Editor’s Note: This is the third in a five-part series.

“Technology doesn’t build the system. People do. But when they build it right, it transforms weeks of work into hours of insight.”

The Parameterized Rules Engine

One of the foundational components of a scalable pricing model is a parameterized rules engine. This approach allows organizations to define business logic using flexible, reusable rules. These rules can be applied to customers and contract attributes, SKU cost data and transaction history to generate clear and actionable output.

Parameterized rules allow pricing teams to respond with speed and precision, even in complex scenarios. They replace ad hoc, reactive decisions with structured logic that is transparent, repeatable and scalable.

Consider the following example based on previous modeling work: A sample of customers, each with different contract constraints, is paired with a group of SKUs influenced by varying inflation or tariff pressures. The business applies tailored pricing logic to this input, including cost thresholds, margin caps, category exceptions and pass-through clauses. Each rule is parameterized, meaning it can be updated without rewriting code or rebuilding the model.

This creates a transparent pricing process where stakeholders can see every step, including input assumptions, applied rules and final recommendations.

The benefits of a parameterized rules engine include:

1. Ease of Adjustment: Business teams can adjust rules and complex conditions without needing support from IT.
2. Scenario Versatility: Teams can generate and compare multiple pricing scenarios in real time.
3. Transparency and Auditability: Conditions and rules are visible, supporting traceability and building trust.
4. Repeatability and Scale: Rules can be consistently applied across millions of customer-SKU combinations.
5. Speed to Insight: Businesses can move from cost changes to decision-ready output within hours.
6. Reduced Risk of Manual Error: Automating pricing logic minimizes inconsistencies, increases accuracy, and improves data integrity.

Parameterized rules do more than improve efficiency. They also bring clarity. In pricing environments influenced by tariffs or inflation, clarity is essential to execution and adoption.

From Data to Decision — Fast, Repeatable, Scalable

A well-structured pricing framework moves from raw data to actionable insight through a defined series of steps:

• Define data attributes from contracts, SKUs, customers and suppliers.
• Map those attributes to variables that can be joined with sales and SKU pricing data.
• Construct formulas that use those variables to create specific customer conditions.
• Generate formula-driven scenarios based on revenue goals, margin targets, risk tolerance, and operational constraints.
• Produce recommendations from the approved rules.
• Present recommendations in a prioritized, actionable, and collaborative format.

Now do it again. And be ready to repeat the process five more times next week.

This is only possible with a structured, repeatable process. Rather than rebuilding logic and reprocessing the steps to executive on that logic for each scenario, distributors can rely on a model that performs predictably, transparently and at scale.

In advanced implementations, distributors go beyond basic tariff data attribution and incorporate full landed-cost modeling. Rather than reacting solely to tariff percentages, these models account for the complete cost-to-serve, including inbound freight, insurance, customs fees and port handling. This level of detail allows pricing teams to generate more accurate margin forecasts and make pricing decisions based on actual costs. Distributors who ignore these hidden costs risk underestimating their exposure and making pricing decisions that erode profitability.

This example shows why automation and visibility are so powerful. I was in a meeting with the CFO of a global distributor in mid-April 2025. The topic? Tariffs. As we described the repeatable framework you’ve just read about, he not only grasped the value quickly, but took it further. Once he understood that the output could be generated automatically, he paused and asked, “Could we also use this to do X or explore Y?”^[1]

It wasn’t just a thoughtful question, it was a breakthrough idea, one that none of us had considered. Once he understood the framework, he began to imagine new possibilities. That’s the real power of a repeatable model: it doesn’t just drive consistency. It fuels innovation. When leaders understand what’s possible, they start asking better questions, and those questions are what position companies to thrive.

Cross-Referencing and Specific Cost Concessions

When disruption hits, product flexibility can protect your margin faster than pricing changes. When the cost of a product rises, can your organization quickly pivot to alternative sources?

The key to enabling that pivot is internal cross-referencing.

Most distributors maintain some level of cross-reference capability, but it is often incomplete or limited to external data, such as competitor part numbers or manufacturer-provided alternates. What is often missing is the internal logic enriched with margin and availability data that enables faster, more strategic decisions.

With a strong internal cross-reference engine, you can accomplish the following:

• Recommend substitutes when tariff-driven cost increases occur.
• Offer good, better, and best options, based on the quality of the SKU.
• Optimize margin, both front side and buy-side with strategic supplier partners.
• Lessen the impact on customer price by guiding customers to tariff-exempt options.

Here’s how this plays out in real-world execution with tariffs:

A product you sell is suddenly hit with a 25% tariff. Your pricing model immediately flags the impact and proposes an increase. Instead of pushing that increase to the customer, your system identifies a functionally equivalent alternative from another supplier already approved in your system, already stocked in your warehouse, and already yielding 4% more margin.

The customer receives a proposal featuring the new item at nearly the same price. Your margin is preserved, and the customer avoids any negative impact. You not only preserve profitability; you elevate your brand’s value.

Cross-referencing isn’t just a data capability — it’s a strategic margin lever.

To build this, you need practitioners who can:

• Define matching logic (exact vs. close vs. substitute)
• Assign margin and availability weights
• Integrate cross-references into pricing decision trees

When one distributor incorporated product alternates into their pricing framework, it became a powerful differentiator with their partner suppliers. By proactively presenting substitutes to customers during inflation disruptions, they not only reduced cost pressure for key customers, but also increased their channel value to the supplier. Suppliers rewarded this capability with both enhanced front-side deviations and backside funding. What started out as a process to dampen tariff impact to the customer, evolved into a strategic advantage, turning the product catalog into a dynamic asset.

Another approach is to negotiate with the incumbent supplier to delay the cost increase. That will typically be resisted, but if you can share with them specific customers (your key customers), you can make the case (and it will be made more powerfully if you can share with the incumbent supplier there are other options) to create a deviation for a period of time. This is another powerful way this distributor in our case study approached addressing inflation.

The Role of AI — Acceleration, Not Autopilot

Many believe AI will fix pricing on its own. It won’t. But it will dramatically improve how your team does it. While AI has remarkable capabilities, it is not a substitute for structured thinking, sound judgment, or disciplined execution. At least not in isolation. What AI can do, when applied thoughtfully, is empower pricing teams to move faster, with greater clarity and less manual burden.

In the case study referenced earlier, a team of pricing professionals manually reviewed over 40,000 customer contracts. They identified unique terms and entered critical attributes, such as escalation clauses, exclusions, and other conditions, into a structured pricing database. This was a necessary step, but it was also labor-intensive.

This is where AI provides leverage.

With the right implementation, AI can extract key attributes from unstructured text, classify and tag them, and present them for human validation. AI does not replace human judgment; it enhances it. The goal is not automation for its own sake. The goal is acceleration.

Automating Contract Data Attribution

AI-powered tools, particularly those using natural language processing (NLP), can assist in converting dense legal text into actionable data:

• NLP models identify pricing conditions such as margin floors, rebate thresholds, or pass-through clauses.
• Machine learning algorithms flag missing or ambiguous terms for human review.
• Classification engines group contracts by risk or pricing tier based on embedded logic.

Platforms such as Coupa and LegalOn demonstrate how NLP can extract contract elements, such as escalation clauses or term-based adjustments, and assign them to structured fields, turning unstructured PDFs or emails into actionable pricing data.

Enhancing Data Validation

AI strengthens data integrity by proactively identifying gaps, inconsistencies, and anomalies before they impact pricing decisions. This is especially important when cost inputs are sourced from multiple systems or supplier channels. Examples include:

• Cross-referencing supplier quotes, tariff rates, and historical records to identify conflicting inputs
• Flagging incomplete or inconsistent line-item details and suggesting potential corrections
• Generating audit-ready pricing justifications that cite source documentation

As distributors manage growing volumes of cost and supplier data from disparate sources, AI becomes essential for automating first-level validation and ensuring that pricing teams operate from a trusted foundation.

Accelerating New Data Integration

In rapidly evolving environments, AI helps ingest and normalize data that would otherwise be difficult to use:

• Mapping supplier product catalogs to internal SKUs, even when formats don’t align.
• Extracting pricing logic from PDFs, emails, or spreadsheets.
• Recommending cross-references based on past substitution or transaction patterns.

The ability to convert raw, unstructured data into structured data attribution enables the analytics engine to generate consistent and trustworthy outputs.^[2]

AI won’t replace judgment — but it will accelerate it.

By combining structured human insight with intelligent automation, distributors can reduce manual workload, strengthen pricing integrity, and build adaptive systems that respond to market volatility without overwhelming their teams.

The Distributors’ Analytics Imperative Series

• Part 1: When Survival Demands an Analytics Revolution
• Part 2: Attribution Breakthrough – Building on Bedrock
• Part 3: The Analytics Prescriptive Engine — From Static Reports to Strategic Recommendations
• Part 4: The Human Algorithm: Why Great Analytics Fail Without Great People
• Part 5: How to Turn Analytics into an Organizational Muscle

Footnotes

[1] To preserve confidentiality, we are withholding the specific ideas. However, as of this writing, those ideas—if successfully implemented—are projected to generate over $20 million in incremental profit for the company in 2025.

[2] According to Zilliant’s recent survey (press release April 9, 2025) of pricing professionals, 83% of B2B companies are already using or exploring AI solutions to adapt to economic volatility, including tariffs.