The Franchise Marketing Gap: What 352 Brands and Operators Reveal About Winning Locally in the AI Era

Franchising has always carried a built-in conflict. The brand owns the strategy. The operator owns the sidewalk. Corporate decides what the marketing should say and when it should run, and then hands the actual execution to hundreds or thousands of local owners who each have their own priorities, budgets, and comfort levels. That split has never fully resolved, no matter how much software the industry has thrown at it.

Now artificial intelligence has entered the picture, and it is reshaping local marketing faster than most franchise organizations can build a plan around it. The promise is real: AI can finally sit between the brand and the operator as a shared layer that keeps both sides working from the same playbook. But promise and practice are two different things. Right now, most franchise systems are experimenting rather than committing, and almost none of them can tell you with confidence what is actually driving results.

To get a clear read on where the market stands, Uberall commissioned research firm Dialog to survey 352 US-based franchise organizations across retail, restaurant, and service categories in March 2026, backed by a round of qualitative interviews for context. I have worked through their findings, and the picture that emerges is a market caught mid-transition: expectations are climbing, adoption is widespread, and yet the fundamentals are still shaky. Below is my read on what the data says and what franchise marketers should actually do about it.

The headline: strategy is centralized, execution is scattered, and nobody can see clearly

If you take one thing from this research, take this. Franchise brands set the marketing agenda, but the local operators carry most of the weight of running it. Both groups agree local marketing matters, and operators actually rate it higher than the brands do. Yet both are tripping over the same basic problems: they cannot tell what is working, they are not sure what content to create, they struggle to keep business information accurate across the web, and they cannot reliably grow or respond to reviews. Add tight budgets, too many channels, and clumsy internal coordination, and you have a system that looks sophisticated on paper and improvises in the field.

A few numbers frame the gap:

• 81% of brands and 93% of operators say local marketing is very important or critical to success. Operators feel the stakes more acutely because they live and die by local foot traffic.
• Roughly half of both groups (51 to 52%) name not knowing what is working as their single biggest obstacle. That is a measurement failure, not a spending failure.
• Close to 90% of both groups report using AI, with operators moving a step faster than corporate.
• 82% of operators versus 69% of brands call visibility in AI search very important or critical.
• 67% of operators versus 50% of brands have already shifted budget into generative and answer-engine optimization, and operators are more likely to treat it as a discipline separate from traditional SEO.

That last set of numbers is the tell. The people closest to the customer are moving fastest, and the people who own the strategy are lagging behind them.

Who actually controls the marketing

In most franchise systems, strategy and decision-making sit at corporate. The brand dictates the plan and leaves operators limited room to deviate. But the brand is not the one placing the ads, replying to reviews, or updating the hours on a location page. That work falls to the operator, who is expected to execute inside brand guidelines, and who frequently does it unevenly.

A true shared-responsibility model, where the brand and the operator split the work as partners, is still the exception. Only about 25% of brands and 20% of operators describe their setup that way. Everyone else runs the top-down version, which makes sense in theory. Corporate has every reason to protect the brand. In practice, though, the lack of coordination creates friction and waste at the exact point where the customer meets the business.

The survey found that brands and operators use largely the same channels, and yet a familiar set of breakdowns keeps undercutting local performance:

• Campaigns do not run as designed. The brand builds a promotion with specific messaging and timing, and the local execution drifts, blunting the impact.
• Review management goes sideways. Individual operators improvise, sometimes offering incentives that violate platform rules, sometimes ignoring reviews altogether, and either way the brand's reputation takes the hit.
• Listings get out of sync. Brands push updates without operator input, and locations end up with broken pages, duplicate listings, and inconsistent citations that quietly erode rankings.
• Content feels generic. Corporate assets rarely capture the character of a specific location, but producing authentic local images and copy turns out to be harder than it looks.
• Local marketing goes off-brand. Under-supported operators make their own ads, posts, and offers, and brand compliance slips.
• Attribution stays murky. Connecting online activity to walk-in traffic and sales is the goal, and almost nobody has real visibility into it.

Customer spotlight - Pizzaville (Canada, 90+ locations)

Pizzaville is a working example of the shared model. Listings and reviews are managed centrally, while operators get the tools to act locally without breaking brand consistency. The results speak for themselves: a 78% lift in Google directions clicks, a 65% increase in click-to-call, and a 73% rise in Google Maps impressions. As their marketing coordinator put it, managing listings across directories went from overwhelming to a streamlined, consistent process.

Everyone agrees local marketing matters - operators just feel it harder

The importance of local marketing is not in dispute. Around 29% of brands called it critical and another 59% very important. More than 90% of operators agreed, and the share of operators calling it critical ran a full ten points higher than brands (39% versus 29%). The reason is straightforward. Brands are usually chasing awareness, while operators are chasing this week's covers, calls, and store visits.

The two groups mostly agree on which channels matter, with a couple of meaningful splits. Local listings management ranks much higher for operators, while brands report higher adoption across essentially every channel by ten points or more. That broader channel mix suggests brands have more bandwidth and bigger budgets, which they do, but operator budgets are nothing to dismiss. 71% of operators reported annual marketing budgets above $200,000, and 37% spend more than $500,000. These are real dollars being spent by people who often cannot measure the return.

Customer spotlight - Burger King Belgium (151 locations, Belgium and Luxembourg)

In a category where customers decide their next meal in seconds, Burger King Belgium moved past basic map listings and built a connected local journey, pairing central brand control with the local presence that actually drives action. The payoff: 1.68 million Google directions clicks (up 13% year over year), 372,000 website clicks from Google (up 9%), and an 88% review reply rate. Local visibility, in their words, is a primary driver of restaurant traffic.

The real problem is measurement, not effort

Here is the uncomfortable truth in the data. Despite healthy budgets, both brands and operators are stuck on execution and, above all, attribution. Measurement is the number-one pain point across the board. Just over half of each group said that understanding what is actually working is the biggest barrier to success. Both track multiple metrics, and neither trusts the framework it is using.

Increased revenue was the top success metric everywhere, which is sensible until you realize that tying marketing to revenue is mostly educated guessing when your attribution is opaque. Tracking has been a headache since the internet began, and it has not been solved. The danger is not just frustration; it is that bad attribution steers budget to the wrong places.

The channel data holds a surprise. Brands lean more on foot traffic and store visits as success measures, while operators lean on website traffic and social engagement, which are online signals. My read is that many operators simply cannot connect a store visit to a specific campaign, so they measure what they can see. Brands also pay more attention to ratings and reviews, which did not even crack the operators' top five. In plenty of systems, operators neglect reviews and lack any disciplined program for managing them.

After attribution, reviews and listings are the next biggest challenges for both groups, and those are the foundation of local marketing. It is telling that so many respondents struggle with the basics when the tools to handle them have existed for years. Operators are also more likely to feel buried by the sheer number of channels; in interviews, several coped by retreating to whatever felt familiar, usually social media, rather than building a system. They are also less sure than brands about what content to produce.

Even some brands flagged not enough budget as a problem, which is a little surprising given their spend. It may reflect ambitious multi-channel programs, or it may be the measurement problem wearing a different mask: when you cannot tell what is working, you cover more channels just in case, and the money runs thin. Either way, the pattern is clear. Strategy is centralized, execution is distributed, and no one has a clean line of sight into results.

AI adoption is a mile wide and an inch deep

Ask about AI and nearly everyone raises a hand: 88% of brands and 89% of operators say they use it. But using AI is a generous phrase that says nothing about sophistication or how deeply the technology is wired into daily work. The data puts both groups in the early-to-middle innings, with operators a step ahead. Interviews made the shallowness plain: AI adoption is often one person quietly using a tool for a few tasks, not a formalized process.

The timeline is worth noting. 53% of operators started using AI in the past year, versus 43% of brands. And corporate is pushing hard: 63% of brands are actively driving AI adoption among their operators, with 40% requiring it across multiple functions and 23% for specific use cases. Another 25% recommend it without mandating it. Often that mandate arrives quietly, baked into an industry-specific SaaS tool that happens to have AI features.

Where is AI actually being used? Marketing and sales lead, followed by customer support (think website chatbots) and business intelligence. Past those shared priorities, the two groups diverge. Brands spread AI wider, into reputation management (37%), product development (33%), and HR (31%). Operators concentrate theirs in operations and logistics (31%) and finance (31%).

The most revealing gap is reputation management. Even though reviews are a top pain point, only 26% of operators use AI for review management, compared with 37% of brands. That is a direct mismatch between where operators hurt and where they are applying the technology that could help.

Customer spotlight - SUNMED | Your CBD Store (310 locations, US)

The largest hemp and CBD retailer in the country rolled out an AI Social Writer to change how operators handle local social, replacing a bottlenecked central marketing team with brand-compliant content that any location can produce. It targets a specific pain: 26% of operators cite confusion about what content to create. As one franchisee said, owners who used to fear social media now find it easy. Underneath all of this sits a messy tool stack, a mix of SaaS platforms with AI features, specialized point solutions, and general-purpose models like ChatGPT and Gemini used for one-off tasks.

AI search: dismissed by skeptics, believed by operators

Is AI search overhyped or a genuine discovery channel worth real money? It is one of the liveliest arguments in digital marketing right now. Skeptics like to point out that AI-generated referrals account for only about 1% of website traffic, and use that to argue the whole thing is a distraction from Google.

I would push back on that reading, and so would the operators in this survey. Referral traffic does not capture AI's real influence, because people now research and shortlist inside AI tools without ever clicking through to a site. When the survey asked whether AI search shapes customer decisions even when it sends no direct traffic, 78% of brands and 86% of operators said it definitely or probably does. Operators believe it more strongly, and they are the ones watching customers walk in the door.

If AI influences buying without a click, then being visible inside AI answers becomes essential, which is exactly what both groups reported: 82% of operators and 69% of brands call AI search visibility critical or very important. The attribution problem resurfaces here too. A majority of operators (63%) say they track their AI appearances, versus 47% of brands, and most of that tracking is still manual. Operators are also confident in their AI strategy, with 74% rating it strong or very strong. That reads to me like an optimistic self-assessment for a discipline this young.

Optimizing for AI: right instincts, shaky confidence

When asked what actually drives AI visibility, both groups landed on the same top three: reviews, images and video, and social media, in slightly different order. A second tier followed, made up of fresh local content, structured data, and inventory data, with local landing pages and citations further down. Those instincts are broadly sound, but the ordering betrays a lack of confidence, which is fair enough given how fast the field is moving.

Most respondents appear to be leaning on their local SEO knowledge rather than a distinct AI strategy. Whether generative and answer-engine optimization are truly different from SEO is itself unsettled; a plurality (50%) of both groups see them as related but only partly overlapping.

The money is already moving. A majority of both groups have reallocated budget toward AI optimization. Among operators, 67% funded it by pulling from other channels (26%) or dipping into experimental budgets (41%), compared with 50% of brands. Brands were more likely to fund AI through a larger overall budget. Meanwhile, 27% of brands and 13% of operators had not put any money behind AI optimization at all. Looking ahead, 86% of operators and 75% of brands expect AI search visibility to become extremely or very important within three years, and most brands plan to spend more, with 53% expecting modest increases and 22% planning significant ones.

A better operating model: manage location performance, not channels

Traditional local SEO was built for a world where Google was the beginning and end of local discovery. That world is gone. Customer journeys now weave through Google, social platforms, and AI-generated answers before anyone decides where to spend. The industry is still arguing about what to call the discipline of managing visibility, reputation, and engagement across all of those surfaces at once. Uberall's term for it is Location Performance Optimization, a model built for how franchises actually operate: it links brand strategy to local execution and uses AI to turn scattered activity into something managed and measurable.

The stakes are higher than they look. AI platforms increasingly hand customers a single conversational answer instead of a ranked list to explore. If a brand is not in that answer, it is not in the running at all. That reality demands a wider view of location performance, one that spans every place a customer might search, AI interfaces included. It rests on four pillars that reinforce one another:

• Visibility across search, maps, social, and AI results.
• Reputation built on accurate business data and a steady flow of reviews.
• Engagement through regular posts, updates, and responses that signal an active location.
• Outcomes measured in the visits, calls, and transactions that activity produces.

Manage those in isolation and you forfeit the compounding benefit of running them together. And that isolation is precisely the problem the survey exposes: strategy defined at the center, execution happening locally and inconsistently.

Three priorities follow. First, get the data right, because fragmented or stale location information erodes trust and visibility, and standardizing it remains harder than it should be. Second, treat content as a first-class asset, since discovery is now conversational and intent-driven, which means location-specific FAQs, service descriptions, and an active review presence carry real weight. Third, close the measurement gap, because most organizations have almost no view into how often their locations surface in AI answers, and what you cannot measure you cannot improve.

Closing the gap

The core challenge in franchise marketing is structural. Brands own strategy, operators own execution, and visibility into performance is broken. AI does not erase that tension, and anyone selling it as a cure-all is overselling. What AI does offer is genuinely new: a coordination layer that can serve both sides of the relationship at the same time.

For brands, that means holding guidelines and consistency at scale, keeping listings accurate, reviews handled, and content compliant across every location without depending on operator discipline alone. For operators, it means relief from a real operational burden, from figuring out what to post to keeping business data current to deciding which channels are worth their time. When execution is coordinated and observable, both sides finally gain the confidence to invest deliberately instead of guessing.

The organizations that win will not treat AI as a pile of tools. They will treat it as infrastructure that connects brand strategy to local execution, replacing variability with consistency and fragmentation with a system. The structural tension will not vanish, but it can be managed deliberately rather than improvised across thousands of locations. The brands that internalize that shift will not just run leaner. They will open real distance between themselves and everyone still improvising.

About the research

The findings above come from a survey of 352 qualified franchise respondents fielded in March 2026 through an online panel, screened to include only people operating or working within multi-location US franchise systems of 25 or more locations, up to 500-plus. The sample split between brand-side respondents (43%) and local operators (57%), spanning retail, restaurants, and business services, among other categories. Both groups answered parallel questions to allow direct comparison, and qualitative interviews added context to the numbers. The survey was conducted by Dialog on behalf of Uberall.

References

• Uberall, Closing the Franchise Gap: Local Marketing in the Age of AI (survey conducted with Dialog, March 2026) - https://uberall.shorthandstories.com/closing-the-franchise-gap/index.html
• Uberall customer story, Pizzaville's Recipe for Success - https://uberall.com/en-us/customers/pizzavilles-recipe-for-success-with-uberall
• Uberall customer story, A Whopper of a Success: How Burger King Belgium Rules the Local Experience - https://uberall.com/en-us/customers/a-whopper-of-a-success-how-burger-king-belgium-rules-the-local-experience-with-uberall

• Uberall customer story, Making Local Work at Scale: Inside SUNMED's Franchise Growth Strategy - https://uberall.com/en-us/customers/making-local-work-at-scale-inside-sunmeds-franchise-growth-strategy
• Search Engine Land, AI sends just 1% of traffic - and it's mostly ChatGPT (November 2025) - https://searchengineland.com/ai-1-traffic-mostly-chatgpt-464653