Gartner defines AI-Augmented Code Modernization Tools as software solutions that use specialized AI agents, generative AI, and deterministic analysis to accelerate the transformation of legacy systems. These tools automate and enhance a broad spectrum of modernization activities, including deep code and architecture analysis, software documentation, dependency mapping, risk assessment, migration planning, and refactoring. By supporting end-to-end modernization workflows, they significantly expedite the adoption of modern software architectures.
The application development life cycle management (ADLM) tool market focuses on the planning and governance activities of the software development life cycle (SDLC). ADLM products focus on the 'development' portion of an application's life. Key elements of an ADLM solution include: software requirements definition and management, software change and configuration management, software project planning, with a current focus on agile planning, work item management, quality management, including defect management. Other key capabilities include: reporting, workflow, integration to version management, support for wikis and collaboration, strong facilities for integration to other ADLM tools.
The market for data integration tools consists of stand-alone software products that enable organizations to combine data from multiple sources and perform tasks related to data access, transformation, enrichment and delivery. They enable use cases such as data engineering, delivering modern data architectures, self-service data integration, operational data integration and supporting AI projects. Data management leaders procure data integration tools for their teams, including data engineers and data architects, or for other users, such as business analysts or data scientists. These products are primarily consumed as SaaS or deployed on-premises, in public or private cloud, or in hybrid configurations.
Gartner defines document management as the tools and practices used to capture, store, process and deliver documents and information in support of personal, team and enterprise needs. Gartner estimates that 70% to 90% of enterprise data is unstructured, posing a significant challenge for organizations that need to unlock its potential using AI and also mitigate the risks of poor information governance. Document management platforms are critical to enterprise application strategies that require AI-ready, unstructured data (aka enterprise content).
IT Infrastructure and IoT refers to the products and services that support the deployment, management, and optimization of core technology systems and connected devices across enterprise environments. This category includes markets that focus on enabling organizations to build and operate resilient, scalable, and intelligent infrastructure. It encompasses solutions for data center management, network infrastructure, and IoT connectivity—spanning on-premises, cloud, edge, and hybrid models.
Gartner defines metadata management solutions as applications to enable the collection, analysis and orchestration of metadata related to organizational data assets. These solutions enable workflow and operational support to make data easy to find, use and manage. They do this by collating metadata in any form from within its own application and third-party systems, and providing the ability to search, analyze and make decisions on the collated results. They also provide transparent cross-referencing over all related metadata, and derive insights from data (such as usage patterns and performance) through analysis of metadata to support a wide range of data-driven initiatives.
Gartner defines the service orchestration and automation platform (SOAP) market as encompassing solutions that empower organizations to manage and automate their entire technology stack, including workloads, workflows, resource provisioning and data pipelines. SOAPs empower infrastructure and operations (I&O) leaders to streamline and accelerate the delivery of business services. These platforms integrate workflow orchestration, workload automation and resource provisioning across an organization’s hybrid IT landscape. By automating and optimizing these processes, SOAPs enable organizations to rapidly deploy workloads, enhance operational efficiency and achieve significant cost savings while ensuring high availability and business continuity. SOAPs enhance traditional workload automation by supporting use cases in data pipelines, cloud-native infrastructures and application architectures. They complement and integrate with DevOps toolchains, enabling organizations to achieve customer-centric agility, reduce costs, improve operational efficiency and establish standardized processes across their entire IT landscape.
Gartner defines technical debt management tools as software solutions that analyze source code, architecture and dependencies to identify, visualize and prioritize technical debt, structural flaws, and security risks. Delivered via SaaS or self-managed models, these solutions utilize static and dynamic analysis — often augmented by AI — to provide actionable insights for remediation, automate code refactoring, and accelerate cloud migration or modernization initiatives. Technical debt management tools provide a structured environment for identifying, measuring, and monitoring the costly structural and security compromises within software applications. These tools offer automated analysis at both the code and architectural levels, effectively revealing risks such as defects, “code smells,” dead code, and architectural drift from established best practices. By abstracting the complexities of manual code reviews and dependency mapping, technical debt management tools enable product teams to maintain long-term delivery speed and application quality. The technical debt management market reflects a consolidation of technologies across static and dynamic analysis, software composition, architecture observability to streamline the remediation process. While AI code assistants are increasingly effective at remediating code-level debt, the market is shifting toward managing architectural technical debt — debt that cuts across multiple systems or architecture layers, which is expected to account for 80% of all technical debt by 2027. These tools are essential for businesses aiming to achieve excellence in software engineering and prevent the “breaking point” where accumulated debt leads to unstable performance and soaring maintenance costs.