Small Language Models And The New Economics Of Enterprise AI

Gary Kotovets, Chief Data, Analytics, and AI Officer at Dun & Bradstreet.

​Enterprise AI is entering an accountability phase. Uber COO Andrew Macdonald ignited headlines when he said it was becoming harder to justify AI spending because higher token usage had not translated into a proportional increase in useful consumer features. His concern captures a question many companies now face as AI moves from pilots into production: How do we turn greater usage into sustainable enterprise value? ​

For many teams, the initial instinct was to send every problem to the largest model available. That approach helped companies experiment quickly, but production systems demand a different level of discipline. The question leaders should ask is: What is the smallest model that can do this job reliably?​

Give each model a specific job​.

I think of small language models (SLMs) as smaller-scale operational units. Each one handles a discrete chunk of work, such as identifying a company website, classifying information, evaluating relationship signals or ranking relevant content. Because the assignment is narrow, the model can be trained and tuned to perform at scale and cost-effectively.​

Large language models (LLMs) still have an important role in complex reasoning and generation. SLMs can take on repetitive, high-volume work before an LLM is called, allowing the larger model to focus on the part of the process that requires its broader capabilities. The two primary benefits are straightforward: lower cost and stronger accuracy on a specific task.​​

Filter before you generate​.

Here’s what that looks like in practice: Consider a production workflow that creates structured company descriptions from content collected across approximately 500 million web pages. Sending all of that material directly to a general-purpose model would require it to process an enormous amount of irrelevant content along with the useful information.