AI in Restaurants: Diners are Warming Up, But the ROI Case Is Still Thin

Restaurant customers appear to be getting more comfortable with AI.

In 2025, 41% of consumers surveyed by PAR Technology said they would choose a restaurant that avoided AI altogether. In PAR's June 2026 survey, average discomfort across restaurant AI use cases fell to 27%. Nearly three in four diners are now open to AI playing some role in their restaurant experience, depending on how it is used.

Restaurant operators are interested in technology, too. In the National Restaurant Association's 2024 Restaurant Technology Landscape Report, 76% of operators said technology gives them a competitive edge. Nearly half said they expected technology and automation to become increasingly important for dealing with labor shortages.

But neither study proves that AI is cutting restaurant labor costs, reducing food waste, or increasing profits. Customer acceptance may be moving in AI's favor, and operators have plenty of reasons to experiment, but the business case still needs to be proven one restaurant at a time.

Restaurant automation and AI aren't the same thing

Restaurant technology now comes with an increasingly long list of AI labels. That doesn't mean every automated feature works — or should be evaluated — the same way.

Before paying extra for an AI feature, ask the vendor a few basic questions:

• What restaurant data does the system use?
• What exactly does the AI predict, recommend, or automate?
• Can managers review or override its decisions?
• How does the vendor measure accuracy?
• What happens when the system gets something wrong?

The National Restaurant Association offers one useful example of what restaurant technology can accomplish. Dough Boy Pizza Co. founder Erica Barrett reported labor costs of roughly 11% to 15% of sales and fewer than 1% of orders being returned at her food-hall concept, where self-order kiosks eliminate the need for cashiers.

That's an impressive operator result, but it isn't a controlled study. Dough Boy also uses an integrated POS and digital menu boards, and the figures don't tell us how the business would have performed without its technology-focused operating model.

For restaurant owners evaluating AI, that's a good standard to keep in mind: a promising example is worth investigating, but it isn't automatically proof of ROI.

3 restaurant AI uses worth testing

PAR found that 48% of surveyed consumers are comfortable with AI making operational decisions such as adjusting labor based on demand or optimizing menus based on inventory.

That doesn't mean operators should automate everything. It does point toward several areas where a controlled test could make sense.

1. Labor forecasting and scheduling

Labor is one of the most obvious targets. The NRA found that 37% of operators expected to invest in automated labor management, recruitment, or scheduling systems in 2024. Another 47% said they expected technology and automation to be increasingly adopted in response to labor shortages.

For a restaurant with several shifts, changing demand patterns, and plenty of historical POS and scheduling data, AI-assisted forecasting may be worth testing.

Don't judge the tool by whether its schedule looks smart. Compare:

• Scheduled labor hours vs actual labor hours
• Labor cost as a percentage of sales
• Overtime hours
• Manager time spent creating schedules
• Understaffed or overstaffed shifts

Record those numbers before turning the feature on. Managers should also retain the ability to change an AI-generated schedule before employees receive it.

Smaller restaurants may not need sophisticated forecasting. If your manager can build an accurate schedule quickly using predictable weekly sales patterns, adding another software subscription may create more cost than value.

2. Inventory and menu decisions

Inventory is another strong candidate. More than half of restaurant operators surveyed by the NRA — 52% — said inventory control and management was a technology spending priority. The Association notes that better inventory management can help reduce spoilage and internal theft.

AI adds another layer by potentially spotting patterns in sales, inventory, and menu performance that managers could otherwise miss.

The best candidates are restaurants already collecting reliable inventory and recipe-costing data. If your counts are inconsistent or recipe costs haven't been updated in months, an AI recommendation is only working with bad inputs faster.

During a pilot, track:

• Food cost percentage
• Waste
• Stockouts
• Inventory variance
• Menu-item contribution margins

Keep menu and pricing changes subject to chef or manager approval until you know how reliable the system's recommendations are.

3. Personalized loyalty offers

Restaurant loyalty programs already have a large base of willing users. The NRA found that 52% of consumers participate in a restaurant, coffee shop, snack shop, or deli loyalty program. Among those consumers, 96% said loyalty programs help them stretch their dining dollars.

PAR's newer research suggests consumers are becoming more receptive to AI-powered personalization as well. Trust in personalized AI offers based on dining history increased from 33% in its 2025 survey to an average of 45% in 2026.

That makes personalization an interesting test for restaurants that already have good customer purchase data.

Instead of measuring how many offers your system sends, watch:

• Redemption rate
• Average check
• Repeat visits
• Incremental revenue
• Discount cost

An offer that gets a high redemption rate but mostly discounts purchases customers would have made anyway isn't necessarily successful.

Related: Best Restaurant Employee Scheduling Software in 2026 

One use case to watch before investing: LLM ordering

Ordering through tools like ChatGPT may eventually become another restaurant sales channel.

PAR found that 62% of consumers surveyed would order through a large language model if given the right offer. Interest was higher among Millennials at 73%, Gen Z at 70%, and parents at 74%.

Those are striking numbers, but they're still answers to a hypothetical survey question. They don't tell us how often consumers will actually place restaurant orders through an AI assistant.

Before treating LLM ordering as a major sales opportunity, restaurants need answers to some practical questions:

• Who owns the guest relationship?
• Are there commissions or transaction fees?
• Who handles refunds and order problems?
• How does the AI receive current menu, pricing, modifier, and availability data?
• Can restaurants see customer and transaction data?
• What happens when the model gets an order wrong?

For now, I'd watch this space closely without building a restaurant's ordering strategy around it.

Customers still want humans in the restaurant experience

One of the clearest findings from PAR's research is that customers aren't asking restaurants to choose between technology and people.

They're more comfortable with AI when they can see a benefit. Automation should support hospitality, not replace it. 

PAR found that consumer comfort rises to 51% when AI helps shorten waits or make service more consistent. But 47% of respondents said AI goes too far when it replaces most human interaction, while 44% drew the line when reaching a person becomes difficult.

Only 7% said a restaurant couldn't become too automated. The right level of automation can depend on your restaurant format.

The NRA found that 70% of limited-service customers would use a smartphone app to place an order and 65% would use a self-service kiosk. Among full-service diners, 60% said they would order using a tableside tablet.

Those aren't specifically AI preferences, but they illustrate an important operating principle: use technology where customers already value speed and convenience, and be more careful where personal service is part of what they're paying for.

A kiosk may feel completely natural in a quick-service restaurant. Removing access to a server in a full-service dining room is a different proposition.

Transparency matters more as AI becomes customer-facing

Restaurants should also tell customers when AI is playing a meaningful role in their experience.

In PAR's 2026 study, 43% of consumers ranked transparency as their top factor for trusting restaurant AI. The previous year's survey found that worker replacement was a major concern, although the questions and measurements across the two studies aren't directly comparable.

For an operator, the practical lesson is simple: don't make guests guess.

If an AI agent is answering the phone, say so. If customers are chatting with automated support, make it easy to reach a person. If AI is personalizing an offer from purchase history, explain how customer information is being used.

Automation works best when the customer gets convenience without feeling trapped inside it.

Restaurant labor data can provide context — not your answer

National labor statistics can help operators understand the environment around them, but they shouldn't determine whether an AI tool is worth buying.

The Bureau of Labor Statistics' Job Openings and Labor Turnover Survey does publish job openings, hires, and separations for the broader accommodation and food services sector. That's more relevant to restaurants than an economy-wide number, but it still includes businesses beyond restaurants and can't tell you what's happening inside one location.

Your own numbers matter more.

If you're considering an AI recruiting, forecasting, or scheduling tool, establish a baseline for:

• Applicant volume
• Time-to-fill
• Employee turnover
• Overtime
• Labor cost percentage
• Manager administrative hours

Then compare the same measures after implementation.

That's the evidence that tells you whether the software is helping your restaurant.

Related: Restaurant Operations Management: Benefits, Best Practices, and Metrics for Success 

Last bite

Pilot restaurant AI before you scale it. The latest surveys give restaurant operators a reason to pay attention to AI. Consumers appear more comfortable with it than they were a year ago, and operators continue to invest heavily in restaurant technology.

What we don't yet have from these sources is broad, restaurant-level evidence showing exactly how much AI reduces labor costs, cuts waste, or increases profits after software and integration costs.

So don't buy AI because the category is hot. Give the technology a job.

Choose one operational problem. Record your current performance. Test the tool at one location or with a small comparable group. Decide in advance which metric needs to improve and how quickly the investment needs to pay for itself.

Then measure the result.

If labor forecasting reduces overtime without hurting service, expand it. If personalized offers drive incremental repeat visits after discount costs, keep them. If an inventory tool doesn't move food costs or waste enough to cover its fees, stop paying for it.

For restaurant operators, AI doesn't need to prove it can change the industry. It needs to prove it can improve your operation.