Getting Started with AI in Business Pricing

Where Do You Start?

No matter the industry or maturity level, most pricing work follows a similar flow:

Some of you might have noticed that elasticity and willingness-to-pay modeling are not included here. That’s intentional. Pricing is very important, but it needs special models, data structures and checks. These go beyond what general-purpose LLMs can do today.

Our goal is to show how pricing teams can use AI right away. We want to do this with little disruption. As such, there is no need for big IT investments and dedicated data science resources.

Step 1: Data Cleaning

The Challenge

Data cleaning is one of the most time-consuming and underappreciated parts of any pricing initiative. Good data is essential for any project, and this includes creating a price corridor, a customer segmentation model or a trade discount strategy. To ensure success, you must start with clean data to ensure success.

Unfortunately, most pricing teams face:

• Inconsistent naming conventions (e.g., “ABC Corp” vs. “A.B.C. Corporation”)
• Missing or null values in critical fields like volume, cost or channel
• Duplicates or partial records across systems
• Data silos, where spend is in ERP, product details in PIM and customer info in CRM

Fixing these issues is essential, but also a major bottleneck. Even simple questions like “What is our average spending by customer type?” can be hard to answer, especially  when datasets are hard to join or trust.

Why AI Is Useful

LLMs (like ChatGPT, Claude or Gemini) and task-specific AI tools (like Trifacta or Alteryx) are particularly helpful in this stage because they can:

• Understand patterns and naming variations
• Perform fuzzy matching and joins
• Generate reusable logic for cleaning (e.g., deduplication, imputation)
• Be prompted in natural language, removing the need for coding or SQL

Pricing teams can use general AI tools to clean and merge data on their own. In comparison to building error-prone formulas or relying on IT, this process is faster and more flexible.

Step 2: Data Analysis

The Challenge

Once your data is clean, the next hurdle is turning it into something meaningful. When analyzing customer behavior, product performance or pricing compliance, pricing teams often face three common challenges:

• Large data volumes that are difficult to slice and interpret
• Lack of clarity on where to start or what to look for
• Dependency on analysts or BI tools to produce insights

This can lead to “analysis paralysis.” Teams may spend days reviewing pivot tables but find it hard to answer key questions, such as:

• Where are we leaking margin?
• Are price increases sticking across regions?
• What’s driving discounting behavior?

Why AI Is Useful

AI helps streamline and guide the analysis process in three powerful ways:

• Identifying patterns, trends and anomalies across large datasets
• Summarizing key insights in plain English
• Acting as an on-demand analyst, responding to natural language queries

Instead of waiting for an analyst to build charts, pricing teams can use AI to speed up time-to-insight and surface actionable findings.

Step 3: Customer Segmentation

The Challenge

Customer segmentation is foundational to pricing strategy. Segmentation helps you create price tiers, discount rules and special offers for different customers. This way, you can treat each customer uniquely and increase profits.

However, most companies rely on overly simplistic or outdated segmentation models. Examples include:

• Basic firmographics (e.g., industry, revenue, region)
• Self-reported personas from marketing
• One-size-fits-all discounting logic based on account size

The result is under-optimized pricing, missed upsell opportunities and a misalignment between customer value and pricing strategy.

Why AI Is Useful

AI enables more advanced segmentation by:

• Analyzing multiple behavioral and transactional variables at once
• Identifying hidden patterns and clusters across customers
• Reducing the need for complex statistical tools or data science support
• Generating explainable outputs, like segment labels and strategies

AI can take you beyond simple “SMB vs Enterprise” models to data-driven, purchase-behavior-based segmentation.

Step 4: Research and Insights Gathering

The Challenge

Pricing decisions are only as strong as the insights behind them. Whether you’re exploring willingness to pay, testing feature value or preparing for a price change, research plays a critical role in shaping strategy.

The problem? Most pricing research is:

• Slow and resource-intensive
• Limited to generic survey templates or reused interview guides
• Susceptible to bias in question design and interpretation
• Disconnected from segmentation or usage data, leading to surface-level insights

Teams often have a hard time getting answers fast enough to make decisions. This is especially true when companies launch new products or introduce tiered pricing.

Why AI Is Useful

AI is a powerful assistant for research planning and insight extraction. With AI, teams can:

• Generate tailored, MECE-aligned interview questions
• Adapt questions based on segment data or behavior
• Summarize key themes across responses
• Reduce reliance on external research vendors

This gives pricing teams the ability to run faster, more targeted discovery efforts—without starting from scratch.

Step 5: Execution and Communication

The Challenge

Even the best pricing strategy can fail if it isn’t executed effectively. One important part of pricing work is how a company shares price changes, especially with sales teams and customers.

Common issues include:

• Generic, one-size-fits-all customer messaging that drives pushback
• Sales teams are lacking tailored talking points for different accounts
• Manual effort to create personalized materials, often delayed or deprioritized
• Customer churn risk when price increases are not explained clearly

In many organizations, pricing execution becomes a last-mile problem—one that undermines months of strategic work.

Why AI Is Useful

AI can dramatically improve the communication layer of pricing by:

• Producing personalized emails or letters based on customer attributes
• Creating battlecards and talking points tailored to segment or deal size
• Adapting tone, structure and messaging based on sensitivity to pricing changes
• Scaling personalization without increasing headcount

This is one of the most immediately deployable use cases, and this stage also bridges pricing, marketing and sales enablement.

Tips for Better Prompting

To get the most value out of AI tools, a few small habits go a long way:

• Stick to the same chat thread for each project. Most tools retain context, so using a single session helps the AI build continuity over time.
• Set the role upfront. Begin prompts with statements like “Pretend you are a pricing analyst…” or “Act as a B2B strategist…” to anchor the response.
• Work with data samples. Paste in a few rows and ask the AI to build logic or SQL/Python you can apply offline.
• Use your voice. Many tools (like the ChatGPT mobile app) allow voice input, which is faster and more conversational.
• Be explicit about the output format. If you need tables, bullet points or slide-ready text, ask for it. LLMs are surprisingly good at following formatting instructions.

Think of AI as a fast, versatile junior analyst. Although it needs guidance and checks, AI can drastically accelerate your workflow with just a few prompts.

The Future

AI is already changing how pricing work gets done, and it doesn’t take a dedicated data science team or a full system overhaul to benefit.

Using general-purpose AI tools for common pricing tasks can help teams work more efficiently by providing valuable insights right now. These tasks include data preparation, analysis, segmentation, research and communication. The key is to start small, experiment often and treat AI as an enabler, not a shortcut.

What’s Next in the Series​

In the next article, we’ll look at how general LLMs like ChatGPT can be embedded deeper into pricing team workflows. We’ll introduce the idea of AI-powered pricing agents that work alongside your team with tactical applications.

Revenue Management Labs partners with Alexander Group to help implement pricing strategies for sustained profitable growth.