How AI is redefining pricing in B2B.

For decades, pricing in B2B companies has been a mix of data, intuition and negotiation. Spreadsheets and historical averages have guided price decisions, often supported by experience rather than prediction. But in recent years, Artificial Intelligence has quietly begun to transform the way pricing teams think, work and decide.

AI has moved beyond buzzword status. It now represents a real opportunity to make pricing smarter, faster and more consistent across complex markets.

The shift is not only about automation. It is about using machines to understand patterns that humans cannot see, and freeing people to focus on strategy and communication rather than number crunching. In essence, AI doesn’t replace the art of pricing – it refines it.

From descriptive to predictive pricing

Traditional pricing analytics tell us what has happened. AI tells us what will likely happen next. By analyzing millions of data points from transactions, bids, discounts and customer behavior, algorithms can identify patterns that predict willingness to pay and price sensitivity. Instead of adjusting prices reactively when margins erode, companies can now anticipate risk, optimize prices before deals are lost, and spot anomalies that indicate leakage.

This shift from descriptive to predictive pricing means moving from hindsight to foresight. It transforms pricing from a reporting function into a strategic capability. The most advanced companies now use AI to simulate how different pricing strategies affect both margin and volume before they even hit the market. This ability to test and learn in a virtual environment shortens decision cycles and increases confidence in price recommendations.

Practical uses of AI in pricing

There are three main ways AI is already improving pricing performance in B2B.

The first is price optimization. Algorithms can calculate optimal price corridors based on historical transactions, product hierarchy, region, customer type and deal size. They can detect over-discounting, reveal which customers consistently buy below the benchmark, and suggest target prices that improve margins without reducing win rates. When properly calibrated, these models evolve as the market changes, learning from each new deal.

The second is deal guidance. AI-enabled systems can feed sales teams with real-time price recommendations directly in the CRM. When a salesperson enters a quote, the system can instantly flag if the proposed price is too low or if a higher price has historically been accepted by similar customers. This reduces margin erosion and ensures more consistent pricing discipline across the organization.

The third is harmonization and anomaly detection. In large portfolios with thousands of customers and products, inconsistency is inevitable. AI can scan pricing data to identify outliers — customers paying far less or far more than others for similar services — and highlight where harmonization opportunities exist. It enables pricing leaders to make evidence-based adjustments that are both fair and defensible.

The human layer in AI-driven pricing

AI can analyze, recommend and even decide. But it cannot yet understand the nuance of relationships, perception and context that shape real-world pricing. The best results come when AI insights are combined with human experience — a “human-in-the-loop” model.

Pricing professionals remain crucial interpreters. They are the ones who ask the right questions: Is this customer strategically important? Does this price reflect our value proposition? Is the data reliable? In this sense, AI becomes an amplifier of human judgment rather than a replacement.

Organizations that succeed with AI-driven pricing build trust between data scientists, pricing teams and sales. They establish transparent governance so that everyone understands how algorithms work and where human override is allowed. This combination of machine precision and human understanding creates a level of pricing intelligence that neither could achieve alone.

Data quality – the hidden challenge

Many companies underestimate how much effort is required to prepare their data before AI can add value. Transaction data may be incomplete, inconsistent or poorly categorized. Discount structures might vary by region or system. Customer segmentation may be unclear.

AI depends on clean, structured data. When data quality is low, the model will reflect that noise. Successful implementations therefore start with a data foundation: defining clear taxonomies for products, standardizing customer groups, and ensuring that financial, sales and pricing data are properly connected. It is tedious work, but without it, AI insights risk being misleading or even harmful.

The lesson is simple: AI can reveal insights hidden in data, but only if that data tells a coherent story. Data governance is not an afterthought – it is the backbone of any AI pricing strategy.

The organizational transformation

Adopting AI in pricing is as much a cultural journey as a technical one. It challenges traditional ways of working, especially in organizations where pricing authority has historically been decentralized or intuition-driven. AI introduces transparency and objectivity. That can feel uncomfortable at first, particularly for sales teams used to negotiating freely.

To succeed, companies need to position AI as a tool that supports sales, not one that restricts it. Instead of saying “the system decides your price,” communicate that “the system helps you find the best possible price for this situation.” The goal is empowerment, not enforcement. When sales teams understand that AI can increase their hit rate and protect margins, adoption follows naturally.

For pricing leaders, this shift also means redefining their role. The new Head of Pricing is not just a policy maker or gatekeeper of discounts, but a translator between data, technology and commercial strategy. Pricing leadership becomes a bridge between algorithms and humans — ensuring that the insights generated by AI turn into profitable decisions in the field.

Generative AI and the next frontier

The newest development is the use of Generative AI in pricing. Large language models can summarize complex datasets, produce executive summaries of pricing performance, or even generate customer-specific price argumentation. Instead of manually compiling reports, pricing analysts can simply ask a model to “explain why gross margin dropped by two points last quarter” or “simulate the impact of a three percent price increase across all B2B contracts.”

These tools are still in their infancy, but their potential is significant. They can automate repetitive analytical work, generate pricing playbooks for sales, and speed up the preparation of management materials. The future pricing function will likely use a hybrid environment: traditional AI models for numerical optimization, and Generative AI for communication, explanation and storytelling. Together, they make pricing not only smarter, but also more accessible to non-specialists.

From technology to strategy

Ultimately, AI in pricing is not about technology; it is about better decision-making. The real competitive advantage lies in how well a company can translate data-driven insight into action. AI can reveal where profit hides, but it cannot execute the change — humans must still lead that transformation.

Companies that treat AI as a plug-and-play solution will be disappointed. Those that see it as an enabler of continuous learning and disciplined improvement will thrive. The future of pricing is not defined by algorithms, but by how intelligently they are used.

When humans and machines collaborate, pricing evolves from a static process into a living system — adaptive, transparent and value-driven. AI gives us the tools to understand complexity at scale, but it is still judgment, empathy and strategy that turn that understanding into profit.

And that is perhaps the most exciting part: AI will not make pricing less human. It will make it more so — by allowing people to focus on the decisions that truly matter.

Author: "Mr Pricing", Tobias Murray, CEO and Co-founder at VAERG vaerg.com

About the author. Tobias Murray helps B2B companies turn pricing into a scalable growth engine. With long-standing experience across industries, he specializes in structured, data-driven pricing strategies that consistently deliver 10–25% EBITDA uplift. As CEO of VAERG, he and his team transform fragmented pricing into a systematic, value-generating discipline.