This post was sponsored by Uberall. The opinions expressed in this article are the sponsor\u2019s own.<\/em><\/p> Who should own AI search visibility across all our locations?<\/p> Should I add more AI tools to manage local listings and reviews, or is that making it worse?<\/p> When 99% of senior marketers say they want an AI orchestration layer, the question is who leads it.<\/p> The ideal multi-location marketing world is one where agentic AI fixes duplicate listings, responds to customer reviews, analyzes sentiment, and spots optimization opportunities before the marketer can say \u201cGBP.\u201d<\/p> However, what multi-location brand CMOs actually have, in today\u2019s far less ideal world, is layers of disjointed AI and marketing tooling creating an unclean and unclear infrastructure.<\/p> This lack of infrastructure makes it nearly impossible to track overall ROI.<\/p> An Uberall survey last year revealed that only around 1 in 4 location marketers can show the impact of their location marketing on sales<\/strong>; I\u2019ll bet that with varying levels of AI tool adoption since that survey, this issue hasn\u2019t improved \u2014 if anything, it\u2019s been exacerbated by it.<\/p> The AI understands what needs prioritizing and resolves it in the background while teams focus on their marketing for multiple locations. It squashes impatience or uncertainty surrounding ROI reporting because its model is built on delivering and visualizing real-time attributable location performance: bookings, table reservations, foot traffic. The clean and clear data that stakeholders wait for.<\/p> The results of ill-equipped and layered martech tooling are bleak for local visibility:<\/p> Today\u2019s real ideal world is about bringing some sense back to the location marketing stack. It will deliver a combination of that sought-after AI orchestration layer, omnichannel search visibility across locations, and the even more sought-after ROI numbers. It\u2019s the Chief Marketing Orchestrator who will lead it.<\/p> Value won\u2019t come from simply plugging data into an LLM. 89% of leaders said their tech investments haven\u2019t fully delivered, with integration complexity the top reason.<\/p> Instead, it comes from plugging all your multi-location marketing data into an orchestration layer that implements the nonnegotiable context engineering tasks, making sure every location\u2019s data and signals are structured for any search system customers are using to discover local businesses.<\/p> Someone needs to do this, and that person becomes your Chief Marketing Orchestrator (CMO). And, luckily, it\u2019s a new evolution of a Chief Marketing Officer.<\/p> The Chief Marketing Orchestrator (CMO) must decide which tasks require human sign-off. Where are the trade-offs? Who owns AI discoverability at a brand and location level? Where can they relieve their team from operational workload and reallocate them to tasks that influence revenue \u2014 turning sentiment analysis into actionable reports for operations, or producing content that drives local engagement? It\u2019s not just a technology story but also a leadership story.<\/p> Any CMO who is truly passionate about what they do for their multi-location brand doesn\u2019t want to blindly outsource every single task to an AI agent. They want to trust the performance numbers and location marketing initiatives they\u2019re reporting back to stakeholders. And they most likely want to feel in control of compute costs.<\/p> At a time when every marketer and every leader is urged to own AI, this often means no one owns the outcome. A streamlined stack with an AI orchestration layer changes that, in that the platform owns the execution and analysis, the CMO owns the overarching strategy, and their team owns the human approvals and guardrails.<\/p> This is the principle Uberall\u2019s agentic AI, UB-I, is built on: The marketer remains in control \u2014 governing the AI\u2019s output, not just guiding or prompting it.<\/p> A CMO investing in the right people to govern agentic AI is a CMO focused on output, not adoption.<\/p> Try doing this manually across 50 locations:<\/p> That\u2019s the daily baseline. At scale, it\u2019s unsustainable \u2014 which is exactly the workload UB-I handles before the team logs in.<\/p> UB-I handles the volume and velocity of local operations that no human team can sustainably match at scale, while flagging anything that requires human judgment before acting. On any given day, that means:<\/p> The team logs in to approve, not to discover what\u2019s broken. Each of these is context engineering in practice \u2014 making location data usable for both human and AI-powered search, at a scale no team can manage manually.<\/p> As globally recognized innovation strategist Shawn Kanungo puts it: \u201cThe 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.<\/em>\u201d The efficient orchestration of local marketing tasks across multiple locations has always been impossible at scale \u2014 and this orchestration layer is exactly what 99% of senior marketers say would be \u201cvaluable\u201d or \u201cvery valuable,\u201d according to an Uberall survey.<\/p> The true value here in implementing an AI orchestration layer to manage omnichannel presence isn\u2019t to optimize the efficiency of existing local marketing workflows \u2014 it\u2019s in enabling what was impossible for marketers to achieve at scale in an eight-hour workday. The workload that 61% of CMOs and VPs at multi-location brands currently describe as \u201ccomplex\u201d or very \u201ccomplex\u201d \u2014 tracking AI visibility, managing location data and listings, monitoring and responding to reviews, and posting local content on social media.<\/p> As I see it, the solution CMOs will want to implement is to stamp out the ROI-burdening exploratory agentic AI projects and focus on operating with it. Because the prize that comes from operating with it well is attractive for multi-location brands, who need to work quickly to restore declining traffic amid zero-click searches.<\/p> Reports indicate that revenue is increasing for brands as customers discover them via AI search \u2014 Adobe reports a 254% increase in revenue per visit for the retail segment. It\u2019s no wonder stakeholders are more interested in SEO and GEO performance than ever before.<\/p> Let\u2019s imagine a multi-location brand as a building with 200 rooms, each hosting its own party. The furniture hasn\u2019t changed, the walls haven\u2019t changed, the infrastructure hasn\u2019t changed \u2014 but there\u2019s a new entrance to the building, one that seems to be a shortcut for guests intentionally looking for you. The other entrances are still in use too. You want to maximize access through every single one so more people find the right room, have a good time, and come back for the next one. You don\u2019t hire someone to manually bring guests to each entrance. You invest in technology to put up signals that do the work for you, so your team can focus on the experience inside the rooms.<\/p> Context engineering is what builds those signals. It\u2019s when AI can orchestrate how brands make their digital footprint machine-readable, consistently accurate, technically discoverable across multiple surfaces, contextually relevant, and socially validated \u2014 without individuals needing to unpeel layers of tech stack insights.<\/p> If visibility on any search or marketing channel improves, every other location performance pillar improves: engagement, reputation, and conversion. These are the four pillars of Location Performance Optimization (LPO), a revenue-first framework I spoke about at brightonSEO in October 2025. LPO connects a brand\u2019s digital presence to commercial outcomes by activating location data and signals across these performance pillars:<\/p> An AI agent that implements these LPO measures to attract more customers, reach new audiences, and influence revenue isn\u2019t exploration. It\u2019s a hard-ROI workflow that pays for the program; they\u2019re the crucial layer that restores and increases search visibility, customer acquisition, retention.<\/p> So, when the board asks about AI ROI and local marketing performance, this new CMO doesn\u2019t just demonstrate AI adoption; they justify AI investment to continue to fund their operations. The gap between the brands measuring real ROI and the companies pretending to \u2014 or being preoccupied by their complex local marketing stacks is wider than ever.<\/p> EY described the moment we\u2019re in well: moving from vibe to value. The \u201cvibe\u201d phase was every company exploring AI \u2014 experimenting, piloting, racking up compute costs, layering up their tech stack \u2014 and either still being in that phase or having concluded it with the frustration of not knowing how to progress to real, quantifiable returns.<\/p> Marketing leaders at multi-location brands, like the Chief Marketing Orchestrator, must adopt and govern agentic-AI-powered stacks that are less exploratory and more ROI-driven. These are stacks that are sensible, streamlined, and enable teams to do things that just weren\u2019t possible before, like logging in to approve fixes, not to discover or prioritize what\u2019s broken. And that approval might not happen before a marketer can say \u201cGBP,\u201d but it\u2019s the orchestration layer \u2014 the added AI \u2014 senior marketers and leaders are looking for.<\/p> Find out how to use Uberall\u2019s UB-I agent for multi-location marketing for your operations<\/p> Image Credits<\/p> Featured Image: Image by Uberall Brand Studio. Used with permission.<\/p> <\/div> Local Search,Sponsored Posts#CMOs #Local #Marketing #Complex #Heres #Fix1782831899<\/p> ","protected":false},"excerpt":{"rendered":" This post was sponsored by Uberall. The opinions expressed in this article are the sponsor\u2019s own. Who should own AI search visibility across all our locations? Should I add more AI tools to manage local listings and reviews, or is that making it worse? 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Step 1. Decide Who Your Chief Marketing Orchestrator Will Be<\/h2>
The Key Responsibilities of a CMO<\/h3>
Step 2. Pivot From Finding New AI To Restoring Search Visibility<\/h2>
Implement The 4 Pillars Of Location Performance Optimization (LPO)<\/h3>
![\"A](\"https:\/\/cdn.searchenginejournal.com\/wp-content\/uploads\/2026\/06\/sej-ub-i-article-image-649-480x284.png\" "\\"61%")
How To Shift From AI Experiments To ROI-Driven Operations<\/h2>