Enterprises are shifting from traditional project-based models to product-aligned operating models that enable speed, agility, and better alignment with customer value. This shift demands more than structural adjustments; it requires a fundamental redesign of how organizations operate across teams, processes, talent, and technology. This Everest Group Viewpoint introduces the Reinvent, Redefine, Reshape, and Reimagine 4R framework to guide enterprise-wide transformation: Reinvent organizational design to enable cross-functional, outcome-driven teams Redefine processes for iterative, value-centric delivery Reshape talent through competency-based, product-oriented roles Reimagine technology as a scalable, modular platform that powers rapid innovation This report outlines how organizations can dismantle silos, streamline governance, and empower teams with the autonomy and tools needed to deliver sustained business impact. It addresses common roadblocks, such as legacy systems, fragmented accountability, and skill mismatches, while offering ways to achieve measurable improvements in time-to-market, operational efficiency, and stakeholder satisfaction. The report is a pragmatic guide for business and technology leaders seeking to modernize operating models and build resilient, product-driven enterprises.

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

Organizations are grappling with ensuring AI systems’ quality and reliability while keeping pace with rapid technology advances and evolving regulatory requirements. As AI adoption grows, enterprises need Quality Engineering (QE) partners that can validate AI models, address AI-specific testing challenges, and embed responsible AI principles throughout the development life cycle. Providers must combine traditional testing methodologies with specialized AI frameworks and tools to ensure compliance with emerging regulations and ethical standards. Mid-market enterprises, particularly, are strengthening their QE foundations while exploring innovative technologies such as generative AI. They seek trusted advisors in strategic partners that can deliver superior technical solutions. These organizations prioritize agile, cost-efficient, and relationship-driven providers that focus on measurable business impact and RoI. Providers with advanced AI testing frameworks, and expertise in detecting biases, explaining models, and monitoring performance are uniquely positioned to support these enterprises. By offering inventive solutions, and strong advisory capabilities, they can help mid-market organizations accelerate their QE transformation, ensuring operational excellence and long-term competitiveness in an evolving market. In this compendium, we provide detailed and fact-based snapshots of 41 global QE providers featured on Quality Engineering (QE) Service – 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: QE Contents In this report, we provide detailed and fact-based snapshots of 41 global QE providers and include: An overview of each provider and their revenue range Adoption by buyer size, industry, and geography Key investments Case studies Proprietary solutions and partnerships Membership(s) Application Services Sourcing and Vendor Management

Rapid generative AI adoption across enterprise functions presents diverse opportunities and significant challenges. Organizations recognize clear pathways to enhanced productivity and new revenue streams, yet most deployments remain cautious and limited, yielding minimal rewards. Despite widespread generative AI risk awareness, most enterprises are unprepared to manage these effectively. However, enterprises with mature risk management strategies achieve 25% higher rewards compared to those who do not have similar risk management maturity, highlighting the vital link between risk management capability and business value. In this report, we examine how enterprises can move beyond cautious experimentation to realize substantial returns from their generative AI investments. The report explores the quality function’s role in assuring AI applications across layers and adoption stages. This research benefits enterprises seeking to build robust risk management strategies while scaling their AI initiatives responsibly and sustainably. Scope All industries and geographies Contents In this report, we: Examine generative AI adoption’s current enterprise status Recommend a roadmap for managing generative AI adoption risks Analyze the approach for the quality function to assure generative AI rewards Memberships Application Services Sourcing and Vendor Management

Organizations are grappling with ensuring quality and reliability in their AI applications and systems while keeping pace with rapid technology advances and evolving regulatory requirements. As AI adoption increases, enterprises need Quality Engineering (QE) service partners that can not only validate AI models and systems but also embed responsible AI principles throughout the development life cycle. Organizations seek providers that understand AI testing’s unique challenges and can blend traditional testing approaches with AI-specific methodologies while remaining compliant with emerging AI regulations and ethical guidelines. Providers offering comprehensive AI testing frameworks, specialized validation tools, and deep expertise in detecting biases, explaining models, and monitoring performance are uniquely positioned to support enterprises in their AI quality assurance journey. These providers must demonstrate technical proficiency in AI testing and a strong understanding of domain-specific AI applications, regulatory requirements, and ethical considerations. In this report, we assess 21 providers featured on the Quality Engineering (QE) Services for AI Applications and Systems PEAK Matrix® Assessment 2024 and categorize them as Leaders, Major Contenders, and Aspirants based on their capabilities and offerings. The study will enable buyers to choose the best-fit provider based on their AI testing requirements, while providers will be able to benchmark their performance against each other. Scope All industries and geographies Service: QE Contents This report features detailed assessments, including strengths and limitations, of 21 providers focusing on delivering QE services for AI applications and systems. Membership(s) Application Services Sourcing and Vendor Management

Mid-market enterprises are trying to strengthen their Quality Engineering (QE) foundations while simultaneously exploring fresh frontiers through cutting-edge technologies such as generative AI. In an ever-evolving market landscape, they want to engage with a provider capable of delivering superior technical implementations and also serving as a true strategic partner. While these enterprises want to focus on excellence and innovation when selecting their provider, they also do not want to be lost in a sea of deals when engaging with larger providers. In their pursuit, mid-market enterprises want partners that are relationship-driven, cost-efficient, agile, nimble, and committed to delivering business impact and return on investment at every step of the transformation. Providers with inventive solutions, accelerators, and strong advisory capabilities can efficiently guide these enterprises through their QE transformation journey. In this report, we assess 32 providers featured on the Quality Engineering Services for Mid-market Enterprises PEAK Matrix® Assessment 2024 and categorize them as Leaders, Major Contenders, and Aspirants based on their capabilities and offerings. The study will enable buyers to choose the best-fit provider based on their sourcing considerations, while providers will be able to benchmark their performance against each other. Scope All industries and geographies Service: QE Contents This report features detailed assessments, including strengths and limitations, of 32 providers focusing on delivering QE services to mid-market enterprises. Membership(s) Application Services Sourcing and Vendor Management

Generative AI’s adoption pace has been extraordinary. The technology’s advanced cognitive capabilities and ability to understand nuanced situations to generate context-aware outputs mark a significant leap in the intelligent systems space. Generative AI’s adoption is also due to its distinctive out-of-the-box accessibility, which grants enterprises effortless access to generative AI systems. However, the technology’s unchecked use has risks. Improper adoption can lead to confidential data leakage, IP violations, regulatory issues, and quality risks, such as model performance issues, biased outputs, and integrity concerns. In this viewpoint, we explore how pivotal a role quality function must play to assure enterprise generative AI adoption journey. We examine the role of quality function in mitigating all the associated adoption risks. This report benefits enterprises who wants to understand this strategic confluence of generative AI and quality function. Scope All industries and geographies Contents In this report, we examine: Generative AI adoption’s current status among enterprises The strategic confluence of generative AI and the quality function Anticipated developments in this space in 2024 and beyond Membership(s) Application Services Sourcing and Vendor Management

Over the years, there has been a growing emphasis on enhancing both the quality of software and the processes used to build it. This shift has prompted enterprises to transition from Quality Assurance (QA) to Quality Engineering (QE), linking the outcomes of the quality function with business results. The increased adoption of newer technologies such as generative AI underscores the importance of understanding their implications across processes, people, and technology, as well as the new opportunities they present for the quality function. Generative AI use cases are rising across the Software Testing Life Cycle (STLC) and in quality interventions for generative AI applications/systems. On the supply side, major participants such as Microsoft, Google, and Meta are investing aggressively to dominate the generative AI landscape. Additionally, there have been investments from leading QE-specific technology providers such as Copado, Katalon, QuerySurge, and Tricentis as well. This presents an opportune moment for enterprises to understand how the quality function can be a game changer in their generative AI journey. In this report, we examine generative AI’s potential in QE and how QE can facilitate smoother adoption of generative AI in the IT landscape. Scope All industries and geographies Market segment: QE services Sources leveraged: analyst inputs, Everest Group research (covering recruiters, industry experts, and industry associations), ongoing interactions with enterprises and providers, and publicly available secondary data sources Contents In this report, we: Analyze QE market size (split across geographies and industry verticals) Examine QE buyer trends across various geographies, industry verticals, and revenue sizes Identify key trends that are shaping the QE market Membership(s) Application Services Outsourcing Excellence

Enterprises pursuing digital transformation are exploring cutting-edge next-generation technologies to gain a competitive edge, drive business model innovation, and expand their operational capabilities. However, to achieve the desired benefits without disrupting their existing operations, implementing comprehensive Quality Engineering (QE) processes for these next-generation technologies is vital. To assist enterprises in their journey toward quality transformation through the adoption of next-generation technologies, providers are investing in innovation and enhancing their capabilities in areas such as cloud, AI, IoT, blockchain, and extended reality. In this report, we assess 35 next-generation QE service providers featured on the Next-generation Quality Engineering (QE) Services PEAK Matrix® 2023 and categorize them as Leaders, Major Contenders, and Aspirants based on their capabilities and offerings. Each profile offers a comprehensive picture of the provider’s vision and strategy, scope of services offered, enterprise adoption, investments, partnerships, case studies, innovative solutions, and strengths and limitations. Scope All industries and geographies The assessment is based on Everest Group’s annual RFI process for the calendar year 2023, interactions with leading next-generation QE service providers, client reference checks, and an ongoing analysis of the QE services market Contents This report features 35 next-generation QE service provider profiles and includes: A summary dashboard – market impact and vision & capability assessment Providers’ strengths and limitations An overview of providers’ QE services business – vision, scope of services offered, and adoption across enterprise segments and geographies Key QE services case studies An overview of providers’ QE services investments, key solutions, and partnerships Membership(s) Application Services Sourcing and Vendor Management

In today’s dynamic business landscape, the enterprise technology landscape is rapidly evolving, with software products taking center stage. The need for faster time-to-market is driving frequent releases and the adoption of both shift-left and shift-right approaches. Heightened importance is now placed on Quality Engineering (QE) as expectations for superior products soar. To support enterprises in their journey toward quality transformation, providers are channeling investments into innovation and enhanced capabilities. With such objectives, enterprises are partnering with providers that can understand their QE goals and suggest solutions that align with their complex technology ecosystem and processes. In this report, we assess 24 QE specialist service providers featured on the Quality Engineering (QE) Specialist Services PEAK Matrix® 2023 and categorize them as Leaders, Major Contenders, and Aspirants based on their capabilities and offerings. Each profile offers a comprehensive picture of the provider’s vision and strategy, scope of services offered, enterprise adoption, investments, partnerships, case studies, innovative solutions, and strengths and limitations. Scope All industries and geographies The assessment is based on Everest Group’s annual RFI process for calendar year 2023, interactions with leading QE specialist service providers, client reference checks, and an ongoing analysis of the QE services market Contents This report features 24 QE specialist provider profiles and includes: A summary dashboard – market impact and vision and capability assessment Providers’ strengths and limitations An overview of providers’ QE services business – vision, scope of services offered, and adoption across enterprise segments and geographies Key QE services case studies An overview of providers’ QE services investments, key solutions, and partnerships Membership(s) Application Services Sourcing and Vendor Management