An interview with AI on how it expects to change our industry

Peter Romeo

Right now, an investment in AI can be expensive. What kind of return should an adapter expect?

 

ChatGPT

You're absolutely right — AI investments in the food-away-from-home industry can be expensive upfront, particularly for small to mid-sized operators. But the expected return on investment (ROI) can be strong if deployed strategically, often in the 15–40% ROI range within 1–3 years, depending on the application.

Here's a breakdown of the cost-benefit tradeoffs and ROI expectations:

AI Applications	Upfront Cost	Payback Period	Potential ROI
Inventory & Waste Management	Low–Moderate	6–12 months	15–30% (via waste cuts)
Labor Scheduling Automation	Low	<6 months	10–25% (reduced overtime, fewer errors)
Personalized Marketing Tools	Moderate	6–18 months	20–40% (increased repeat business, higher ticket sizes)
Self-Service Kiosks / Ordering	High (hardware + software)	1–2 years	25–50% (faster orders, fewer errors, upselling)
Kitchen Robotics	Very High	3–5 years	15–30% (labor savings, consistency)
AI for Delivery Routing	Moderate	6–12 months	20–35% (fuel and time savings)

 

Where ROI Is Strongest:

• High-volume environments (e.g., QSRs or chains) benefit most because even small gains scale fast.
• Labor-intensive operations see faster returns, especially in cities with rising wages.
• Delivery-heavy models (e.g., pizza chains, ghost kitchens) gain from logistics AI and customer targeting.

What ROI Looks Like in Practice

• Example (for a mid-size fast casual restaurant):
• Invests $50K in AI tools: scheduling, personalized app, demand forecasting
• Gains:
  • $12K/year saved in labor misallocation
  • $8K/year saved in food waste
  • $10K/year gained from better marketing conversion
• Total ROI over 2 years = ~$40K = 80%
  Payback period: 12–18 months

 

What Affects ROI Outcomes

• Data quality: AI is only as good as the data it's fed. Inaccurate POS or staffing data can limit value.
• Staff adoption: Tools that aren't embraced by managers or staff reduce impact.
• Incremental vs. disruptive deployment: Starting small and scaling up usually leads to better ROI than betting on a single large transformation.

Peter Romeo

What should food businesses consider in deciding whether to adopt AI?

 

ChatGPT

Food businesses should approach AI adoption strategically, weighing costs, capabilities, customer impact, and long-term goals. Here's a structured framework to help them decide:

Define the Business Problem Clearly: AI works best when it’s tied to a specific, measurable challenge — not as a vague “tech upgrade.” Before adopting AI, ask:

• Are you trying to reduce labor costs?
• Improve order accuracy?
• Cut food waste?
• Increase sales through personalization?

Assess Operational Readiness, key considerations include:

• Data quality: Do you have clean, consistent data from POS, scheduling, and inventory systems?
• Digital infrastructure: Are your software tools modern enough to integrate with AI systems?
• Staff training capacity: Can your team adopt and maintain AI tools, or will it require retraining?

Evaluate ROI Potential: Prioritize low-cost, high-ROI AI applications first (e.g., labor scheduling, digital menu recommendations). Ask:

• What is the expected payback period (e.g., 12–24 months)?
• Will this tool scale across multiple locations?
• Are there hidden costs (training, software maintenance, vendor lock-in)?

Understand Customer Expectations

• Will this improve the customer experience (e.g., faster service, better personalization)?
• Will it raise privacy concerns or reduce perceived hospitality?
• Can customers opt out or choose human interaction when they prefer?

Consider Ethical, Legal, and Brand Impacts

• Data privacy: Are you complying with laws (e.g., CCPA, GDPR) when collecting and using customer data?
• Labor ethics: Will this tech eliminate jobs, and how will that affect brand perception or community relations?
• Bias in algorithms: Is the AI equitable and inclusive in its recommendations or hiring decisions?

Start Small, Then Scale

• Pilot in one location or department.
• Measure impact.
• Iterate and expand only after proving results.

Adopting AI is not “all or nothing.” The most successful food businesses layer AI gradually into their workflows.