Only 3% of restaurant operators are rolling out AI companywide

By James Calloway

Only 3% of restaurant operators said they were implementing artificial intelligence (AI) projects companywide, while nearly 39% reported using AI tools in some form and 20% were experimenting with small-scale pilots.

The National Restaurant Association survey of 212 operators, conducted between June 18 and July 18, found that 31% had no plans to implement AI. Another 42% were taking a cautious approach, 26% were excited, 18% were skeptical, 7% were not interested and 4% were unsure.

Why are operators holding back?

The National Restaurant Association’s report said: “The results suggest that many operators are still evaluating the business case for AI before committing to broader adoption.”

Most operators are not rejecting the technology; implementation practicality is holding them back, according to the source. Four years of intense AI attention, including drive-thru voice ordering and AI agents, has not produced broad restaurant deployment, and the source names no platform, restaurant chain or vendor behind those applications.

Their leading concerns were:

• 46% cited complexity.
• 43% cited cost.
• 42% worried about customer acceptance.
• 35% cited data security and privacy.

No dollar amounts, implementation schedules or integration scopes accompany those answers. Operators therefore have no disclosed basis for judging whether labor savings, higher sales or better service will repay implementation and training expense.

Where is AI actually being used?

Among the nearly 39% of restaurants using AI in some form, the most common applications were:

• Personalized marketing and outreach, 58%.
• Customer-feedback analysis, 48%.
• Recruiting, scheduling and labor management, 37%.
• Customer-demand and sales forecasting, 34%.
• Taking orders and processing payments, 20%.

These percentages describe use cases among AI users rather than shares of the entire survey, and the source does not say whether respondents use one tool or several. The report characterizes the priorities as pragmatic and low-risk.

The mix is operationally coherent. Personalized marketing can influence customer economics, including spending, visit frequency and order value, while feedback analysis can shape menu and service decisions. Neither result is measured here: the survey discloses no ticket lift, conversion change, complaint rate or revenue gain.

Forecasting demand and sales, used by 34%, bears directly on inventory turns, meaning how quickly restaurant stock sells and replenishes, and on food waste. Labor-management tools at 37% can affect labor expense, while order and payment systems at 20% can affect throughput and labor allocation. The report supplies no forecast-accuracy gain, waste reduction, labor-hour saving, transaction volume or margin result.

The report also offers no gross-margin result, defined as revenue remaining after product costs, so sales growth and labor efficiency remain assumptions rather than measured operating economics.

What would lower the barrier to adoption?

Operators identified three practical routes:

• More training and education, 56.5%.
• Financial assistance or incentives, 45%.
• More case studies or examples, 42%.

Training leads the list, indicating that operators want operating knowledge before they commit capital. The source specifies no provider, incentive amount, eligibility rule or measured outcome from the requested case studies.

Financial assistance addresses cost, but 43% still regard cost as a leading obstacle. Training can reduce complexity, yet 46% continue to identify complexity as the largest concern. Neither intervention proves the return on an individual deployment.

What can operators and suppliers conclude?\n

Customer economics remain unsettled. Forty-two percent of operators fear customers will not accept AI, while a recent Gallup poll found that only 35% of Americans believe AI will improve their lives. That Gallup figure measures public belief, not restaurant transactions, order frequency or willingness to pay.

The survey provides no respondent geography, chain-size breakdown, named distributors or suppliers. It also identifies no technology platform, installed base or disclosed transaction volume. In technology procurement, channel power—the operator’s ability to set contract terms and timing—therefore remains with restaurants rather than unproven vendors.

A separate Technomic Top 500 spotlight included in the source reports that chain restaurant sales slowed again in 2025 as consumers reduced dining, although coffee, beverages, snacks and chicken thrived. The spotlight provides no margin, traffic or transaction figures, so it cannot establish whether AI will offset pressure on restaurant unit economics.

The next test is evidential, not rhetorical: whether vendors and operators disclose paid deployments, transaction volumes, labor hours saved, forecast accuracy, waste, inventory turns, customer acceptance and gross-margin effects. Until they do, the 3% companywide adoption figure carries more information than headlines about AI agents.