Gartner defines AI application development platforms as those that offer the required technology and workflows to design, build, test and deploy AI-embedded applications. These platforms provide access to foundation models and the capability to ground and place guardrails around them. Software engineering teams utilize these platforms to build AI applications, such as assistants, agents and multimodal applications. Software engineering leaders face increasing pressure to incorporate AI into their products. AI application development platforms host the necessary tooling for enterprise developers to build AI assistants, agents and multimodal apps without extensive knowledge of machine learning. AI application development platforms focus on providing the features developers need to ground models with organizational knowledge. They also reduce risk by implementing responsible AI processes and guardrails within their AI-embedded applications. These platforms help scale the development of AI-embedded applications by offering governance, evaluation metrics and support throughout the application life cycle. Not every platform will offer access to first-party models or application-testing capabilities.
Gartner defines AI code assistants as tools that generate and analyze software code and configuration. They use foundation models like LLMs, program-understanding technology, or both. Developers engage with these assistants to generate, analyze, debug, test, fix, refactor code, search dependencies, update libraries, create documentation, understand code, upgrade versions, translate languages and review commits. They help developers learn and explore codebases and access related information, such as frameworks and tools. AI code assistants integrate with developer environments, code editors, command-line terminals, chat interfaces, project management tools, monitoring, logging and deployment tools. Some are customized to an organization’s specific codebase and documentation. AI code assistants enhance software developers’ experience by boosting their efficiency, accelerating application development, minimizing cognitive overload, amplifying their problem-solving skills, enabling faster learning, fostering creativity and maintaining their state of flow.
Gartner defines cloud AI developer services (CAIDS) as cloud-hosted or containerized services and products that enable software developers who are not data science experts to use artificial intelligence (AI) models via APIs, software development kits (SDKs) or applications. Core capabilities include automated machine learning (autoML) including automated data preparation, automated feature engineering and automated model building, and model management and operationalization for language, vision and tabular use cases. Optional and important complementary capabilities include AI code models and assistants. Cloud AI developer services help organizations embed intelligence, such as AI and ML insights, into their applications. While that is what cloud AI developer services offer, it is more important to note how they accomplish this. These services democratize and increase the availability of AI and ML to software engineers through the automation and features offered. Traditional activities regarding data acquisition, data quality, feature engineering, algorithm selection and model training are augmented by the technology. Cloud AI developer services open up a world of possibilities for software engineers to build AI and ML production capabilities and features for enterprise-built applications.
Gartner defines the generative AI (GenAI) knowledge management apps/general productivity submarket as technologies that enable companies to better retrieve and contextualize information and insight from their knowledge bases, including enterprise AI search, conversational AI platforms, and productivity tools for communications and content development.
Generative AI (GenAI) model providers focus on developing and providing generative AI technologies and make them available to other developers, businesses and general public through APIs or commercial licenses. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. This layer of vendors offers access to commercial or open-source foundation models such as LLMs and other types of generative algorithms (such as GANs, genetic/evolutionary algorithms or simulations). These models can be provided for developers to embed into their applications or be used as base models for fine-tuning customized models for their software offerings or internal enterprise use cases. This helps businesses gain the benefits of advanced generative AI technologies while avoiding the high costs, expertise requirements and time needed to develop these technologies in-house. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
Gartner defines the no-code agent builder (NCAB) market as SaaS-delivered products that offer an integrated design and runtime environment to build, publish and manage AI-powered agents without using coding. AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments. NCABs are used to build agents that can be surfaced in the context of enterprise applications (CRM, ERP or digital workplace applications) and embedded in custom web and mobile apps. In some cases, these agents are invokable by popular AI assistants (e.g., Microsoft Copilot, OpenAI ChatGPT, Google Gemini).