The chemical industry has a pricing problem. Chemicals are getting more commoditized every day, margins are shrinking, and companies can't keep up with how fast things change. One reason? Most companies are still using pricing systems built for a world that doesn't exist anymore.

Sure, chemical companies have spent millions on digital transformation. But when it comes to pricing, they're still stuck in the past with cost-plus calculations, rigid rule sets, and CPQ systems that actually make the commoditization problem worse.

The Value-Based Pricing Trap

Here's the thing: most specialty chemical companies already know they should be doing value-based pricing. They understand their products deliver real value beyond raw material costs. The problem is that their traditional pricing systems can't actually capture that value effectively.

Take a specialty polymer company competing against cheaper alternatives. They know their product helps customers cut waste by 30%, improves processing efficiency, and meets critical regulatory requirements. So they try to price for that value.

But their pricing system can only handle this through rigid rules and static calculations. Maybe they have a "regulatory compliance premium" or a "waste reduction multiplier." When market conditions change or new competitors emerge, these static approaches break down. Sales teams end up falling back on cost-based negotiations because that's all their systems can really support.

The result? They're trying to do value-based pricing with tools that inevitably push them back toward commoditization.

Why Rules-Based Pricing Doesn't Work Anymore

Many companies have tried to get smarter about pricing by building rules-based systems. You know the type:

• IF customer buys >1000 tons AND signs 2+ year contract THEN give 3% discount

• IF Competitor X shows up THEN drop price by 5%

• IF raw materials spike >10% THEN raise price by 8%

Sounds logical, right? The problem is that rules like these break down fast in real markets.

First, these rules are based on what happened in the past, not what's happening now. A rule that worked great during stable times can destroy margins during a supply crunch.

More importantly, rules can't handle the “art” of pricing. Real pricing decisions involve dozens of variables and unstructured contextual factors when going into a price. Does the procurement team like to negotiate prices a lot? Is this a key customer? Have they been mad about a recent quality issue? There’s plenty of art in delivering the right price to a customer.

CPQ Systems: Just Fancy Calculators

Many chemical companies have their CPQ (Configure, Price, Quote) run pricing for them. And sure, they're great at keeping price lists organized and making sure sales reps don't go rogue with discounts.

But here's what CPQ systems don't do: they don't actually figure out what the right price should be. They just automate whatever pricing logic you had before. If you were commoditizing your products with cost-plus pricing, your CPQ system will just commoditize them faster and more consistently.

CPQ systems are reactive. They price whatever the sales rep configures instead of suggesting what should be configured and priced to maximize value.

AI-Native Pricing: How It's Actually Different

AI-native pricing isn't just rules-based pricing with better software. It's a completely different approach that learns from patterns instead of following predetermined rules.

Instead of programming responses to scenarios you think might happen, AI systems learn from what actually happens in the market. Every deal, every customer interaction, every competitive move becomes data that makes the next pricing decision smarter.

Here's what makes AI-native pricing fundamentally different:

It handles complexity: While rules-based systems break down with more than a few variables, AI agents can handle hundreds of structured and unstructured factors at once. Customer history, competitive landscape, inventory levels, production schedules, raw material trends, seasonal patterns, and market sentiment all get processed simultaneously.

It learns continuously: Every quote teaches the system something new about what customers value and what competitors are doing. The system gets smarter with each interaction instead of staying static.

It finds value you're missing: This is the big one. AI can identify patterns in customer behavior and outcomes that humans miss. For example, let’s say you lost a quote because a competitor undercut price by 10%, AI agents can be trained to understand “why” behind dramatically different prices, and suggest changes for next time that don’t automatically follow just dropping the price.

It does it fast, across every SKU: Humans ironically are pretty good at looking at a bunch of data points, factoring in context and coming up with the best price. But when you catalog approaches 1000 SKUs, the context and time any human has can break down fast. AI agents don’t suffer from this problem, because their entire goal can be designed around being the best pricing analyst for every quote.

Here's How It Works in Practice

Let's say you're pricing a specialty solvent for an automotive manufacturer. You got the quote request via email and you have previous email context about the quote. Due to inventory issues, the automotive manufacturer needs it in under 5 days.

Traditional rules-based system: Base price + volume discount + automotive margin + delivery premium = $2.85/liter. Lead time is set at 3 weeks. Done.

AI-native system for pricing: Analyze previous customer interactions, quote history, and sales history for pricing. AI agents contact the production scheduler and inventory planner to see what we have on hand and what it would take to squeeze this order in. Price at $3.2/liter for expedited order, deliver a quote to customer in 5 minutes, and win customer loyalty + extra business.

In this AI-native system, AI might discover that this customer values reliable delivery over low prices, operates on thin margins but high volume, and historically pays premiums during supply crunches. So it recommends $3.20/liter with guaranteed delivery terms. The customer accepts because it addresses what they actually care about.

That extra $0.35/liter might seem small, but multiply it across thousands of transactions and you're talking about millions in recovered margin.

Why This Matters for Your Business

Chemical companies starting to use AI-native pricing are seeing advantages that go way beyond just better margins:

You can actually differentiate: Instead of competing on static product features, you compete through superior pricing that matches the operational advantages of your plants.

You respond faster: AI systems adjust to market changes immediately, not months. You capture opportunities while competitors are still having meetings about what to do.

You understand customers better: The deep analysis required for AI pricing generates insights about customer behavior and market dynamics that you've never had before.

Complexity becomes an advantage: AI thrives on the kind of complexity that overwhelms human analysts, letting you pursue sophisticated strategies across your entire portfolio.

Getting Started (It's Not as Hard as You Think)

You don't need to overhaul everything at once. Most successful companies start with one product line or customer segment and expand from there.

The key requirements: historical data about customers, products, and market outcomes; sales teams willing to work with and improve AI; and a knack for continuous improvement rather than a "set it and forget it" mentality.

But here's what companies are finding once they get going: better margins, faster responses to market changes, stronger customer relationships, and insights that inform everything from product development to competitive strategy.

Conclusions

The chemical industry's commoditization problem is getting worse, not better. Yet there is tremendous appetite for build AI to prevent margin collapse in the chemicals industry. The early adopters that take advantage of this have the most to gain from the operational advantages AI brings.