Walmart vs. McDonald’s: What Their Different Approaches to AI Pricing Teach Small Businesses

Walmart and McDonald’s illustrate different approaches to AI-powered pricing. Discover what their strategies reveal about customer trust, local demand, profitability, and responsible data use.

Key takeaways

• Walmart’s public pricing position demonstrates the strategic importance of customer trust.
• McDonald’s interest in local pricing analysis illustrates how market conditions can influence business decisions.
• AI pricing should focus on sustainable profitability rather than extracting maximum revenue from individual customers.
• More consumer data does not automatically produce better or more responsible decisions.
• Businesses should measure the actual effects of price changes rather than relying on correlations.
• Human judgment remains important even when sophisticated pricing algorithms are available.

Imagine walking into a store and discovering that the person standing next to you is being charged less for the same product. Not because they have a coupon, belong to a loyalty program, or qualify for a special promotion, but because an algorithm has determined that you’re willing to pay more.

Most shoppers would probably find that frustrating. Yet artificial intelligence is making it increasingly possible for companies to estimate what individual customers are willing to spend.

For large retailers and restaurant chains, that creates an interesting business dilemma. AI can help identify opportunities to improve pricing and profitability, but using too much customer information could undermine the trust companies have spent decades building.

Two of America’s most recognizable companies, Walmart and McDonald’s, illustrate different ways of thinking about the issue.

Walmart has publicly emphasized its commitment to pricing products rather than individuals. McDonald’s, meanwhile, has been exploring AI-supported pricing recommendations that account for transaction patterns and local market conditions.

These approaches are not necessarily opposites. Localized pricing recommendations are different from charging individual customers based on their personal profiles. Nevertheless, the contrast highlights an important strategic question: How much pricing power should businesses give to algorithms?

James Sun, CEO and Co-Founder of Kapnova, an AI company focused on business decision-making and profit optimization, believes the answer requires more than sophisticated technology.

In a written interview, Sun explained why companies need to balance pricing efficiency with customer relationships, and why maximizing revenue from individual transactions may not be the best long-term strategy.

Lesson 1: Walmart Understands That Customer Trust Has Economic Value

Walmart built much of its reputation around helping customers save money. Its everyday low-price positioning has become an important part of how shoppers understand the brand.

That reputation makes pricing predictability particularly valuable.

If customers believe Walmart offers competitive prices without secretly calculating how much each shopper can afford, that expectation supports the company’s broader value proposition.

Walmart’s stated approach of pricing the product rather than the person reflects this concern.

The business lesson goes beyond retail. A company can invest millions in advertising to build trust, only to damage that trust through pricing practices customers consider unfair.

Sun believes businesses should consider how a pricing decision affects their reputation before implementing it.

“My rule: If you can’t comfortably explain your pricing logic to customers, reconsider it,” he says.

This does not mean that businesses must charge the same prices in every market or avoid promotions. Seasonal discounts, regional price differences, and loyalty rewards can all have understandable business explanations.

The distinction is whether customers can reasonably understand why a price differs.

For smaller businesses, Walmart’s approach suggests that transparent pricing can itself be a competitive advantage.

An independent retailer may not be able to match Walmart’s purchasing power or distribution network. It can, however, establish straightforward pricing policies that customers understand and trust.

Lesson 2: McDonald’s Shows Why Local Market Conditions Matter

McDonald’s operates in markets with different labor costs, competitive conditions, consumer preferences, and demand levels.

A pricing strategy that works in one location may not produce the same results elsewhere.

This is where AI-supported analysis can help. By examining transaction data and local demand patterns, a business can make more informed pricing recommendations across markets.

For example, a restaurant in a busy commercial district may face different operating costs and demand patterns than one in a suburban neighborhood. A national chain can factor those differences into its pricing strategy.

Importantly, this is not necessarily personalized pricing.

Localized pricing involves differences between markets or locations. Personalized pricing involves differences based on information about individual customers.

Sun explains that distinction clearly.

“Localized pricing adjusts for different markets. Personalized pricing means two people might pay different prices based on what a company knows about them.”

For smaller businesses, the lesson is to consider local economic conditions rather than copying competitors’ prices without understanding the underlying market.

A neighborhood bakery, for example, might discover that certain products sell particularly well on weekends. A service company could find that demand differs substantially between the areas it serves.

Those insights can inform pricing, promotions, staffing, and inventory decisions without requiring the business to collect sensitive personal information.

Lesson 3: The Highest Possible Price Is Not Always the Best Price

Large corporations invest heavily in understanding how pricing affects profitability. Smaller businesses can benefit from the same underlying principle, even without access to enterprise-level analytics.

Sun offers an illustrative example involving a beauty product that costs $12 to produce and sells for $30.

At 10,000 units, the business generates $180,000 in gross profit.

If the company increases its price to $33 and sales decline by only 5%, gross profit rises to $199,500.

However, if the same increase causes sales volume to decline by 20%, gross profit falls to $168,000.

The price increase itself is neither good nor bad. Its value depends on how customers respond.

This is an important distinction because businesses often focus on individual metrics without considering the full economic picture.

A higher average transaction value might look encouraging, but not if the company loses enough customers to reduce total profit.

Sun argues that businesses need to understand the actual financial consequences of their decisions.

The principle applies to companies of every size. A large retailer may evaluate millions of transactions, while a neighborhood store might analyze a few months of sales. Both are trying to answer the same question: Which pricing decision produces the best sustainable business result?

Lesson 4: More Customer Data Isn’t Always Better

One reason AI pricing has attracted attention is that companies can now analyze far more information about consumers than they could in the past.

Shopping history, browsing activity, location information, and other behavioral signals may help predict what customers will purchase and how much they might pay.

However, the ability to collect and analyze information does not automatically justify using it.

The Federal Trade Commission’s investigation into surveillance pricing has examined how businesses and pricing intermediaries use detailed consumer information to influence prices and offers. Federal Trade Commission

In January 2025, the agency reported that certain pricing intermediaries could use information such as location, browsing behavior, and shopping activity to tailor commercial offers. Federal Trade Commission

These capabilities create a difficult question for business leaders: When does understanding customers become exploiting them?

Sun recommends drawing a clear boundary around sensitive information.

Businesses can reasonably analyze historical sales, product costs, competitor prices, inventory, and overall demand. He cautions against using someone’s health, financial circumstances, or vulnerabilities to charge that individual more.

He illustrates the difference with a skincare product.

If an algorithm determines that increasing the general selling price from $40 to $43 could improve profitability, the business has identified a potential pricing opportunity.

But if the algorithm decides that one particular customer should pay $55 because of personal circumstances, the ethical implications change considerably.

For smaller companies, this is a useful reminder that collecting more customer data is not automatically a sign of business sophistication.

Sometimes the smarter strategy is to use less information and make better decisions with it.

Lesson 5: Successful Companies Look Beyond Short-Term Revenue

One of the dangers of automated pricing is that an algorithm may optimize for a narrow objective while ignoring other factors that contribute to business success.

If a company instructs its system to maximize revenue per transaction, the system may recommend higher prices or frequent adjustments.

Those recommendations could improve immediate financial performance while creating problems that appear only later.

Customers may purchase less frequently, switch to competing brands, or become frustrated by unpredictable prices.

Sun has observed similar challenges while working with consumer brands, particularly in the beauty industry, where repeat purchases and loyalty are important.

A business might report stronger quarterly revenue after increasing prices, yet experience declining customer retention over the following months.

That is why Sun recommends evaluating gross profit, acquisition costs, repeat purchases, and customer retention alongside revenue.

The underlying lesson is familiar to successful companies: Customer relationships have long-term economic value.

Large brands invest heavily in loyalty programs, customer experience, and brand recognition because repeat business matters. Pricing decisions should support those investments rather than undermine them.

Lesson 6: Good Business Decisions Require More Than Correlation

Large organizations have access to extensive analytics, but having more data does not guarantee that managers will interpret it correctly.

Sun identifies one particularly costly mistake: Assuming that because sales increased after a promotion, the promotion caused the increase.

Imagine a retailer launches a 20% discount and reports a 30% increase in sales.

Management might celebrate the promotion and plan to repeat it. However, if seasonal demand and advertising would have increased sales by 25% anyway, the discount produced far less additional demand than the headline figure suggests.

The company may have given up significant profit margin unnecessarily.

This is why Sun emphasizes causal inference, which attempts to identify whether a particular business action actually produced an observed result.

His interest in this problem developed partly from his experience with influencer marketing.

He recalls spending heavily on campaigns that generated engagement and activity without necessarily demonstrating additional revenue attributable to those investments.

That experience eventually led him to study causal inference at MIT and build Kapnova around the relationship between AI, economic models, and measurable business outcomes.

For smaller businesses, the lesson is straightforward: Don’t judge a promotion solely by what happened afterward. Try to determine what would likely have happened without it.

Lesson 7: The Best Companies Know When Not to Change Prices

Businesses are often encouraged to optimize everything. If technology can improve prices, marketing, inventory, and operations, why not allow it to make continuous adjustments?

Because customers value stability, too.

Frequent price changes may create uncertainty, particularly for businesses whose customers make regular purchases or maintain long-term relationships.

A customer who feels confident about what a product will cost next month may be more valuable than someone who makes one purchase at a higher price.

Sun believes AI systems should incorporate business constraints and human judgment rather than focusing exclusively on mathematical optimization.

“Sometimes the best recommendation is to leave prices alone,” he says.

That perspective is particularly relevant for entrepreneurs who admire the efficiency of large corporations but cannot afford to lose customers through poorly considered experiments.

The goal is not to imitate every technology investment made by Walmart, McDonald’s, or other major companies. It is to understand the business principles behind those investments and apply them appropriately.

What Small Businesses Can Learn From Walmart and McDonald’s

The two companies demonstrate that AI pricing does not have to follow a single model.

Walmart’s public emphasis on consistent, understandable pricing reflects the importance of customer confidence. McDonald’s interest in AI-supported local pricing analysis illustrates the potential value of understanding demand and market conditions.

Both approaches offer useful lessons, but neither removes the need for human decision-making.

For smaller businesses, the practical strategy is to combine the strongest elements of both: use data to understand costs and demand, maintain clear rules around customer information, and evaluate pricing changes based on their effects on long-term profitability.

The biggest lesson may be that better technology does not automatically produce better business decisions.

As Sun explains, AI should help companies make decisions that create measurable value, not simply encourage more changes.

For entrepreneurs learning from major corporations, that principle is worth adopting long before investing in sophisticated AI systems.

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