A streamlined stack with an AI orchestration layer changes that: the platform owns execution and analysis, the CMO owns strategy, and the team owns human approvals and guardrails. This is the principle behind Uberall’s agentic AI, UB-I: the marketer remains in control, governing the AI’s output.

Consider the daily baseline for a brand with 50 locations: open each profile across Google, Apple, Bing, and directories; check for formatting inconsistencies; draft review responses matching brand guidelines; audit for missing descriptions and generate local keyword copy. At scale, that workload is unsustainable. UB-I handles it before the team logs in, drafting AI-generated replies, correcting formatting, and generating missing attributes , all while flagging anything requiring human judgment.

As innovation strategist Shawn Kanungo puts it: “The companies I am watching win are not the ones optimizing the ROI of existing workflows. They are the ones using agents to do things that were previously impossible at any price.” That’s the true value of an orchestration layer: enabling what was impossible for marketers to achieve in an eight-hour workday.

Step 2: Pivot From Finding New AI To Restoring Search Visibility

The prize for multi-location brands is restoring declining traffic amid zero-click searches. Adobe reports a 254% increase in revenue per visit for the retail segment from AI-driven discovery. Stakeholders are more interested in SEO and GEO performance than ever.

Think of a multi-location brand as a building with 200 rooms, each hosting a party. There’s a new entrance , a shortcut for guests , but all entrances are still in use. You want to maximize access through every one. You don’t hire someone to manually bring guests to each entrance; you invest in technology to put up signals that do the work, so your team can focus on the experience inside. That’s context engineering.

Implement The 4 Pillars Of Location Performance Optimization (LPO)

If visibility on any channel improves, every other pillar improves: engagement, reputation, and conversion. These are the four pillars of LPO, a revenue-first framework:

• Visibility: Every location is accurately represented across all discovery surfaces (website, Google, Apple, Yelp, Bing, directories).An AI agent that implements these measures to attract customers and influence revenue isn’t exploration. It’s a hard-ROI workflow that pays for the program. When the board asks about AI ROI, this new CMO doesn’t just demonstrate adoption; they justify investment to continue funding operations.How To Shift From AI Experiments To ROI-Driven OperationsEY described the moment well: moving from vibe to value. The “vibe” phase was every company exploring AI , experimenting, piloting, racking up compute costs, layering up tech stacks , and either still being in that phase or concluding it with frustration. Marketing leaders at multi-location brands must adopt and govern agentic-AI-powered stacks that are less exploratory and more ROI-driven. These are stacks that enable teams to log in to approve fixes, not to discover what’s broken. That’s the orchestration layer senior marketers are looking for.