Get Started with Kana

Back

Half of QSR Brands Are Investing in AI. So Why Is Marketing Still Three Weeks Behind?

Why quick service restaurant marketers need an agentic operating layer that acts on store-level signals while they still matter

Khan Smith
Khan Smith
Oct 22, 2026
Half of QSR Brands Are Investing in AI. So Why Is Marketing Still Three Weeks Behind?

In April, Starbucks and Little Caesars both launched apps inside ChatGPT. A guest can describe what they're craving, get a recommendation or a built-out order, and then hand off to the brand's own app to check out. On July 1, Square went a step further. U.S. restaurants on Square Online Ordering can now be discovered, browsed, and ordered from directly inside ChatGPT and Claude, with the order routed straight into the restaurant's point of sale (POS) system and kitchen display, and no added marketplace commission.

It's still early. When Restaurant Business asked ChatGPT, Gemini, and Claude where to get a burger delivered, all three defaulted to DoorDash, Uber Eats, or Grubhub, and the publication noted that artificial intelligence (AI) is likely behind only a small fraction of restaurant orders today. Meanwhile Olo, Toast, Square, and SpotOn are all building ordering experiences designed to be read by AI agents.

The direction is clear enough, though. The moment a guest decides what to eat is starting to happen inside a conversation the brand isn't part of. What lands on the restaurant's side is the order. For quick service restaurant (QSR) marketers, that raises the value of the signals they still own: the POS, the loyalty program, the app, and store-level traffic. And right now, most teams are acting on those signals weeks after they show up.

QSR's Timing Problem Is an Architecture Problem

We've been talking with a lot of restaurant marketing teams this year, and the pattern is consistent. Nobody is short on tools. Most QSR brands already run a POS, an ordering platform, a loyalty program, a customer relationship management (CRM) system, a data warehouse, a handful of agency relationships, and a co-op structure. None of them reconcile cleanly, so the marketer ends up being the connective tissue. Our own research puts the cost of that at 60 to 80 percent of an experienced marketer's time spent on operational execution rather than judgment.

That overhead shows up as lag. Co-op and designated market area (DMA) budgets get locked quarterly. Day-part campaigns get committed weeks out, blind to weather, local events, and competitor moves. The guest decides in minutes. By the time the weekly comp-sales readout arrives, it's a record of decisions made three weeks too late.

The lag is getting more expensive. U.S. restaurant traffic fell 2.9 percent year over year in November 2025 while the average check rose 3.8 percent, according to Revenue Management Solutions data reported by QSR Magazine. Placer.ai data cited by eMarketer shows quick service visits fell in four of the first five months of 2026. And the value wars haven't fixed it. Facteus found that 15 of 18 major QSR brands had lower customer retention in May 2026 than when the value wars started in May 2024, down 1.69 points on average. Of the 12 brands that grew transactions over the past year, 9 still saw retention fall.

A discount can buy a visit. It doesn't seem to buy the next one. The bigger issue is targeting. A national promotional calendar can't tell the difference between a market that needed the offer and one that would have converted anyway, or between an item your kitchen can serve tonight and one that's 86'd. So the offer goes out everywhere and the margin goes with it.

Then there's the part with no real analogue in other industries. Franchisees contribute 2 to 7 percent of gross sales to pooled marketing funds, and they can see exactly which stores got left out. Your marketing accountability is distributed to people who hold the receipts, and that conversation is impossible to win with a national number that showed up late.

Where Agentic Marketing Changes the Equation

At Kana, we define agentic marketing as an operating layer built to learn, so a marketer's judgment and decisions compound instead of getting rebuilt from scratch every time. In a restaurant context, the practical meaning is simple. The system watches the signals you own, connects them across the stack you already run, and puts a recommended action in front of your team while the guest is still deciding.

Based on the data, the QSR industry isn't short on AI adoption. In a survey of roughly 170 limited-service brands cited by the Association of National Advertisers (ANA), 51 percent were already investing in AI, another 22 percent planned to start within the year, and 53 percent of those investing were applying it to marketing and personalization.

The same survey also shows what's holding the category back. 37 percent of restaurant brands say siloed technology is keeping them from getting full value from their AI investments. That's a plumbing problem, and it shows up in the broader data too. Epsilon's 2026 benchmark study found that 71 percent of marketers use AI for productivity, but only 9 percent use it for revenue generation. In Kana's own survey of 225 senior leaders, team skills and acumen ranked as the top barrier to agentic marketing, ahead of data quality and budget.

So the better question for a QSR chief marketing officer (CMO) to ask any Agentic AI vendor is what it's prepared to own. A few things we'd press on:

  • Does it write as well as read? A system that only reads is basically just a fancier dashboard.
  • Are permissions checked before the agent acts? A franchisee's store data isn't corporate's to use however it likes, and only a system that enforces that up front should survive a franchise agreement.
  • Can the audit trail answer a franchisee? When someone asks why their market's budget moved, "the model decided" just isn't going to fly. A dated, reasoned record should be the baseline requirement.
  • Does it give the same answer twice? Ask the same question three times in the demo. A system that can't do that can't support a co-op reporting obligation.

How We Recommend QSR Teams Begin Agentic AI Adoption

Sequencing matters more than speed. We recommend restaurant marketing teams start with on-demand agents that complete a defined task when asked, move to continuous agents that monitor in the background and flag what changed, and expand into autonomous agents only as trust builds. Teams that start at the third level tend to discover their data problems in front of a franchise advisory council, which is the worst possible room for that discovery.

We'd also scope the first pilot around a connection between systems rather than around a campaign. If siloed data is what's broken, a pilot that succeeds on top of it hasn't proven anything you can scale. Here's where we'd start:

  1. Build the segment while the moment is still there. Pull sized, suppressed segments straight from CRM and loyalty data in minutes instead of filing a two-week ticket with the data team. Suppression matters more than sizing in QSR, because the guest you shouldn't send the offer to is usually the one who was already coming in.
  2. Check the kitchen before the offer sends. Have an agent watch store-level operational signals and catch a promotion for an item that's 86'd before it goes out. The guest who finds out at the counter learns something about your brand that no campaign will unteach.
  3. Let spend follow the market. Shift budget toward whatever channel and market is converting right now, with your team approving every move. This is also the step that changes the franchisee conversation, because it starts from real store-level numbers instead of a broader system average.
  4. Build offer logic for each member. Aim each offer at adding a visit that wasn't already coming, instead of sending the same buy-ten-get-one to a weekly regular and a lapsed guest. We put this one last on purpose. It's where the margin is, and it depends most on the connections above being stable first.
  5. Measure AI ordering as its own channel now. Square already makes the source of each AI-placed order visible in its reporting. Tag those orders separately while the volume is small, so you have a baseline when it isn't.

Why Kana Is Built for This

QSR marketing leaders are usually offered two choices for new software. Build it, which is expensive and never quite finishes. Or buy it, which fits generically and then takes months of integration work. Packaged martech looks like it should fit restaurants, but your franchise structure isn't your competitor's, and neither is your day-part mix or your co-op math. Kana takes a third approach we call Build-With: applications shaped to your workflow, configured to your data, and governed by your policies, delivered at the speed and cost of buying.

That means no rip-and-replace and no data migration. Kana connects by application programming interface (API) and sits on top of Toast, your warehouse, your loyalty platform, your ordering system, and your ad platforms. Our Forward Deployed Engineers work inside your environment during onboarding. The knowledge graph underneath your applications learns the things no general model has ever seen, like your day-part structure, your menu architecture, your franchise map, your co-op boundaries, and your loyalty economics, and it gets smarter with every interaction. When models update, platforms change, or regulations shift, the maintenance stays with Kana. It doesn't land on a marketing team that was never staffed to carry it.

Kana's Audience Builder, Campaign Orchestrator, and Personalization applications are built to run that loop together, from the first segment to the offer logic behind each member's next visit.

AI search, and agentic ordering and commerce is going to keep changing, and quickly. The restaurant brands that come out ahead will be the ones acting on what they know about each market while the guest is still deciding. For a deeper look, download our white paper, Nobody Eats at the National Average. And if you'd like to see what this looks like on your own data, get in touch today.

Sources

Khan Smith
Khan Smith
Oct 22, 2026
Stay Connected
Subscribe to our announcements and notifications.