Most multi-location advertisers struggle to scale their operational efficiency because manual processes, which are inherently unscalable, tend to become completely untenable as an advertising strategy’s complexity increases. And when it comes to complexity, multi-location campaigns are the top of the food chain. 

This article outlines why it’s so difficult to scale multi-location ad campaign strategies and how two enterprise-level advertisers—Cox Automotive and Storage Rentals of America (SROA)—were able to convert multi-location complexity into their competitive advantage by centralizing and automating their approach.

Why you can’t scale your multi-location advertising campaigns

Consider this: every multi-location ad campaign incorporates many unique variables. Specific locations or addresses, multiple ad channels, unique strategies, hundreds of product sets, different audiences, even local business nuances…every one can play a factor in the types of strategies you build. 

But for every variable you’re trying to account for within your ad campaign, you’re creating more chances for something to go wrong, which means more pressure on the system (and teams) underneath it all. 

Campaign complexity multiplies with each new variable but your team’s capacity increases in a straight line. You’re actually facing a structural barrier, not a workload problem. This challenge is amplified by the fact that the data sources you rely on for multi-location strategies—local inventory signals, brand rules, budgets, location-level business goals—rarely live in one place. 

A manually-built multi-location campaign strategy for 10 locations can survive for a while. But if you try to apply the same build process for 1,000 locations, it will fall apart. Your strategy can be rock-solid, but the workflow to put everything together wasn’t built to handle the operational complexity required to pull it off.

The four reasons why manual multi-location advertising workflows aren’t scalable

You hired your team to be strategists, but most AdOps team members are relegated to mere “copy and paste” connectors between advertising systems. This brings expensive and unnecessary risk to your AdOps. One wrong budget entry, one missed creative update, or one broken naming convention, and the negative impact compounds quickly. These are predictable (and often seen) outcomes when people are forced to repeat the same tedious-but-vital actions at scale.

When multi-location ad campaigns are built using manual workflows, advertisers tend to hit one of these four roadblocks (or, honestly, all of them) pretty quickly:

Manual campaign builds hinder speed to market

The sheer volume of grunt work required to launch localized campaigns across hundreds of locations can’t be outpaced by hiring more people. When every location-specific creative, copy tweak, and channel setting must be handled individually, launch deadlines stretch from hours to weeks. Longer launch timelines often mean opportunities, slower optimizations, and less value delivered to clients when timing matters most.

Untimely budgeting processes drive up costly errors

Pacing budgets across hundreds or thousands of location-channel combinations is one of the easiest places for an unstructured campaign system to break. Manual budget management often leads to underspend and overspend, costly errors and credits, and near-constant anxiety around where dollars are actually going.

Inconsistent performance reports take forever to build 

Disconnected multichannel data sources force teams to rely on slow reporting processes that ultimately deliver stale, unactionable insights. Plus, relying on humans to build reports by hand means you often end up with client reports built in the same program in different ways, causing confusion and weakening client trust.

You lose your strategic agility

Lastly, all of these things—manually building reports, pacing across different channels, QA’ing or implementing one-off fixes—leave your team with little time left to do the strategic work they need to move things forward. That means no testing, no channel expansion, no new strategic ideas. Ultimately, this is how operational friction halts growth and profitability for your entire organization.

How to scale franchise advertising with agentic automation and AI

The key to overcoming these four roadblocks to achieve truly scalable multi-channel advertising strategies is replacing high-risk manual tasks with deterministic agentic automation. This requires building a foundational operational layer for your AdOps by connecting your data, workflows, assets, and business rules into one unified system. 

Curious what this looks like in practice? Let’s take a look at how both an advertising agency and an in-house team converted their team’s multi-location ad campaign workflows from being manual to using agentic AI and automation. 

Case study: How an enterprise advertising agency runs multi-location strategies for thousands of localized auto dealers

Cox Automotive shows this problem looks like from the agency side. For Cox Automotive, the challenges of multi-location management wasn’t isolated to one brand, one message, or one report. It was an environment with massive dealer volume, distributed execution, and a reporting burden that had become a drag on the business.

"My team was very bogged down in data entry and reporting,”said Anne Thiel, Senior Director of Advertising and Revenue Operations at Cox Automotive during a session at POSSIBLE 2026. “It was just such a time suck. Automation has freed them up."

Working with Fluency, Cox Automotive translated the company’s complex decision trees (historically done by talented strategists) into agentic automation through Fluency Blueprints. This enabled campaigns to be set up, launched, and optimized dynamically based on real world conditions.

By documenting every "if this happens, then do this" scenario for campaign variables, Anne’s team has created a governance layer so agents could handle specific, repetitive tasks for multi-location campaigns without human intervention. 

In addition to setup and launch, the Cox team has also solved reporting issues agentically. 

Reporting wasn’t challenging just because it was a lot of manual work (though obviously it played a factor). The team’s reporting methodologies started to slip because it wasn’t possible to execute consistently at scale. 

Technically, yes, the team was building multichannel performance reports for their clients. But they weren’t building reports in a way that could be standardized, repeated, and scaled without burning valuable team time or driving up labor costs.

"Imagine 40 to 50 different ad strategists sending 5,000 dealer clients a totally different deck every month, each with different tone and representation,” said Anne. “That's what we were doing."

Anne’s team uses Fluency to automatically create, deliver, and execute monthly client reports (all under the close supervision of ad strategists). Each monthly client performance report is built according to each client’s specific KPIs and brand elements, then automatically delivered to the client on a set cadence. Cox’s team remains fully hands-off from the creation and delivery of monthly reports, aside from setting the agentic workflow up and defining the strategy. Anne mentioned that the decks also include “a really cool AI agent voice that reads through the deck for the client, so they can listen to it anywhere."

Anne said that using these technologies has drastically improved monthly reporting workflows for their enterprise multi-location clients by making the process more consistent, scalable, and easier to distribute. 

Anne is also excited by the fact that she’s shown the company how agentic AI can add value to their AdOps in a practical way, directly within daily workflows.  Not as a flashy layer on top, but as part of the workflow. She credits AI and automation as critical tools that “greatly helped us scale the business and be more consistent."

Case study: Running multi-location strategies for 650+ stores in-house as a national brand

Storage Rentals of America (SROA) is a great example of how agentic AI and automation can help advertisers overcome the same core multi-location problems from the in-house side.

Melissa Cartagena, Chief Digital Officer and CMO at SROA, recognized that the scale of SROA’s portfolio had already outrun the team’s ability to manage it manually. 

"When I first started a little over a year ago, I had only 2 people managing paid media across 650 stores,” she said during a POSSIBLE 2026 session on agentic AI and automation in advertising. She immediately recognized that this wasn’t just a staffing problem, it was a foundational operating model problem. 

“One of the first things I thought was: we need a platform to automate."

To solve this, Melissa and her team worked with Fluency to centralize key data signals—like occupancy, pricing, and move-ins—into a single operating system to drive real-time performance. A unified architecture allowed SROA to consolidate platforms and maximize full-funnel efficiency across multiple networks. 

“We centralized feeds from our internal environment and focused on business outcomes, not just digital KPIs,” she explained during the session at POSSIBLE. 

It also meant she could move her team away from manual execution to autonomous decisioning. She emphasized that this shift required a new approach to quality assurance and team management at scale.

“Change management is definitely a thing,” Melissa noted, emphasizing the need for a team “willing to embrace” the technology and "think differently" about their day-to-day workflows.

With a centralized platform and automation in place, Melissa was confident that the SROA team could move at a very different speed. She’s already seen the benefits paying out. 

"If the CEO was to come to me tomorrow and say, 'I need to load up another 100 stores because we're acquiring these stores,' I now know that I can launch them within a 24 to 48-hour period of time versus weeks and weeks of having to manage campaigns across multiple networks," Melissa said during POSSIBLE 2026. 

Using agentic automation means the team has to do less manual setup for localized campaigns, which means both faster launches and less dependence on disconnected publisher workflows. 

In addition to speed, Melissa’s team gained strategic capacity, which she said “was the biggest thing for me as a brand.”

“Now, the team can just step back and be more strategic,” she added, “because they don't have to worry about the things they were living in day-to-day before."

Build an operational foundational layer to scale multi-location ad campaigns with agentic advertising 

Cox Automotive and SROA are different organizations—one is an agency, one is in-house, and they work within vastly different industries—but the patterns underlying their operational challenges were the same.

Both companies were trying to run highly complex multi-location digital ad strategies that had outgrown their human team’s capacity to execute them effectively with manual workflows. 

Melissa, Anne, and their teams realized that in order to solve the localization complexity problems they faced, they needed to start running their AdOps workflows as a governed system.

When you connect your data, workflows, and business rules into a single foundational layer to run your advertising operations, the benefits are felt all the way to your bottom line: 

• More strategic capacity: Your human teams shift from "data entry and reporting" to upselling, testing new strategies, and having meaningful conversations with clients.
• Faster launch times: According to Melissa, localized campaign builds that used to take weeks to build out can now be launched in 24 to 48 hours using agentic automation.
• Lock-tight budgeting integrity: Deterministic automation eliminates manual data entry errors and the stress of babysitting pacing throughout the month. Best of all, automated budgeting for advertisers eliminates costly overspends and underspends across your entire portfolio. 
• Consistent, ready-to-share performance reporting: With agentic AI and automation working within your, campaign performance decks can out on the same day every month with a consistent tone without wasting hours exporting and merging data sets.  
• Exponential multi-channel scalability: Instead of repeating manual campaign setups for every individual channel and variable, teams can set hyper-localization strategies at the root level. Agentic automation tools, like Blueprints, then build and launch ads across Google Ads, Meta, TikTok, and programmatic DSPs en masse. Basically, automation replaces thousands of manual clicks with a single, governed workflow.

Structured operational governance is key to agentic advertising for multi-location advertising campaigns

In addition to driving multi-location performance, both SROA and Cox are focused on safely and reliably leveraging AI. Running agentic AI within a governed infrastructure means you can truly maximize and scale AI’s effectiveness across your AdOps. 

"You cannot start building agents without governance,” Anne emphasizes in her POSSIBLE presentation. 

For example, Anne and the Cox Automotive team are creating "run books": clear decision trees that tell an AI agent exactly what to do if a specific campaign variable changes. By putting this governance in place first, Anne and her team ensured that AI is a functional part of the workflow and not just a "flashy layer" tacked on top of their best-practice strategies.

Both of these organizations are also using their human teams to oversee all agentic workflows. This is a best practice designed to mitigate the risks of AI hallucinations or off-strategy execution by keeping humans as the final gatekeepers. maintaining a final layer of human accountability.  

Building this governance layer first is what is enabling the company to use automation to absorb the most time-intensive, repetitive tasks and let Cox’s humans do the work that actually moves the bottom line. 

How to improve franchise advertising efficiency and agentic governance

Simplifying your strategies to fit your current workflow limitations will limit your company’s long-term competitive edge. But if you’re ready to turn multi-location complexity into an advantage, the answer is bringing in a governed system that can execute, adapt, and scale under pressure.

The answer is to build a workflow that can support the real complexity of the business.

So, how can you do that? 

• First, you need to build governance infrastructure for your AI tools. It has to sit inside the system that holds your data, workflows, and business rules together. Otherwise it creates more noise than value.
• Second, focus on integrating AI and automation agents that take the most time-intensive tasks off your team’s plate. Reporting, budgeting, campaign or account launches, and ongoing campaign management tasks are the best jobs for agentic automation to absorb. (They’re also the tasks that suck out your teams’ souls). 
• Lastly, make sure you’re truly building solutions that address operability and not just capability. Bringing in more tools to address isolated features or pain points will only add to your operational drag. A truly scalable solution will work across channels, enable execution across multiple AdOps functions, and deliver more value when complexity increases instead of less.

Bringing your advertising business into the agentic age means you’ll need to rethink the way things are currently being done. For some advertisers, this shift is harder than others. But if you can get your organization on board with centralizing your data foundation and automate operational burdens, you’ll truly be able to turn scale from an adversary into an advantage.