Pricing and Discount Rule

There are no documented pricing rules. Every rep prices deals from instinct, tribal knowledge, or whatever they think the market will bear. There's no list price, no discount framework, no margin floor. 'What should I charge for this?' gets answered differently depending on who you ask and what day it is.

AI cannot automate any pricing because no pricing logic exists. There's nothing to encode.

A price list exists — a spreadsheet or document showing list prices for standard products. But discount rules are informal: 'you can give 10-15% for big orders, maybe more for strategic accounts.' There are no written volume break schedules, no documented margin floors, and no defined escalation path for exceptions. Pricing discipline depends on individual rep judgment.

AI can generate quotes using list prices but cannot apply appropriate discounts because discount logic isn't documented. Every non-list-price quote requires human judgment.

Pricing rules are documented in a policy manual — volume discount tiers, customer-tier multipliers, seasonal pricing windows, and margin floor thresholds. The policy exists as a reference document that reps consult when building quotes. But the rules are in a static document separate from the quoting system — reps read the policy and manually apply it. Enforcement depends on compliance, not system controls.

AI can check quotes against the documented pricing policy and flag deviations, but cannot enforce pricing in real-time because the rules aren't embedded in the quoting workflow.

Pricing and discount rules are encoded in the CPQ system. Volume breaks calculate automatically from order quantities. Customer-tier pricing applies from CRM data. Margin floors trigger approval workflows when breached. Promotional pricing windows activate and expire on schedule. The pricing rules are enforced by the system, not by rep compliance. A rep cannot send a quote that violates policy without explicit approval.

AI can generate fully compliant quotes automatically, enforce margin discipline in real-time, and flag pricing anomalies. Cannot yet optimize pricing for competitive situations because the rules are enforcement-based, not optimization-based.

The pricing model is a formal ontology with machine-readable rules linking product costs, competitive positioning, customer value, contract terms, and historical outcomes. Pricing rules are not just enforcement constraints but optimization targets — the system recommends 'price at $X because this maximizes expected value given the competitive situation, customer tier, and volume.' An AI agent can ask 'what pricing strategy maximizes margin for this customer segment given current competitive intensity?' and get a precise, data-driven answer.

AI can perform dynamic pricing optimization — recommending prices that balance margin, win probability, and customer lifetime value based on comprehensive market context. Full pricing automation for standard and semi-custom scenarios.

Pricing rules are a living optimization model that adjusts in real-time. Material cost changes propagate through pricing instantly. Competitive intelligence adjusts market positioning dynamically. Demand signals influence promotional timing. The pricing model is not a static rule set applied to quotes — it's a continuously optimizing engine that produces the best price for each transaction given the current state of the business and market.

Fully autonomous pricing management. AI optimizes pricing in real-time based on costs, competition, demand, and customer value — the pricing model evolves continuously with the market.

Ceiling of the CMC framework for this dimension.