Generative AI in Pricing: Dynamic, Value-Based and Fair

Pricing is one of the most powerful yet least evolved levers of strategy. For decades, executives have relied on a mix of market data, gut instinct, and negotiation skill. But in 2025, a new force has entered the equation — Generative Artificial Intelligence — capable of analysing unstructured data, generating predictive insights, and even drafting value propositions that resonate with customers.

For Australia’s mid‑market companies, where pricing discipline often makes the difference between margin resilience and erosion, generative AI offers an unprecedented opportunity. It enables dynamic pricing without chaos, value‑based pricing without bias, and transparent pricing that strengthens trust rather than undermining it.

The Strategic Shift: From Price Setting to Value Discovery

Traditional pricing asks, 'What will customers pay?' Generative AI reframes the question to, 'What do customers value most, and how can we capture it fairly?' This distinction matters. Price setting is transactional; value discovery is relational.

Generative AI gives strategists the tools to move beyond spreadsheets into a multidimensional understanding of customer behaviour, sentiment, and willingness to pay.

By training models on proposal data, sales conversations, market commentary, and customer reviews, organisations can uncover the intangible factors that shape price sensitivity: urgency, trust, perceived quality, or switching cost. These insights don’t replace human judgment — they sharpen it.

Dynamic Pricing, Reimagined

Dynamic pricing once required complex optimisation engines accessible only to airlines and global e‑commerce giants. Today, generative AI platforms can simulate those same capabilities in natural language, using data already available inside most firms.

A mid‑market logistics company, for example, could connect its historical shipment data, fuel cost indices, and customer contracts to an AI engine that forecasts demand fluctuations. The model might recommend marginal price adjustments per route — increasing by 1.5% in high‑load corridors while offering loyalty discounts in under‑utilised regions.

The benefit isn’t just precision; it’s speed. Decisions that once required monthly analysis can now update daily. Generative AI further improves communication. It can automatically draft customer‑ready explanations: 'Pricing has been adjusted in line with fuel cost movements and service demand. Your loyalty tier ensures continued preferential rates.' Such transparency helps preserve trust even in a dynamic system.

Value‑Based Pricing at Scale

Value‑based pricing aligns price with customer outcomes, but it has historically been hard to scale because it demands deep understanding of perceived value. Generative AI changes that calculus by analysing qualitative signals. It can process product feedback, service tickets, and account notes to determine what customers truly value — speed, reliability, sustainability, or relationship depth.

Imagine a B2B services firm using AI to assess thousands of project summaries. The model clusters projects by outcome language: 'reduced downtime,' 'accelerated deployment,' 'cost avoidance.' Each cluster reveals not just what was delivered but what was valued. Pricing can then shift toward performance‑based models that link price to measurable outcomes.

Human + Machine: Redefining the Pricing Team

An AI‑enabled pricing function looks different. Instead of analysts buried in spreadsheets, you have strategists orchestrating feedback loops between AI insights and commercial decisions. Generative AI handles the heavy lifting — data synthesis, scenario simulation, and draft narrative creation. Humans provide what machines cannot: ethical oversight, empathy, and contextual nuance.

This collaboration transforms pricing meetings. Rather than debating whose spreadsheet is right, teams debate why customers behave the way they do — and how to align prices accordingly.

Fairness and Trust: The Ethical Mandate

As AI systems touch more commercial decisions, fairness becomes non‑negotiable. Australian regulators are already exploring frameworks around algorithmic transparency, consumer protection, and responsible AI use. Mid‑market companies must get ahead of these expectations.

Fairness begins with visibility. Every automated decision should leave an audit trail: the data used, the assumptions applied, and the rationale generated. Generative AI can assist by producing explainable narratives, converting algorithmic logic into human language: 'This quote reflects regional demand growth, service tier, and historical purchase volume.' When pricing becomes explainable, it becomes defensible — both ethically and legally.