Artificial intelligence-led pricing has become common in consumer markets. For instance, when booking a taxi through apps like Uber during peak hours, you may notice prices dynamically adjusting based on demand, distance, and time of day. Uber has even been rumoured to consider smartphone battery levels when setting fares. Similarly, Amazon offers its sellers AI-based dynamic pricing options, including predictive pricing, which utilizes advanced forecasting and optimization algorithms aimed at achieving goals like increasing margin, revenue, or sales.

Businesses of all sizes are increasingly aware of the potential of AI-driven pricing, with a recent study finding 76% believe it is highly relevant to increase their profitability. While only 27% currently use AI to optimise promotions, you will likely see a significant rise in prices being set by algorithms across every B2C and B2B industry within the next five years.

AI-led pricing: The good, the bad and the ugly

The rationale for introducing AI-led pricing is clear: it efficiently handles vast data, enabling swift insights, trend responsiveness, and margin optimization.

This not only benefits businesses, but consumers too, as algorithms adjust prices based on factors like elasticity, demand-supply dynamics, and customer income sensitivity, ensuring affordability and accessibility.

Of course, there are risks to implementing AI-led pricing. Algorithms are only as good as the data they’re trained on, and any bias in the data can influence subsequent pricing decisions.
Without training on a broad set of contextual data, algorithms may overlook key local, cultural, or behavioural conditions.

Furthermore, AI-led pricing reduces human involvement, potentially leading to decisions made without empathy or intuition, which are sometimes essential.

Are businesses ready?

Despite the potential benefits, many businesses are far from fully grasping AI’s capabilities or integrating it into their pricing strategies.

Surveys reveal that numerous companies lack the necessary IT infrastructure and mature datasets for AI-led pricing adoption. Successful implementation goes beyond technology, necessitating robust data quality, accessibility, and trust across the organization. Collaboration between data scientists and managers, supported by a robust governance process, is essential.

Achieving meaningful AI integration in pricing requires a comprehensive change management process, impacting data capture, storage, and management practices across the business.

Introducing an AI pricing methodology

Integrating AI into your pricing demands a methodology aligning vision with objectives, focusing on what your business truly needs and ensuring effective delivery. This methodology comprises five key elements: pricing strategy, setting, execution, data, and governance. The first three elements assess your pricing strategy’s maturity, while data and governance are crucial for robust AI integration.

A strong governance framework ensures proper pricing process management, including approval workflows and AI analytics alignment. Additionally, data quality—accuracy, granularity, and frequency—is essential for delivering insightful outcomes.

Six steps to implementing AI in your pricing

  1. Identify the questions you want answering

For your AI pricing strategy to be a success, you need to hone in on specific questions within your business that you want AI to help you find answers to, such as determining your profitability targets, adjusting your revenue goals, or indexing your performance against competitors.

  1. Clarify your current pricing strategy

It’s important to establish the rules and systems by which you currently set your prices. Are you using a cost-plus model? Are you basing prices on competitor activity? Or are you using a value-based model for pricing (which we would always recommend)?

Each approach to pricing will determine the questions you need to answer with your AI strategy, and every answer will then throw up additional questions that your data will need to address.

  1. Identify data resources

You need to identify your data resources and plan how you intend to manage them.

Will your data come from your enterprise resource planning (ERP) system? Do you have a CRM? Are there external data sources that need to be integrated?

Build these into a roadmap that consolidates your strengths and enables you to improve any weaknesses in data resourcing.

  1. Create a single source of truth

Establish a centralized repository for all pertinent data, encompassing product and customer information, sales records, market performance, inventory levels, demand forecasts, and segmentation data. By standardizing data storage and formatting, organizations can ensure the reliability, accessibility, and consistency of their analyses.

  1. Choose your analytical tools

At this point, you can now begin to consume the data you have accumulated to help answer the questions you’ve highlighted as critical to your pricing strategy.

But for that to happen, you need the right suite of analytics tools to deliver the insights you need. There are dozens of different options available, so you must get expert guidance on the tools that are most effective at delivering the right outcomes.

  1. Collaborate, educate, communicate

It is important to foster collaboration between technical and business teams to bridge the gap between AI-driven analyses and pricing strategies. Establish clear communication channels to ensure that insights derived from AI models are effectively communicated and understood across the organization. Additionally, invest in change management processes to educate stakeholders on the technical and business aspects of AI integration, fostering trust and alignment with pricing strategies.

The influence of AI will only grow

AI’s potential to revolutionize pricing strategies is vast, though still in its early stages. In the future, it will be able to streamline tasks like customer segmentation and personalized pricing, leveraging online behaviour and preferences. However, algorithmic pricing raises ethical concerns, demanding careful management.

Yet, a future where AI is ubiquitous in pricing seems inevitable, with companies competing to deliver the most competitive prices. Success hinges on skilled personnel who can effectively leverage AI tools through collaboration, education, and communication. AI is a great tool, but it is still only a tool. And it will only add value to your pricing if the people entrusted with it can collaborate, educate and communicate in the right way.

At Pearson Ham Group, we leverage AI and machine learning in our Pricing transformation initiatives to expedite optimization for clients. Contact our consultants to explore how we can transform your pricing strategies and operational processes and increase value in a sustainable way.