Assessing Algorithmic Versus Generative AI Pricing Tools
Law360, 2024
Companies rely on pricing algorithms to optimize prices at speeds and capacities that humans cannot match. Typical pricing algorithms often require advanced technical expertise and are expensive to develop, thus placing small firms at a disadvantage. However, the advent of generative AI models such as large language models (LLMs) has opened a new era in the field of algorithmic pricing. Trained on large volumes of data, LLMs can perform a wide variety of tasks, including the design of pricing recommendations far more cheaply than traditional algorithmic tools. In their article “Assessing Algorithmic Versus Generative AI Pricing Tools,” published on Law360, academic affiliate Maxime Cohen, Principal Jimmy Royer, and Manager Tim Spittle explore what’s different – and what’s not – about relying on LLMs to price products or services.
In their article, the authors provide background information on typical pricing algorithms, including why many companies use them and the various types on the market. They describe how LLMs operate, how they increase access to data-driven pricing recommendations, and why pricing differentiation may be driven by a user’s ability to craft a high-quality prompt. The authors also analyze differences between typical algorithmic pricing tools and LLMs in terms of licensing and deployment, input and data, and risks and advantages. They note that, similar to typical algorithmic pricing tools, the use of these tools for pricing is likely to come under increased regulatory scrutiny. Professor Cohen, Dr. Royer, and Mr. Spittle conclude by opining that LLMs offer an affordable and effective way for small firms to design pricing recommendations, which may increase competitiveness in markets in which gathering pricing intelligence is otherwise difficult or prohibitively expensive.