The Increase in Demand for APIs will Come From AI and LLMs by 2026

More than 30% of the increase in demand for application programming interfaces (APIs) will come from AI and tools using large language models (LLMs) by 2026, according to Gartner. A survey of 459 technology service providers (TSP) conducted from October to December 2023 found that 83% of respondents reported they either have already deployed or are currently piloting GenAI within their organizations.

“With TSPs leading the charge in GenAI adoption, the fallout will be widespread,” said Adrian Lee, VP Analyst at Gartner. “This includes increased demand on APIs for LLM- and GenAI-enabled solutions due to TSPs helping enterprise customers further along their journey. This means that TSPs will have to move quicker than ever before to meet the demand. Enterprise customers must determine the optimal ways GenAI can be added to offerings, such as by using third-party APIs or open-source model options. With TSPs leading the charge, they provide a natural connection between these enterprise customers and their needs for GenAI-enabled solutions.”

The survey found that half of TSPs will make strategic changes to extend their core product/service offerings to realize a whole product or end-to-end service solution. With this in mind, Gartner predicts that by 2026 more than 80% of independent software vendors will have embedded GenAI capabilities in their enterprise applications, up from less than 5% today.

“Enterprise customers are at different levels of readiness and maturity in their adoption of GenAI, and TSPs have a transformational opportunity to provide the software and infrastructure capabilities, as well as the talent and expertise, to accelerate the journey,” said Lee.

Throughout the product life cycle, TSPs need to understand the limitations, risks, and overhead before embedding GenAI capabilities into products and services. To achieve this, they should document the use case and clearly define the value that users will experience by having GenAI as part of the product; determine the optimal ways GenAI can be added to offerings and consider how the costs of new features may affect pricing decisions; address users’ prompting experience by building optimizations to avoid user friction with steep learning curves; and review the different use-case-specific risks, such as inaccurate results, data privacy, secure conversations, and IP infringement, by adding guardrails specific to each risk into the product.