Generative AI is fundamentally shifting how software is developed and maintained. What began as discrete copilots for code suggestions has evolved into a more mature and integrated layer of AI-powered software engineering. Generative AI is now embedded across the entire development value chain – from requirement gathering and prototyping to code generation, testing, documentation, and deployment – reshaping development workflows and enabling asynchronous, agent-assisted collaboration. As enterprises move from experimentation to execution, generative AI solutions are expanding from point tools to full-stack development platforms, offering intelligent support for decision-making, team collaboration, and engineering governance. Increased investments in contextual intelligence, orchestration, explainability, and secure AI integration are driving this shift. In this report, Everest Group assesses 21 leading generative AI solution providers across four segments: Luminaries, Fast Followers, Influencers, and Seekers, based on their market performance and ecosystem positioning. The report provides insights into how these providers are enabling practical value, improving software delivery speed, and redefining developer experiences in the age of generative AI. Scope Geography: global Industries: all industries adopting software development practices Focus: generative AI solutions in software development, covering providers of copilots, intelligent agents, development platforms, and contextual coding tools Contents In this report, we: Position 21 technology providers offering gen AI products for software development on Everest Group’s Innovation Watch Assessment framework Assess the providers’ gen AI capabilities for software development Examine key business challenges and gen AI’s role in overcoming them

Software product management is evolving rapidly as organizations push for faster innovation, sharper customer focus, and measurable business outcomes. Product managers are no longer just roadmap custodians; they now orchestrate complex workflows, integrate fragmented toolchains, and lead in navigating rising delivery expectations. Their role now demands doing more with less, across an increasingly interconnected and high-pressure environment. Systems of Execution (SoE) represent a new operating paradigm that combines AI, automation, and orchestration to streamline execution across the product lifecycle. By linking insights to decisions and decisions to actions, SoE empower product teams to act with real-time intelligence and remain closely aligned with strategic goals. In this Viewpoint, we examine product managers’ evolving responsibilities, the systemic challenges constraining modern product organizations, SoE’s transformative potential, and how leading enterprises are adopting SoE to improve delivery speed, cross-functional alignment, and business impact. Scope Industry: all Geography: global Contents In this viewpoint, we examine: The product management industry’s evolution and its key challenges SoE as a paradigm that unifies AI, automation, and orchestration for smarter execution How product organizations can implement SoE to streamline workflows, enhance speed, and stay aligned with business goals

As enterprises strive for greater adaptability, autonomous execution, and continuous optimization, they are reimagining application development. Traditional approaches – focused on static requirements, linear workflows, and manual oversight – can no longer keep pace with real-time business demands. Development teams must now build systems that not only deliver features but also respond intelligently to change, act independently, and learn from every outcome. Systems of Execution (SoE) offer a new blueprint for application design – one that integrates AI, automation, event-driven architecture, and adaptive workflows to create intelligent, self-optimizing applications. By enabling real-time sensing, autonomous decision-making, and dynamic orchestration, SoE-ready applications fundamentally redefine enterprise responsiveness and resilience. This Viewpoint examines application development’s evolution in the context of SoE. It explores the architectural principles behind SoE-ready systems, the foundational investments enterprises must make, and real-world case studies demonstrating how leading organizations are unlocking value through agentic, context-aware applications. Scope Industry: agnostic Geography: global Contents In this viewpoint, we examine: SoE-ready application development The required technology investments A pragmatic roadmap for enterprise leaders to execute an SoE strategy How SoE enable future-proof, resilient, and intelligent enterprises

Enterprises have long recognized application modernization as essential to achieving agility, resilience, and innovation. Yet despite significant investments in cloud, DevOps, and containerization, outdated execution models, siloed governance, and manual processes derail modernization efforts, failing to keep pace with dynamic business needs. Traditional modernization approaches treat transformation as a one-time initiative, rather than a continuous capability, resulting in costly delays, limited scalability, and persistent technical debt. The report introduces Systems of Execution (SoE) as a transformative paradigm to application modernization, embedding perception, reasoning, execution, and learning into a self-optimizing, autonomous modernization execution mechanism. SoE-enabled modernization facilitates real-time decision-making and execution through intelligent agents powered by telemetry, AI, and policy-as-code. By shifting from static modernization programs to intelligent systems of execution, enterprises can reduce risk, accelerate outcomes, and continuously align IT with evolving business priorities. This Viewpoint provides a detailed roadmap to implement SoE-enabled modernization for enterprises, covering foundational system architecture, phased adoption strategy, and real-world examples from early enterprise adopters. It also outlines key strategic imperatives for both enterprises and providers and serves as a strategic guide to operationalizing SoE-enabled modernization at scale to close the long-standing execution gap prevalent in current application modernization strategies. Scope All industries and geographies Contents In this report, we examine The execution gap in traditional modernization approaches SoE’s role in transforming application modernization execution A phased implementation roadmap for enterprises adopting SoE-enabled modernization The core components of an SoE-enabled modernization architecture Real-world examples of agentic adoption Strategic imperatives for enterprises and providers

As small and medium-sized enterprises, particularly those with annual revenues below US$5 billion, accelerate their digital transformation journeys, custom application development is becoming increasingly essential in helping them remain agile, competitive, and responsive to evolving market demands. Unlike off-the-shelf or low-code solutions, custom application development enables mid-market enterprises to build scalable, secure, and tailored applications that address specific operational needs, industry challenges, and growth objectives. With leaner IT teams and tighter budgets than large enterprises, mid-market firms must ensure that every technology investment delivers tangible business value. Custom-built solutions help modernize legacy systems, streamline processes, and enhance customer and employee experiences—while offering control over feature prioritization, integration, and long-term scalability. As AI-powered tools and cloud-native platforms become more accessible, custom development is also becoming more efficient and accessible, empowering mid-sized firms to unlock intelligent automation, data-driven decision-making, and innovation at scale. In this report, Everest Group assesses 16 leading mid-market providers’ capabilities and market impact in the custom application development space, as part of its Custom Application Development Services PEAK Matrix® Assessment – Focused on Mid-market. The evaluation is based on a holistic review of each provider’s vision, delivery capabilities, proprietary assets, market success, and client feedback. This research aims to help mid-market enterprises identify the right-fit partners for their custom application initiatives, and equip providers with actionable insights to benchmark and refine their strategies in an increasingly competitive and dynamic market. Scope All industries and geographies Buyer size: small and medium-sized enterprises Service: custom application development services Contents This report features detailed assessments, including strengths and limitations, of 16 providers focusing on delivering custom application development services to small and medium-sized organizations.

As enterprises accelerate digital transformation, custom application development remains essential for them to address unique business needs, modernize legacy systems, and build differentiated digital experiences. Unlike off-the-shelf or low-code solutions, custom development empowers businesses to create scalable, flexible, and secure applications tailored to their specific requirements. The growing demand for personalized digital solutions, coupled with the emergence of AI-driven capabilities, is reshaping how enterprises approach application development – making it faster, smarter, and more aligned with business goals. In response, providers are enhancing their custom application development portfolios by building proprietary frameworks and accelerators, investing in AI-enablement, expanding global delivery capabilities, and nurturing deep domain expertise. Strategic partnerships and a strong focus on specialized talent are also key to delivering high-impact, enterprise-grade applications that drive real business outcomes. In this report, we assess 31 leading application development providers featured in the Custom Application Development Services PEAK Matrix® Assessment. Our comprehensive evaluation will help enterprises identify the right-fit partner for their development initiatives, while offering providers actionable insights to benchmark and improve their capabilities in a dynamic and competitive market. Scope All industries and geographies Service: custom application development services Contents This report features detailed assessments, including strengths and limitations, of 31 providers focusing on delivering custom application development services.

Boomi is expanding its footprint in the evolving agentic AI space with the introduction of Boomi AI Studio, a comprehensive platform designed to help enterprises manage AI agents at scale. This solution offers enterprises a unified environment to design, govern, and orchestrate AI agents, addressing a growing challenge around agent sprawl. By enabling centralized oversight and enhanced control mechanisms, Boomi AI Studio empowers organizations to maintain performance and accountability across increasingly complex AI ecosystems. With the early access release of this platform, Boomi is signaling its long-term commitment to innovation in AI and positioning itself at the forefront of the agentic AI landscape. Scope All industries and geographies The report is based on Everest Group’s assessment of Boomi’s product launch Contents In this report, we: Outline agentic AI’s key issues and objectives Examine the themes that drive and inhibit demand Evaluate Boomi’s current positioning and its agentic AI offerings Analyze the new release’s strengths and improvement areas

As enterprises build and modernize cloud and on-premise applications, growing architectural complexities and limited IT landscape visibility are driving a greater focus on Application Management Services (AMS) to ensure smoother modern application operations. Enterprises want to reduce their AMS costs and improve productivity by adopting next-generation technologies such as generative AI and hyperautomation. They are increasingly bundling their run scope with services such as application modernization and infrastructure management to derive more value from their investments. To address these evolving enterprise priorities, providers are expanding their capabilities, strategically partnering with technology providers, and developing proprietary IP to offer differentiated services. Additionally, they are expanding delivery centers and investing in specialized talent to deliver tailored, high-impact solutions that align with enterprises’ unique needs. In this compendium, we provide detailed and fact-based snapshots of 31 global AMS providers featured on Application Management Services (AMS) – Provider Compendium 2025. Each profile offers a comprehensive picture of the provider’s operational overview, delivery presence, solutions and investments, and case studies. Scope All industries and geographies Service: AMS Contents In this report, we provide 31 global AMS providers’ detailed and fact-based snapshots and include: Each provider’s overview and revenue range Adoption by buyer size, industry, and geography Key investments and case studies Proprietary solutions and partnerships Membership(s) Application Services Sourcing and Vendor Management

As AI becomes a key business innovation and competitive advantage driver, enterprises must rethink how their IT stack supports AI adoption. Many organizations currently implement AI as a bolt-on solution rather than integrating it into their IT stack’s core. However, this approach limits long-term differentiation and business impact. To unlock AI’s full potential, enterprises must transform their entire IT operating model, ensuring that AI is seamlessly embedded across infrastructure, middleware, databases, applications, and data layers. This report explores the IT stack’s evolution in an AI-powered world, highlighting key enablers such as AI-driven infrastructure, advanced data engineering, and application modernization. It also examines system integrators’ essential role in helping enterprises navigate this transition. Beyond the technical aspects, the report touches upon broader IT operating model changes, including strategy and solution elements. By providing a comprehensive view of what it takes to build an AI-ready enterprise, this report serves as a strategic guide for IT leaders, decision-makers, and business executives. It offers actionable insights into modernizing the IT stack, leveraging system integrators, and preparing for the fundamental shifts required to sustain AI-driven innovation. Enterprises can use this knowledge to develop a robust AI adoption strategy, ensuring long-term business value and competitive resilience in an AI-first future. Scope All industries and geographies Contents In this report, we explore: IT stack’s future in an AI-powered world AI enablement through infrastructure, applications, and data engineering System integrators’ role in making an enterprise IT stack AI-ready IT operating model’s other elements that will need changes Memberships Cloud and Infrastructure Services Application Services Sourcing and Vendor Management

As enterprises integrate AI into their business processes, ensuring AI systems’ quality and reliability is a significant challenge. Quality Engineering (QE) is the cornerstone across the entire AI adoption lifecycle, from assessing AI readiness to ensuring robust implementation and scaling efforts. However, traditional QE functions struggle to address the unique AI-specific assurance challenges, such as data bias, ethical considerations, and continuous performance monitoring. In this report, we explore how enterprises can evolve their QE capabilities to address AI systems’ complexities and ensure successful AI adoption. The report outlines a transformative framework, Quality@360⁰, focusing on advancing the skillset, mindset, and toolset needed for end-to-end AI assurance. Enterprises that proactively adapt their QE functions to meet AI’s demands will realize increased value from their AI initiatives and achieve long-term business success. Scope All industries and geographies Contents In this report, we: Examine the current enterprise AI adoption status Analyze the strategic confluence of AI and the quality function Assess levers to evolve the quality function to take on the AI assurance mandate Memberships Application Services Sourcing and Vendor Management