Is your AI-powered pricing working for you or against you? Many B2B pricing solutions rely on AI without visibility, making pricing decisions without transparency, control, or explainability. But that becomes a business risk when sales teams can’t justify prices, market shifts happen faster than AI models can adapt, and regulators demand explainability. Mitch Lee, Profit Evangelist at Vendavo, breaks down why a lack of transparency in AI is bad for B2B and why explainable, adaptable AI is what businesses need to take back control of AI in pricing.
AI-powered pricing solutions promise precision, efficiency, and competitive advantage. But here’s the catch: Not all AI is built for business success.
Different types of AI offer different strengths, and choosing the wrong approach for your pricing strategy can do more harm than good.
Some AI models prioritize computational power over clarity—but in B2B pricing, transparency and flexibility are essential. That might work in B2C industries like airlines or eCommerce, but B2B pricing is a different game—one that requires transparency, adaptability, and business-driven logic.
If your AI-driven pricing can’t explain its decisions, how can your sales team defend them? If it takes weeks to update AI models, how can you stay competitive? And if regulators start scrutinizing pricing fairness, how can your company prove compliance?
It’s time to move beyond AI buzzwords and focus on what really matters: smarter, explainable pricing that puts businesses in control and solves actual problems.
The AI Pricing Hype vs. Reality
AI is everywhere in pricing today, but many so-called “AI-powered” pricing tools are just rebranded traditional techniques. Some vendors slap an AI label on their technology, but are just using the same statistical models they’ve always used. That’s not artificial intelligence, that’s artificial marketing.
The real challenge is price discovery. Businesses don’t need AI for AI’s sake. They need a solution that helps them find the right price efficiently, accurately, and with confidence. And that starts by matching the right type of AI to the right use case.
When the AI model is too complex or opaque, it creates more issues than it solves. If a pricing engine spits out a number but can’t show how it got there, it’s not a tool. It’s a liability —and a big one at that.
Instead of focusing on buzzwords, pricing solutions should be evaluated on how well they:
- Deliver explainability – Pricing teams should be able to justify every AI-driven price.
- Adapt to market changes in real time – Pricing adjustments should happen instantly, not in weeks or months.
- Ensure compliance – Transparent pricing logic is critical to meeting regulatory requirements.
- Give businesses control – AI should enhance decision-making, not dictate it.
- Solve real business problems – You get the results you need.
The best AI-powered pricing makes smarter, strategic pricing possible. Anything less is a liability for your business.
Why Choosing the Right AI Approach is a Risk for B2B Pricing
Some AI-powered pricing engines, like neural networks, are powerful in theory but problematic in practice—especially for B2B. Many AI-powered pricing engines, like neural networks, claim to be revolutionary, but their biggest flaw is also their biggest risk: They don’t tell you how they reach a decision.
That’s a nightmare for sales teams. Imagine you’re negotiating a high-value deal, and AI recommends a price that seems too high. The customer pushes back and asks why. But there’s no clear answer, just a number generated by an algorithm that no one fully understands. The deal stalls. The customer walks away.
Beyond sales, overly complex or poorly matched AI creates problems across the organization:
- Lack of transparency – If AI pricing recommendations can’t be explained, they can’t be trusted.
- Slow adaptability – AI models that require extensive retraining struggle to keep up with sudden market changes.
- Data bias risks – AI is only as good as the data it’s trained on. If historical data is flawed, so are its recommendations.
- Regulatory compliance issues – AI-driven pricing faces increasing legal oversight, so companies need to prove compliance (and that’s nearly impossible with opaque models).
- Risk of mispricing – If AI gets a price wrong, who takes responsibility? If the price is too high, deals are lost. Too low, and margins erode. Either way, you take the hit.
Here’s the bottom line: AI-driven pricing that can’t be explained, adjusted, or audited is not a competitive advantage. It’s a potential problem.
AI That Enhances Business Logic, Not Replaces It
AI should work alongside pricing teams, not replace them. The most effective B2B pricing solutions combine AI-powered insights with business-driven rules and strategic guardrails.
A well-designed AI pricing solution should:
- Offer full transparency – Pricing logic should be clear, explainable, and defensible.
- Support real-time adaptability – Market conditions change quickly, and pricing models should adjust just as fast.
- Ensure compliance and auditability – Businesses must be able to track, justify, and report on AI-driven pricing decisions.
- Allow business-driven customization – AI should respect company rules, pricing floors, and margin protections.
- Work with existing data – AI should be effective using the data businesses already have, without requiring massive, clean historical datasets.
The short version: AI-powered pricing should empower businesses to see, understand, and refine AI-driven insights.
The Problem with Over-Reliance on Neural Networks
Many vendors promote neural networks as the ultimate pricing optimization method. While these models have value in some applications, they also come with serious drawbacks:
- They’re opaque. Neural networks function without visibility, making pricing decisions impossible to justify.
- They require extensive data. These models demand enormous amounts of high-quality historical data, which is something most B2B businesses don’t have.
- They’re slow to adapt. Because neural networks must be retrained with new data, they often lag compared to fast-changing market conditions.
- They create compliance risks. Without transparency, businesses are vulnerable to regulatory scrutiny.
- They’re expensive to implement. Neural network-based pricing requires deep system integrations, which increases costs and slows adoption.
A better approach blends AI with proven pricing techniques such as:
- Power and risk modeling to assess price sensitivity and margin impact
- Price elasticity analysis to predict customer response to price changes
- Rules-based pricing logic to align pricing with strategic goals
- Machine learning insights to optimize pricing while keeping human oversight in place
Rather than blindly trusting AI to set prices, you need it to be a tool to support and enhance strategic decision-making. It should make your job easier, not riskier.
AI-Powered Pricing That Works for Your Business
Businesses need to focus on transparency, adaptability, and compliance as AI-driven pricing evolves. Regulations on it are increasing, and companies that invest in explainable, compliant AI today will be ahead of the curve when stricter standards emerge.
It’s important to remember that B2B pricing will always require human expertise. AI should support pricing teams, not replace them. Technology should solve real pricing challenges, not add complexity. The best AI-powered pricing solutions will automate decisions as well as deliver confidence, control, and long-term business impact.
Want to see AI-driven success in action? Vendavo’s explainable, controllable guidance for success comes from the combination of Artificial Intelligence + Human Intelligence. Reach out today to speak with our experts.