How Dynamic AI Pricing Could Eliminate Food Waste in Stores

Walk into a typical grocery store at closing time and you’ll usually find the same sad scene:
perfectly good yogurt with tomorrow’s date, bruised-but-edible apples, and salad kits that look
fresher than your Sunday mood – all headed for the trash. Meanwhile, shoppers are complaining
about high prices and the planet is quietly overheating under a blanket of methane from rotting
food in landfills. Fun times.

Globally, roughly one-third of all food produced for humans is lost or wasted. Supermarkets are a
major pinch point in that chain, and cutting retail food waste in half by 2030 is actually
embedded in the United Nations’ Sustainable Development Goals.
That’s not just a nice-to-have – it’s a climate and food security priority.

Enter dynamic AI pricing: a mix of data science, electronic shelf labels, and a surprisingly
simple idea – lower the price of perishable food gradually and intelligently before it
expires, not in a frantic sticker party at 9:45 p.m. If we get it right, this technology could
make a serious dent in food waste, improve grocery profits, and help more people afford fresh
food. Not bad for a few lines of code and some smart tags.

Why So Much Food Gets Wasted in Stores

The overstock problem

Grocery retailers live in fear of empty shelves. If you show up and can’t find your favorite
Greek yogurt or chicken thighs, you might switch stores. So managers routinely over-order to
avoid lost sales. The result: mountains of perishable inventory with fixed shelf lives, sitting
under fluorescent lights, slowly ticking toward “unsellable.”

Traditional forecasting methods – spreadsheets, rough rules of thumb, and last year’s sales
curves – don’t react well when the weather is weird, a big game is on, or a TikTok trend makes
everyone crave feta and tomatoes at once. That mismatch between demand and supply is a huge
driver of food waste at retail.

The “yellow sticker” strategy is too little, too late

Many chains already try to cut waste with markdown stickers on soon-to-expire items. The problem
is that those markdowns tend to be:

• Manual: Someone has to walk the aisle with a scanner and a roll of stickers.
• Infrequent: Prices might change once a day – if that – even though demand
  changes constantly.
• Panic-driven: Discounts are often steep and last minute, so staff slash
  prices when products are almost unsellable.

Studies have shown that better-timed markdowns can significantly increase sell-through and lower
waste, but retailers struggle to decide when to drop the price and by how much.
That’s exactly where AI-powered dynamic pricing shines.

What Is Dynamic AI Pricing, Exactly?

Dynamic pricing is just “prices that move.” Airlines and ride-hailing apps have used it for
years. But in grocery stores, the goal is different. Instead of jacking up prices during a
snowstorm, AI-driven grocery pricing focuses on:

• Selling perishable food before it expires
• Protecting margins by making smaller, smarter discounts earlier
• Keeping prices fair and transparent for shoppers

In practice, dynamic AI pricing for food waste combines:

• Electronic shelf labels (ESLs): Digital tags that can change prices in
  seconds across thousands of SKUs, eliminating the need for manual stickers.
• Algorithms that “understand” time: The system knows each item’s remaining
  shelf life and how quickly it’s selling.
• Real-time data feeds: Sales, inventory, promotions, even weather and local
  events, all feed into a model that predicts demand.
• Automatic price adjustments: The AI nudges prices down (or occasionally up)
  multiple times a day to keep product moving and profits healthy.

Companies like Wasteless have built AI systems that calculate “the perfect price at this exact
moment” based on thousands of data points: expiry dates, historical sales, store location,
competition, and more.

How AI Pricing Actually Cuts Food Waste

Linking price to remaining shelf life

One of the simplest but most powerful ideas is to connect price directly to shelf life. A yogurt
with five days left might be only slightly discounted, while one that expires tomorrow gets a
deeper markdown. Dynamic pricing platforms can adjust these discounts gradually throughout the
day – sometimes dozens of times – so stock sells steadily rather than in a last-minute fire sale.

Some pilots reported waste reductions of about 30–33% and revenue increases of around 6% on
products that would otherwise be discarded.
In other trials, grocers using AI-based markdowns on meat, poultry, and fish saw food waste cut
by up to 80% while keeping margins intact.

Matching discounts to real demand

Instead of “everything 50% off after 7 p.m.,” AI can tailor markdowns based on:

• Day of the week and time of day
• Weather (grill packs sell better on sunny weekends)
• Local events (game nights, holidays, concerts)
• How similar items performed in the past

Research on dynamic pricing for grocery waste shows that well-designed strategies can reduce
waste by about 21% on average while increasing gross margins by roughly 3%.
In other words, retailers don’t have to choose between “doing the right thing” and making money.

Combining pricing with better ordering

Price optimization is even more powerful when paired with AI demand forecasting. Tools like
Shelf Engine and Afresh help retailers order the right amount of stock in the first place,
leading to an average 14.8% reduction in food waste from improved ordering alone.

Add dynamic pricing on top of smarter ordering and you get a compounding effect: fewer
overstocked items to begin with, plus better clearance of what does end up close to its
use-by date.

Who Wins With Dynamic AI Pricing?

Shoppers

For customers, AI-driven markdowns can turn grocery shopping into a mini treasure hunt:

• Fresh food that’s 20–70% cheaper because it needs to be eaten soon
• Clear digital labels that show both the price and the use-by date
• More predictable markdown patterns (no need to guess when “reduced” stickers appear)

Research on electronic shelf labels in U.S. and European stores suggests that dynamic discounts
can especially benefit budget-conscious and low-income shoppers by making fresh produce, meat,
and dairy more accessible – while simultaneously reducing food sent to landfills.

Retailers

For grocers, the business case is compelling:

• Waste reductions of 20–40% or more on perishable categories
• Sales lifts of around 6–10% on discounted items as shoppers pick them first
• Higher recovered revenue from food that would otherwise be thrown away
• Lower labor costs because staff no longer spend hours stickering products

AI-powered dynamic pricing also gives retailers sharper control over margin. Instead of one
blunt 50% markdown, the system can test, learn, and settle on discounts that preserve the most
profit while still moving product.

The planet

Food waste isn’t just sad; it’s a climate problem. When organic waste rots in landfills, it
generates methane, a greenhouse gas far more potent than CO₂ in the short term. Cutting retail
food waste – even by 20–30% – can avoid significant emissions and support national climate
targets.

Concerns: Is This Just “Surge Pricing” in Disguise?

The phrase “dynamic pricing” understandably makes people nervous. Nobody wants their milk going
up in price mid-aisle because a rainstorm hit or a football game is on. Some lawmakers in the
U.S. have already raised concerns about electronic shelf labels enabling real-time price spikes,
especially during emergencies.

The good news: the systems being tested in food waste pilots are generally designed around
markdowns, not surge pricing. European retailers like Albert Heijn use digital tags
primarily to discount products that are nearing expiration, not to raise prices when traffic
spikes.

To build trust, retailers adopting AI pricing should:

• Commit publicly that dynamic pricing will not be used for sudden price hikes on essentials
• Show clear before/after prices and expiry dates on digital labels
• Explain in-store and online how markdowns help reduce waste and support affordability
• Offer opt-in notifications or apps that alert shoppers to fresh markdowns

Transparency is key. If customers see that pricing is being used to make food cheaper
and keep it out of the trash, resistance tends to fade quickly.

What Stores Need to Make Dynamic AI Pricing Work

1. Clean, connected data

AI is only as good as the data it’s fed. Grocers need accurate inventory counts, reliable
product master data (including expiry dates), and transaction logs that the models can learn
from. Many retailers are already investing in this foundation for other projects like demand
forecasting and loyalty analytics.

2. Electronic shelf labels and integration

Dynamic pricing without digital labels is like trying to stream 4K video over dial-up – possible
in theory, painful in practice. ESLs allow prices to change quickly and consistently, while
integrations to POS and back-office systems ensure that what’s on the shelf matches what’s in
the checkout system.

3. A clear playbook and KPIs

Retailers that succeed with AI pricing usually:

• Start with a limited set of categories (like meat, dairy, or salads)
• Define success in terms of waste reduction, margin, and customer satisfaction
• Run controlled pilots in a group of stores and compare to a similar control group
• Iterate on rules and guardrails based on results

Some pilots have reported up to 40% reductions in waste and sizable profit improvements in just
a few months, giving retailers confidence to scale.

4. Partnerships and ecosystems

Few grocers want to build all this in-house. The current landscape includes AI startups focusing
on dynamic pricing, companies that provide ESL hardware and software, demand-forecasting
platforms, and even apps that help move surplus or near-expiry items to consumers at steep
discounts.

The most effective approach is often an ecosystem: use one partner for ordering optimization,
another for in-store pricing, and a third to clear remaining surplus through “too good to waste”
deals or surprise bags.

Experiences and Lessons From the Aisles

So what does dynamic AI pricing look like in real life, beyond slick dashboards and cheerful
pilot reports? Let’s walk through a few realistic scenarios inspired by existing pilots and
programs around the world.

Scenario 1: The meat department makeover

Imagine a regional grocery chain where the meat department has always been a waste hotspot.
Steaks, ground beef, and marinated chicken routinely hit their sell-by dates and head straight
to the dumpster. Staff try to slap on “Reduced” stickers at the end of the day, but by then the
case looks picked over and customers are already home making dinner.

After rolling out AI pricing and ESLs, the store manager notices a subtle change in the case.
Instead of one generic price, each pack shows a slightly different price based on date and
demand. Ground beef that expires in four days might be at 5% off, while packs with two days left
are at 25% off. Products quietly repriced three or four times throughout the day – without
anyone printing labels.

Shoppers start learning the pattern: if you plan to cook tonight, you can score a good deal by
choosing the item with the closer date. The manager sees meat waste drop by a third, while meat
sales actually rise because customers feel they’re getting value. The biggest surprise? Staff
love it because they spend far less time stickering and rescanning items at the register.

Scenario 2: Produce that doesn’t quietly disappear

In produce, things are messier – literally. Apples bruise, greens wilt, and bananas ripen all at
once. An AI system using IoT data and historical sales patterns sets up a four-stage pricing
curve: full price at the start of the week, slight markdown midweek, deeper discount as the
weekend approaches, and a final push on the last day.

On a hot Saturday, the model detects faster-than-usual demand for melons and nudges markdowns
down a bit – they’re selling fine. But on a cold, rainy Tuesday, lettuce and berries that are a
few days from expiry get a sharper discount to attract attention. Over time, the store cuts
produce waste by around 20%, improves margins, and gets fewer complaints about “tired-looking”
displays because stock actually moves.

Scenario 3: Customers on a budget become loyal fans

A different chain leans into communication. In-store posters explain that the new digital labels
are part of a plan to “Save More, Waste Less.” The store app adds a “Last Chance Deals” tab,
showing discounted items by department. Families on tight budgets quickly figure out how to plan
meals around those markdowns.

Instead of feeling like guinea pigs in some high-tech experiment, shoppers see themselves as
partners: they rescue food that would otherwise be wasted, save money, and feel good about it.
Research suggests that when dynamic pricing is framed as a fair trade-off – slightly more
complex prices in exchange for more discounts and less waste – most customers accept it, and
many actually prefer it.

Scenario 4: What happens behind the scenes

Behind the scenes, the data science team is constantly tuning the algorithms. They monitor
metrics like:

• Waste rate by category
• Average markdown depth
• Sell-through by shelf-life “bucket”
• Customer complaints or confusion at the shelf

When they see discounts that are too steep, too often, they re-adjust the curves. When waste
creeps up in a category, they examine whether stocking rules, planograms, or even packaging need
changes. Dynamic AI pricing becomes less of a “set-and-forget” tool and more of an ongoing
conversation between the model, the merchants, and the frontline teams.

The main lesson from these real-world experiences: the technology works best when it’s
implemented with clear guardrails, good communication, and a human willingness to learn. It’s
not magic, but it’s a powerful lever – especially when combined with smarter ordering and
creative programs like discounted “surprise bags” for surplus items.

The Bottom Line

Dynamic AI pricing won’t single-handedly solve the global food waste crisis, but it can
dramatically reduce how much perfectly good food gets thrown away in supermarkets. The evidence
so far is clear: done responsibly, it cuts waste by double-digit percentages, protects margins,
and makes fresh food more affordable for the people who need it most.

The opportunity is right there on the shelf. Every carton of eggs with a ticking date, every
pack of chicken cutlets nearing its sell-by, every salad kit inching toward wilting is a small
pricing puzzle. AI gives retailers the ability to solve millions of those puzzles every day –
and turn what used to be waste into dinner.