Summary
AI is changing how buyers search, compare, and purchase. But the bigger question is what pricing looks like when these systems become part of everyday life. In this episode, Michael and Avy look 10 to 15 years ahead, exploring shopper bots, fewer promotions, hidden B2B prices, brand value, scarcity, and the uneven effect of AI…
In this episode, Michael and Avy look 10 to 15 years ahead, exploring shopper bots, fewer promotions, hidden B2B prices, brand value, scarcity, and the uneven effect of AI on costs.
Key takeaways
- Shopper bots could make price comparison constant and automatic.
- Promotions may lose impact as buyers wait for the right price.
- Brand, quality, reliability, and scarcity may become stronger pricing levers.
- B2B companies may make pricing harder to access and compare.
- AI can create more value through speed while also putting pressure on price.
- High-value AI applications may become more expensive, while routine work becomes cheaper.
When the shopper bot becomes the buyer
The most important change may be the rise of the personal shopper bot. A consumer could tell it what they need daily, weekly, or occasionally. The bot would then monitor retailers, compare prices, check delivery times, and purchase automatically when the right conditions were met.
That would turn shopping into a continuous pricing exercise. Buyers would not need to wait for a sale email or visit several websites. Their AI would already know when toilet paper is running low, which retailer has the best offer, and whether the delivery time works.
For pricing teams, that creates a major shift. If a large share of customers knows that a product goes on promotion every few weeks, they may simply wait. A business could move from selling most units at regular price to selling most units on promotion.
That is not a sustainable model. Companies would be giving away margin to customers who were prepared to pay more.
Are promotions becoming less useful?
This raises a tough question: Do promotions still matter when AI can predict them?
In a bot-driven market, traditional promotional spending could lose much of its effect. Instead of responding to a discount at the moment of purchase, the customer’s bot may plan around it. The promotion becomes expected, not exciting.
Some businesses may move closer to an Everyday Low Price model. A steady, competitive price could be easier for bots to evaluate and harder for shoppers to game. Others may need to rethink their product and pack architecture, offering different sizes, formats, or product versions by channel.
The right answer will depend on the category, customer behavior, and economics. This is where a customized pricing approach matters. Revenue Management Labs works with companies to understand which margin levers are actually available, rather than assuming the same promotion strategy works across every retailer, market, or product line.
Brand and scarcity may carry more weight
If price becomes easy for AI to compare, the harder question is how buyers evaluate everything beyond price.
A low-cost product is not always the best choice. A supplier that cannot deliver on time can shut down a production line. A cheaper service may create more errors, rework, or risk. Quality, support, reliability, and expertise can all create value that does not appear in a simple price comparison.
Companies will need to communicate those value drivers in ways AI systems can recognize. It will not be enough to say that a product is premium. Businesses will need clear evidence of what makes it better and why the price gap is justified.
Scarcity may also become a stronger lever. Luxury brands already use limited production and controlled availability to protect perceived value. More companies may use limited editions, restricted supply, or exclusive access to create demand that cannot be reduced to a lowest-price search.
This is not a replacement for sound pricing strategy. Scarcity only works when customers believe it is real and the underlying offer has value.
B2B pricing could become even more opaque
B2B prices are already difficult to compare. Buyers often need to complete an RFP, speak with a sales team, or request a custom scope before they see a number.
AI could make that situation more extreme. If companies publish prices openly, bots may scrape, compare, and rank them instantly. That can make it easier for buyers to negotiate and harder for suppliers to protect differences in value.
We may see more companies qualify buyers before sharing pricing. Software providers, professional services firms, and manufacturers could make pricing dependent on factors such as:
- The buyer’s needs and use case.
- The expected scope and volume.
- Required service levels.
- Implementation complexity.
- The value created for the customer.
The downside is reduced market visibility. If every price is custom and hidden, companies may struggle to understand their position relative to competitors. Pricing leaders will need stronger internal data, market insight, and governance to make decisions with confidence.
AI may create both value and price pressure
AI can deliver work much faster. A process that once took one or two days may be completed in minutes. That creates clear value for the customer.
But customers also know that AI is doing the work. If the system is less accurate than a trained person, they may expect a lower price. The provider is caught between two forces: faster delivery supports a higher price, while lower labor requirements create pressure to pass savings along.
In many routine services, competition may push prices down quickly. It may only take one provider to use AI aggressively, cut prices, and capture volume.
At the same time, high-value AI applications could become more expensive. A software engineer who uses substantial AI capacity may deliver far more output, but that productivity comes with additional consumption costs. The real cost of the role may include both the person and the AI resources supporting them.
This points to a clear bifurcation:
| Type of work | Likely pricing effect |
|---|---|
| Routine, repeatable tasks | Prices may fall as delivery becomes easier to automate |
| High-value, specialized work | Prices may rise as AI increases capability and demand |
| Work requiring judgment and accountability | Value will depend on accuracy, trust, and expertise |
What pricing leaders should do now
The future is still uncertain, but the questions are practical today. Leaders should begin by testing how AI could change customer behavior, promotion response, competitive visibility, and cost-to-serve.
They should also strengthen the basics: segment customers, quantify value drivers, track realized prices, and understand which discounts truly change demand. AI can help analyze patterns faster, but it should support—not replace—pricing judgment.
Revenue Management Labs combines embedded AI with hands-on pricing expertise to help organizations turn those insights into workable strategies. The goal is not to guess what pricing will look like in 2040. It is to build the data, capabilities, and execution discipline needed to adapt as the market changes.
The companies that perform best may not be the ones with the lowest price. They will be the ones that clearly understand their value, know which customers they serve, and can make pricing changes stick.
