AI in Pricing: Hard Truths from the Trenches
Our 4th Pricing Gym session on July the 10th brought together seasoned leaders from construction, healthcare, tech, and B2B services to tackle a question that's keeping pricing professionals up at night: Is AI genuinely transforming pricing, or are we just putting lipstick on analytics pigs?
What emerged was a fascinating blend of war stories from the trenches and fresh insights that challenged conventional wisdom. Here's what we learned and what two decades of pricing experience tell me about where this is all heading.
The Uncomfortable Truth About "AI Pricing"
Here's what twenty years in pricing trenches teaches you: every revolutionary tool promises to eliminate the human element. None succeed.
Today's AI pricing landscape reminds me of the CRM revolution circa 2005. Vendors promised automated customer relationships. What we got were better databases that still required human insight to drive revenue.
The reality check: Most current AI pricing solutions are sophisticated predictive analytics engines wearing machine learning makeup. They excel at automating transactional pricing, one participant mentioned their tool handles 70% of routine pricing actions, but consistently stumble on strategic decisions.
Why? Because pricing isn't just math. It's psychology, market dynamics, competitive positioning, and customer relationship management rolled into one complex decision matrix.
The Data Disaster Nobody Talks About
Every pricing professional knows the dirty secret: we spend more time cleaning data than analyzing it. AI doesn't change this equation, it amplifies it.
During our session, a construction industry veteran shared his team's six-month journey before their AI tool produced its first useful output:
“Six months of data harmonization, exception handling, and business rule definition. The AI was ready to work; the data wasn't.”
The hard lesson: AI doesn't solve data problems—it exposes them. Companies rushing into AI pricing without addressing foundational data hygiene are setting themselves up for expensive disappointment.
Legal Landmines in the AI Pricing Gold Rush
Here's where seasoned pricing professionals earn their stripes: understanding that every pricing decision carries legal implications.
The legal expert in our session delivered sobering news about public AI tools. Sharing pricing data with external AI models creates confidentiality breaches, IP exposure risks, and potential trademark invalidation issues.
The professional approach: Deploy AI pricing within secure, governed environments with clear data handling protocols. The cowboys uploading pricing data to ChatGPT? They're creating liability time bombs.
The Ethics Crisis Nobody Saw Coming
Twenty years ago, dynamic pricing meant seasonal adjustments and volume discounts. Today, it means algorithmic price optimization that can shift faster than human oversight can follow.
COVID-19 provided the stress test. Imagine if dynamic pricing algorithms had priced ventilators beyond hospital budgets during the pandemic peak. The market efficiency might have been perfect. The human cost would have been catastrophic.
The veteran's perspective: Ethical guardrails aren't nice-to-haves: they're business continuity requirements. Price-gouging allegations can destroy decades of reputation-building in weeks.
Sales Teams: The Make-or-Break Factor
I've watched countless pricing initiatives fail at the sales handoff. The pattern is always the same: brilliant analysis, sophisticated algorithms, and sales teams who treat the new prices like suggestions rather than strategy.
The breakthrough insight from our session? Successful AI pricing implementations position technology as a sales accelerator, not a constraint. The best performing teams frame AI recommendations as "here's how to hit your targets faster" rather than "here's what you must charge."
The alignment secret: When sales teams see AI as their competitive advantage rather than corporate oversight, adoption rates soar, and revenue follows.
Competitive Intelligence: The Strategy Trap
AI can scrape public tender data, analyze competitor pricing patterns, and identify market trends faster than any human team. But here's what twenty years teaches you: following competitors is often a path to mediocrity.
During our session, a B2B services leader described how their AI tool perfectly tracked competitor pricing moves and how blindly following those moves nearly destroyed their differentiation strategy.
The wisdom: AI provides competitive intelligence; strategy requires human judgment. The companies winning with AI pricing use technology to understand the competitive landscape, then make strategic decisions that create rather than follow market dynamics.
The Future: Agentic AI and Digital Twins
Recently, I have created a “pricing sparring partner,” an AI/ML powered digital twin that challenges my pricing assumptions before client presentations.
This represents the future of AI in pricing: not replacing human judgment but sharpening it. The AI asks uncomfortable questions, stress-tests the logic, and identifies blind spots that human teams miss.
The evolution: We're moving from AI as a pricing calculator to AI as a strategic thinking partner. The companies preparing for this transition are building competitive advantages that will compound over the years.
What This Means for Your Pricing Strategy
After facilitating hundreds of pricing discussions, I can predict the question you're asking: "So what should I actually do?":
If you're just starting: Focus on data foundation before AI implementation. Clean, consistent, accessible data is your prerequisite for any AI success.
If you're evaluating tools: Prioritize secure, internal deployments over flashy public AI integrations. Your pricing data is your competitive advantage, protect it accordingly.
If you're implementing: Align sales incentives with AI recommendations. The best algorithm in the world fails if your sales team ignores it.
If you're scaling: Build ethical oversight into your processes. The companies that thrive long-term are those that optimize for sustainable value creation, not short-term margin extraction.
The Bottom Line
AI in pricing isn't overhyped, it's misunderstood. It's not a replacement for pricing expertise; it's an amplifier of it. The professionals who thrive in this environment are those who understand technology's capabilities and limitations equally well.
After twenty years of watching pricing evolve, I'm convinced we're at an inflection point. The next five years will separate companies that use AI strategically from those that chase AI tactically.
The question isn't whether AI will transform pricing, it's whether you'll master the transformation or be mastered by it.
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Connect with us to explore how AI can responsibly accelerate your pricing strategy—or join our next Pricing Gym session to learn from the practitioners who've already walked this path.
