Using The Power Of AI To Transform B2B Pricing Strategies

Harikishore Sreenivasalu is the founder and CTO of Aarini Consulting . In the dynamic landscape of B2B commerce, businesses are increasingly turning to AI to optimize their pricing strategies. A survey conducted by Gartner Quadrant found that pricing algorithms can increase revenue from 1% to 5% and increase customer life cycle by 20%, and the market for AI based solutions in retail is predicted to reach $24.1 billion by 2028 .

While many companies have already implemented custom Gen AI and ML solutions in their workflows, these can be expensive, unreliable and technically complex, and there is still significant room for improvement in how they are implemented. As the CTO of a leading ITO company, I have found that pricing managers and sales managers can use AI tools to fill a crucial gap in pricing strategies if they shift their thinking in a fundamental way.

Instead of simply asking, “How much data insights can we get out of the AI engine?” we should also think about what kind of data we can feed back into the engine to get even better results. Here are a few examples of putting both those questions into action. One of the key ways AI’s analytical, decision making and recommendation power can be put to use is in identifying insights in current sales and pricing models.

A typical example is customer comparisons and performing price recommendations for complex deals. By analyzing historical sales data, AI can help pinpoint those outliers as well as identify margin gaps and leakage. The success of pricing strategies relies on effective customer and product segmentation.

Here AI can contribute by refining these segments(microsegmentation) through a nuanced understanding of buying patterns and leveraging available information on customer behavior and product performance. Pricing and sales managers often hesitate to use AI in pricing because they don’t trust it, either because the quality of data is poor or because the engine is missing critical information.

While AI excels in quantitative analysis based on sales and market performance, it often lacks access to qualitative insights such as customer sentiment or product perception. Information such as how customers perceive particular products or services is critical when trying to plan pricing strategies.

Bridging this gap with quality data is crucial. Providing AI with both quantitative and qualitative information enhances its ability to make informed recommendations. When sales teams are creating deals or making proposals, they can feed this data back to the AI to train it to refine future questions and insights.

For example, a sales agent may be offering a customer a discount for a product. Maybe this customer is opening a new factory and soon will need more of this product. That agent’s thinking needs to be explained to the AI so it can make more informed recommendations. The collaborative efforts of various teams—product management, sales, marketing and business development—form the bedrock for building a comprehensive knowledge base.

This encompasses interactions, negotiations, and market perceptions actively feeding into AI models’ learning processes. Combining qualitative insights with quantitative data can help AI continuously improve and propose optimal prices. While AI stands as a powerful tool in reshaping B2B pricing strategies, it cannot meet the challenges on its own yet.

Robust governance, data quality and control frameworks are crucial to instill trust in AI models’ recommendations. Human experts such as marketing or product managers need to serve as co pilots, providing day to day inputs and aligning AI driven insights with real world business objectives. The integration of AI marks a transformative journey for B2B pricing strategies, offering optimization in decision making, segmentation, and the infusion of qualitative insights.

However, the effectiveness of AI depends on high quality data, collaborative efforts and a structured oversight framework. Treating AI as a strategic partner in pricing strategies, rather than a standalone solution, opens the doors to more competitive, responsive, and informed B2B pricing practices.

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