AI Helps Georgia Hotels Survive Rising Food Prices

Food inflation isn’t an abstract macroeconomic headline anymore. It’s a line item that’s squeezing every hotel breakfast buffet, every tour group lunch, and every restaurant menu in Georgia.

This week, Prime Minister Irakli Kobakhidze publicly called on law enforcement agencies and Parliament to probe what he described as excessively high grocery prices, raising the possibility of cartel-like coordination among retailers. He cited sharp differences between Georgia and European supermarkets—specific items allegedly priced far higher in Georgia (for example, pasta nearly double, rice far higher, chocolate up by almost half). He also claimed average markups of 86% from border to shelf, and pointed to unusually high net margins in parts of the retail chain.

If you’re running a hotel, a guesthouse, a restaurant, or a tour operation, here’s why this matters: you can’t wait for policy to fix your cost base. You need operational tools that react faster than inflation. That’s where practical AI comes in—not as a buzzword, but as a set of systems that help you forecast demand, control purchasing, reduce waste, and protect margins without degrading guest experience.

This post is part of our series „როგორ ცვლის ხელოვნური ინტელექტი ტურიზმსა და სასტუმრო ბიზნესს საქართველოში“—and the core theme today is simple: when input prices become unstable, the winners are the businesses that measure reality daily and adjust automatically.

What high grocery prices really mean for tourism businesses

Answer first: High grocery prices hit tourism twice—directly through higher F&B costs and indirectly through weaker local spending and changing traveler behavior.

Hotels and tour operators often treat groceries as “just kitchen costs.” That’s a mistake. In Georgia, food and non-alcoholic beverages have been a major inflation driver; official data referenced in the news put annual inflation at 4.8% in November, with food and non-alcoholic beverages up 10.3% year-on-year. That kind of sustained increase changes your economics quickly.

Here’s how it shows up on the ground:

• Breakfast becomes your hidden profit leak. A “free breakfast” model is only free in marketing. In reality, it’s the most exposed bundle of eggs, dairy, bread, fruit, coffee, and spreads.
• Tour group catering gets harder to price. If you sell packages months in advance, you’re carrying food price risk.
• Menu engineering stops working if you don’t update it often. A menu designed around last quarter’s supplier prices can quietly destroy margins.
• Local travelers trade down. When household grocery bills rise, weekend stays and add-on restaurant spending can drop.

Policy investigations won’t solve your P&L this season

The government may investigate retail markups, entry fees, payment delays, or cartel behavior. That’s a public policy conversation. But operationally, your business still needs to answer three questions every week:

1. What will demand look like in the next 14–30 days?
2. What should we buy, and when?
3. Where is waste happening—and how do we stop it without making guests unhappy?

AI is good at exactly these questions because they’re data problems.

Where AI delivers the fastest cost control (without hurting guests)

Answer first: The quickest wins come from AI-assisted forecasting, purchasing, and waste reduction—not from flashy guest-facing tools.

When costs rise, many hospitality teams default to blunt tools: shrink portions, raise prices, or cut variety. Guests notice. Reviews follow.

A better approach is to reduce variability and waste. In most hotels I’ve worked with, the biggest preventable losses come from mismatch: buying too much, prepping too early, and not seeing what actually sells.

AI demand forecasting for occupancy + covers

Even simple machine learning models outperform “gut feel” because they can weigh multiple signals at once:

• historical occupancy and pickup pace
• day-of-week seasonality (especially critical in Tbilisi vs. resort areas)
• holidays and school breaks (December/January is peak volatility)
• events and flight patterns
• booking channel mix (OTA vs direct often predicts F&B spend)

Practical output: a daily forecast of breakfast covers, restaurant covers, and tour group meals.

Once you forecast covers better, you buy better. And once you buy better, you waste less.

AI-assisted purchasing: smarter than “cheapest supplier”

Rising prices often come with chaotic availability. Teams react by chasing discounts, switching brands, and adding ad-hoc purchases.

AI can help by optimizing for a realistic basket of constraints:

• contracted supplier prices vs spot prices
• delivery days and minimum order quantities
• shelf life and spoilage risk
• substitutable items (e.g., butter vs spreads, seasonal fruit swaps)

A strong rule: Don’t optimize each ingredient in isolation. Optimize the menu and procurement together.

Waste detection from POS + kitchen logs

You don’t need fancy sensors to start. If you have:

• POS data
• inventory counts (even weekly)
• production sheets (prep quantities)

…you can train simple anomaly detection to flag problems like:

• “We prepped 30% more salads than sold every Tuesday.”
• “This cheese SKU consistently expires before use.”
• “We’re over-portioning one dish on one shift.”

Result: fewer emergency purchases, fewer thrown-away items, steadier quality.

Pricing during inflation: AI is your guardrail, not your excuse

Answer first: AI helps you raise prices less often—but more correctly—by tying price changes to demand and margin realities.

Hospitality pricing gets emotional in inflationary periods. Owners fear losing guests. Managers fear negative reviews. Chefs fear quality decline. Everyone is right, and that’s the problem.

AI brings discipline.

Use “margin bands,” not one perfect price

Set acceptable ranges instead of a single target:

• target gross margin by category (breakfast, à la carte, minibar)
• minimum contribution margin for packaged tours
• max discount allowed for low-demand days

AI can recommend price adjustments when you drift outside bands.

Dynamic packaging beats constant menu price changes

Guests tolerate package changes better than constant menu edits.

Examples:

• keep breakfast included, but switch to two-tier breakfast (standard vs premium) where premium absorbs high-cost items (salmon, specialty cheeses)
• offer early-book tour packages with a “supplier cost adjustment” clause for group meals (transparent, not sneaky)
• push direct booking perks that cost you less than food (late checkout, room upgrade) instead of F&B-heavy perks

AI helps identify which perk drives conversion for each segment.

Lessons from the grocery debate: transparency and competition matter

Answer first: Whether or not there’s cartel coordination, the tourism sector should assume price volatility stays—and build systems that make costs visible daily.

Kobakhidze’s statements highlighted several structural concerns: high shelf markups, alleged “chain cashback” entry fees, payment delays, and a rapidly expanding supermarket landscape. Critics argue the proposed enforcement approach risks becoming a crackdown rather than a market fix.

Here’s my stance: tourism businesses shouldn’t bet their survival on a political outcome. Investigations may reduce prices, or they may create uncertainty. Either way, operators need internal transparency.

The operational equivalent of “anti-monopoly” is multi-sourcing

Hotels and restaurants get trapped when they rely on one distributor “because it’s easier.” In inflation, that convenience becomes expensive.

AI-supported procurement makes multi-sourcing feasible by:

• comparing real delivered cost (including shrink/spoilage)
• tracking supplier performance (fill rate, substitutions, delays)
• recommending reorder points by shelf life

Multi-sourcing doesn’t mean chaos. It means resilience.

If Georgian producers prefer exporting, hotels can become stable buyers

The news noted a painful dynamic: suppliers may find it more profitable to export than sell into retail chains.

Hotels can counter this by forming predictable demand (weekly standing orders, seasonal menus built around local supply) and using AI forecasting to keep commitments realistic. That’s good for margins and good for Georgia’s tourism story: authentic local food with stable quality.

A practical 30-day AI plan for hotels and tour operators in Georgia

Answer first: Start small: forecast demand, connect it to purchasing, and measure waste weekly. You’ll see impact within one month.

If you’re thinking, “We’re not a big chain, we don’t have a data team,” that’s fine. You don’t need one to start.

Week 1: Get your data into one place

Minimum dataset:

• daily occupancy (past 12 months if possible)
• POS sales by item and time
• purchase invoices by supplier
• a basic inventory count (top 30 SKUs)

Goal: one spreadsheet or simple dashboard you trust.

Week 2: Build a covers forecast and tie it to prep

Use a basic model (even a regression or time-series forecast) to predict:

• breakfast covers
• restaurant covers by daypart

Then set prep rules: “prep = forecast + safety buffer.” Your buffer should shrink as accuracy improves.

Week 3: Optimize purchasing for shelf life and substitution

Create substitution maps:

• dairy substitutions
• seasonal produce swaps
• meat/fish alternates for specials

AI can suggest swaps when a SKU price spikes or availability drops.

Week 4: Run a waste audit with AI flags

Track:

• items thrown away (value, not just volume)
• expired SKUs
• dishes with low sales but high prep

One strong habit: hold a 30-minute weekly “cost stand-up” with kitchen + purchasing + GM.

A tourism business that reviews costs weekly will outperform one that reviews costs monthly—especially during food inflation.

What to do next (and how this fits our series)

Grocery prices are now a national political issue, and the numbers being discussed—large price gaps, high markups, and food inflation above overall inflation—mirror what hospitality operators feel every day. The tourism sector in Georgia can’t control retail structures, but it can control how fast it detects change and how intelligently it responds.

In our „როგორ ცვლის ხელოვნური ინტელექტი ტურიზმსა და სასტუმრო ბიზნესს საქართველოში“ series, we usually talk about AI for guest communication and bookings. This time, the priority is less glamorous: profit protection through operational clarity.

If you want leads that turn into real savings, start with a quick diagnostic: share your last 90 days of occupancy, POS, and top supplier invoices, and we’ll map where AI forecasting and purchasing optimization can cut waste and stabilize margins—without guests noticing any “cost-cutting” at all.

Where do you feel inflation most right now—breakfast, restaurant menus, or group catering?