What Restaurant Operators Actually Need from AI

Somewhere in the last 18 months, a question started showing up in your company's board meetings, in QBRs, in emails from an executive who'd just come back from a conference: What is our AI strategy?

You leave with an action item and an inbox that won't stop growing. Your POS has an IQ. Your delivery platform has an AI merchant suite. Your loyalty vendor just announced agentic capabilities. Every partner you have is racing to get "AI" somewhere in their product name.

Most of it is noise. Not because AI isn't real or the intent isn't genuine, but because there's a wide gap between announcing AI capabilities and actually deploying them against the problems your teams face every day. The industry is, by and large, still in the planning stage, even as the press releases say otherwise.

As Olo’s Chief Operating Officer, I spend a lot of time thinking about how we can both apply AI internally and use AI tools to drive incremental traffic and make life easier for our customers. I'm not writing this to add to the noise, but because the AI story that matters for your business is the one most vendors haven't been telling. They're busy announcing, while we're busy delivering.

We've been building AI for years

Olo has been the industry's go-to and most reliable digital ordering infrastructure for more than 20 years—processing over 4 million orders a day across more than 800 brands. That scale matters beyond transaction volume. The training data behind Olo's AI models encompasses more brands and locations (90k and counting) across all service models, food types, and growth stages than any single chain could build on its own, and deeper on individual guests (108M and counting) than any point solution working from a slice of the journey. Because the platform has been running at this scale, across this breadth, for this long, the AI works better. For years, it's been solving the problems your teams face every day.

Providing more accurate order quote times: Inaccurate pickup and delivery estimates kill repeat visits. OrderReady AI uses machine learning to analyze historical order data (by location, time of day, and order complexity) and gives guests accurate estimates. At P.F. Chang's, it achieved a 20% improvement in lead-time quote accuracy and a 50% reduction in manual lead-time extensions by operators. Better estimates for guests, less work for in-store staff, and more orders accepted.

Fighting fraud: Olo Pay's fraud models are built on restaurant transaction data, not generic retail patterns. They recognize what a legitimate restaurant order looks like, which means fewer false positives blocking real guests and fewer chargebacks slipping through. When honeygrow moved to Olo Pay, fraudulent orders and chargeback costs plummeted by close to 83%.

Delivering personalization at scale: Most cross-sell logic in restaurant ordering is static—the same suggestions for everyone, every time. Smart Cross-Sells use AI to recommend contextually relevant items based on a guest's order history, current cart, time of day, and location. In our testing, AI-powered cross-sells generated 10% higher basket values than static recommendations, because the model responds to the actual guest rather than applying a universal rule.

Identifying at-risk guests: Proactively knowing which guests are about to churn is one of the most actionable insights a restaurant brand can act on. Olo's Guest Data Platform uses machine learning to calculate individual-level guest lifetime value and churn risk. California Fish Grill used these predictive insights to drive a 41% increase in identified guests and $7 million in digital revenue from personalized campaigns.

Streamlining marketing: Restaurant marketers are stretched thin. The AI Creative Assistant built into Olo's Marketing suite turns a prompt into a campaign-ready email copy in one click—no separate tool, no context switching. A capability built into the workflow your team already uses.

Every one of these shipped because an operator had a problem that machine learning could solve better and faster than any generic, universal rules could.

Our philosophy: AI should solve your problems, not become one

Restaurant operators are under-resourced, under time pressure, and justifiably skeptical of technology that overpromises. They've seen enough "transformative" platforms turn into more dashboards to manage and more noise to filter. In our view, this is the number one problem stalling enterprise AI deployments.

Olo's standard: AI should be assistive before it's autonomous. Eliminate manual configuration work, surface the right insight at the right moment, execute with a human in the loop and build toward more autonomous workflows as trust grows. If it doesn't reduce the operator's workload, we redesign it.

AI belongs where it genuinely changes the outcome, not everywhere it could fit just to say "AI-powered."

What we're building next: from assistive to autonomous

We're developing an AI operations layer, Olo Assist™, that brings Olo's capabilities together into a coherent in-platform experience. The framing that guides how we build it: moving operators from "tell me what's happening" to "fix it for me."

The first agentic capability we're shipping is Hours Management. Instead of navigating a dashboard to update store hours location by location, the AI handles it, and the operator reviews and approves. That shift from doing to reviewing is what it actually looks like when AI reduces friction, rather than just describing it.

From there, Olo Assist™ is built around three modes that expand over time as operators build trust in the tool:

• Direct Action (Agents): the operator tells the platform what they need, and the AI executes—automating hours updates today, menu changes and operational tasks tomorrow
• Ask Anything (Conversational): a natural language layer on top of your data, so operators can ask plain-English questions and get answers without submitting a data request
• Proactive (Prescriptive/Predictive): AI surfaces recommendations before you have to ask—Smart Cross-Sells already do this at the ordering level, and over time it expands to campaign recommendations, segment alerts, and engagement triggers across channels

The level of autonomy is yours to set. Operators can stay in review-and-approve mode or let certain tasks run autonomously. Trust gets earned in stages.

Why the platform is the differentiator

Brands can assemble pieces of this stack from other vendors—especially if those vendors own the interfaces where operators ask questions, generate insight, and take action. Every point solution that becomes a daily workflow is a piece of the operator relationship moving to a different platform. Olo's answer is a stronger platform case: ordering (including catering), loyalty, payments, and guest data in one system produces better AI outputs and better economics than stitched-together point solutions.

This is where 20 years and 4 million daily orders become a structural advantage. Across hundreds of brands and billions of transactions, Olo's AI sees a guest's complete digital journey—how they pay, what they order, how they respond to campaigns, how they rate their experience. When the Guest Data Platform predicts churn risk or Smart Cross-Sells recommends an add-on, the model is working from that full context. A vendor operating on a single data source is working with a fragment of that picture.

Shopify built AI-driven conversion optimization (continuous testing, personalized recommendations, dynamic merchandising) into the core of its platform, not as a feature on top. Restaurant commerce has the same opportunity, but only if the data lives in one place.

That matters even more as AI agents start making purchasing decisions on your guests' behalf. As LLMs, voice assistants, and other agents start placing orders, the question is whether your brand is positioned to participate in that ecosystem, or gets routed through a marketplace instead. Through the Olo Network, built on Olo Accounts for persistent guest identity and agent-readable structured menus, we're building the infrastructure that keeps your brand in the transaction wherever the guest's AI is shopping. The Olo App, launching later this year, is built from the ground up for this world. Owned by your brand, not ceded to a third party.

The next 12 months

For the brands navigating this new frontier, the move isn't to evaluate every AI announcement that lands in your inbox. It's to make sure the platform your teams live in every day is already doing the work, and that you're partnered with someone who's been building it that way for years.

The restaurant brands that get this right won't be the loudest. They'll be the ones with their data organized, their direct channels owned, and a platform that was ready when the moment came.

Olo has been working toward this for a long time, because the problem restaurants have always brought to us—how to grow digital revenue without adding complexity for the teams running the business—is exactly the kind of problem AI is built to help solve. The brands already building, not just planning, are about to pull ahead. Olo is the platform that gets them there.

Jo Lambert is the Chief Operating Officer at Olo, overseeing all core business operations, including product, engineering, and go-to-market functions, as well as strategic initiatives spanning partnerships, business development, and AI strategy—positioning Olo to execute its vision across technology, market presence, and ecosystem growth. She brings more than 25 years of experience scaling businesses globally across financial services, media, and technology, known for being a transformational leader with a proven track record of operating and accelerating revenue.