Pricing is one of the most powerful but often underestimated levers for increasing profits and company value. Even small changes can have a big impact: a price increase of just 1% can boost operating profit by up to 11% with the same sales volume. In contrast, a price reduction of 5 percent requires a rather unrealistic sales increase of around 19 percent if profits are not to plummet. Artificial intelligence (AI) is now putting pricing back on the agenda for small and medium-sized businesses. In this interview, enomyc experts Jan Ulrik Holsten and Franz A. Wenzel explain how AI is changing pricing and what options this opens up for companies.
As we know, there are many different answers to the question of how high or low the price of a product or service should be set. What strategies do companies use today to set their prices?
Jan Holsten: The most common approaches include cost-based, competition-oriented, and value-oriented pricing. In the cost-based strategy, also known as “cost-plus,” a markup is added to the production costs. This is very simple and safe, but it does not take into account the market or customer value. Competition-oriented pricing is based on the prices of competitors. This is particularly suitable in commoditized markets, but it is risky because price wars can squeeze margins. The most demanding, but also the most effective, is the value-based strategy: here, the price is based on the value that the product creates for the customer. This is where the highest margins can be achieved – provided that the customer benefit is clearly communicated.
At least in B2C business, however, there are also prices that change constantly...
Jan Holsten: That's right. This is known as dynamic pricing. Prices are flexibly adjusted to demand, time, inventory, or other factors. This can be observed, for example, when flight or hotel prices fluctuate throughout the day. The key is to ensure that the chosen strategy fits the product, market, and company goals.
Speaking of flight or hotel prices: How sensitive are customers to fluctuating prices?
Jan Holsten: This is measured by price elasticity—another key concept in pricing. If demand is price elastic, even a small price increase will lead to a disproportionate drop in sales. If demand is inelastic, however, sales hardly change. Elasticity-based price optimization uses this principle to maximize profit. This means finding the optimal price at which the margin and volume effects balance each other out. It all sounds quite theoretical, but thanks to AI, it is now also feasible for small and medium-sized businesses. Studies show that AI-based pricing solutions can increase the EBITDA margin by 2 to 5 percentage points. Small and medium-sized businesses in particular should therefore think carefully about whether they want to miss out on this opportunity.
Can you give us a few examples to illustrate the specific benefits of AI in pricing?
Franz Wenzel: Gladly. It is important to differentiate between industries and business models. In industrial B2B business, companies often have hundreds of products and individual customer discounts. AI can learn from past transactions and recognize price-relevant patterns. For example, historical orders can be used to determine which customers are less price-sensitive. These customers can then be offered higher prices or lower discounts. AI algorithms also help to suggest the optimal price or discount range for each customer order. This approach is called AI-supported deal scoring. One tech company has increased its return on sales by 4 to 8 percentage points compared to conventional pricing. AI can also identify unnecessary discounts and quick wins – for example, when the company offers discounted additional services that the customer does not expect. Such margin leaks can be easily eliminated and have an immediate impact on profits. In short, AI enables B2B companies to calculate their offers much more precisely and manage their discounts much more intelligently.
More and more manufacturers are also selling their goods directly to end customers. What role can AI play here?
Franz Wenzel: Personalization is the key word in direct-to-consumer business. AI can be used here in a similar way to how it is used in retail. This means that AI models analyze customer data from the online store, segment buyers according to behavior and value, and enable personalized pricing on this basis. For example, price sensitivity can be determined for each segment: premium-oriented customers receive fewer discounts, while price-sensitive bargain hunters can take advantage of promotions more often. AI also helps with dynamic price adjustments in real time – for example, to respond to online demand, time of day, or competitor prices. Avoiding channel conflicts is also important for industrial D2C providers: AI can optimize channel-specific prices without cannibalizing partnerships in traditional sales – for example, by offering direct customers special bundles or services instead of simply lower prices. Overall, AI enables manufacturers to achieve higher margins in the end customer business without having to compromise on customer satisfaction, because prices are value-based and adjusted to individual customers.
Can you give us another example from traditional wholesale?
Franz Wenzel: In wholesale or B2B commerce, AI can be used effectively to optimize complex price lists and terms and conditions. Many medium-sized companies still work with flat-rate surcharges or discounts, or even with traditional Excel spreadsheets. AI can quickly remedy this situation: machine learning with sales and purchasing data can be used to determine optimal price metrics for each product and customer group. For example, AI could recognize that certain customers would be willing to pay significantly more for express delivery, while others are price-sensitive and react to small price changes. Accordingly, the system could suggest charging differentiated surcharges for services in the future. Discounts and bonuses can also be rebalanced using AI. The goal is to reduce margin-eating “discountitis” as much as possible. Such data-driven adjustments improve profitability in day-to-day business and at the same time relieve the sales team because less manual pricing is required.
Why do you think this is particularly relevant for small and medium-sized enterprises?
Franz Wenzel: Because small and medium-sized enterprises can use AI to optimally manage existing complexity despite limited resources. AI can transform the flood of pricing data into concrete recommendations. Those who get in early gain a head start – and studies show that AI pricing projects are generally more successful than other AI initiatives. So it's worth actively pursuing this lever.
The topic of artificial intelligence is very dynamic. How will this be reflected in pricing in the coming years?
Jan Holsten: AI will rapidly change pricing. We expect that generative AI (GenAI) and even more sophisticated algorithms in particular will continue to revolutionize pricing over the next two to three years. Experts agree that advanced pricing strategies will soon be almost unimaginable without AI support.
Overall, we see three trends that are likely to bring about lasting change in marketing and sales processes:
First, there is the “always on” trend. This means that prices will change at any time and be highly individualized because AI allows them to be adjusted in real time to even the smallest market changes. As a result, dynamic price adjustments will become ubiquitous in the future – whether online or in stores with electronic price tags. Algorithms can take into account not only classic factors such as demand or competition, but also unstructured data such as customer reviews, social media trends, or even sentiment indicators. This fusion of pricing and personalization will offer customers a tailor-made shopping experience, but it will also greatly increase the complexity behind the scenes.
What changes can be expected in the B2B sector?
Jan Holsten: A second relevant trend is the integration of AI pricing into sales processes: AI-supported negotiation assistants could emerge in B2B sales – essentially digital co-pilots for key account managers who make live suggestions during price negotiations about which concessions to offer and where to stand firm. Some companies are already using tools that tell the salesperson: “Customer X in industry Y has been charged an average of 5% higher prices; try a 3% surcharge in your next offer.” In the future, chatbots could even negotiate with customers – for example, for standard contract extensions or in e-commerce checkout – according to the motto: “If you buy now, I'll offer you a 5 percent discount.” This changes the role of sales because routine pricing decisions are automated, while people focus on complex cases. Marketing and sales must work even more closely with data science to achieve this.
Third, we assume that fairness and transparency will become increasingly important in all technology, because as AI becomes more prevalent in pricing, the call for price transparency and fairness will also become louder. Regulatory authorities are closely monitoring dynamic prices, especially in sensitive areas. Companies must therefore ensure that their AI pricing strategies remain fair to customers and explainable.
Franz Wenzel: Of course, AI pricing will also give rise to entirely new business models. Much of this is still in its infancy, but the next few years will see pilot projects and the first standard solutions emerge.
What does this mean for companies?
Franz Wenzel: For decision-makers, this means that waiting is not an option. Companies that hesitate now risk losing customers to more agile competitors in the near future – whether through smarter pricing, personalized offers, or faster response times.
Jan Holsten: I would say that those who invest in AI pricing now will take their marketing and sales to a new level and secure a sustainable competitive advantage. Or to put it more bluntly: pricing is becoming a game changer, bringing together topics such as AI, data strategy, customer experience, and profitability.
Dear Mr. Holsten, dear Mr. Wenzel: Thank you very much for talking to us.
