Security operations are under unprecedented pressure. As enterprises expand across hybrid and cloud-native environments, their Security Operations Centers (SOCs) face an accelerating volume of signals, escalating threat velocity, and rising analyst fatigue. Despite significant investments in detection, analytics, and orchestration platforms, most organizations remain constrained by a fundamental execution gap – the disconnect between knowing what needs to be done and actually doing it at scale, speed, and governance. This report introduces Systems of Execution (SoE) as a next-generation architectural framework to transform how security operations are structured and delivered. SoE do not replace the SOC – they redefine it. By embedding AI-driven reasoning, policy-aligned automation, and real-time decision orchestration, SoE empower the SOC to evolve from reactive workflows to proactive, outcome-aligned execution. They act as the connective tissue across detection, triage, containment, and learning, translating intent into governed action across diverse systems, tools, and environments. Through detailed analysis and real-world insights, this report outlines how SoE address the long-standing fragmentation in security operations, highlighting their impact across five core SOC layers: ingestion, triage, decisioning, containment, and reporting. It explores how intelligent agents, explainable automation, and feedback-driven learning loops enable security teams to respond dynamically while preserving human oversight and trust. The study also examines the organizational implications of adopting SoE for enterprises and service and technology vendors. For enterprises, SoE represent a shift from tool-centric investments to outcome-centric resilience, where metrics such as mean-time-to-containment, autonomy readiness, and explainability define success. For providers, SoE introduce new delivery models centered on execution maturity and trust transparency. For technology providers, they signal a market pivot from automation to adaptive autonomy – demanding interoperable, auditable, and policy-aware solutions. By embedding intelligence and autonomy within operational governance, SoE offer a path toward a self-improving SOC – one that learns from every incident, acts with context, and scales securely across the digital enterprise. In doing so, SoE transform security operations from reactive detection to predictive, governed execution, positioning cybersecurity as a continuous enabler of enterprise trust, agility, and resilience.

The digital advertising landscape is at a turning point. Long-standing silos between AdTech and MarTech are eroding, giving way to a new, integrated ecosystem rooted in first-party data and real-time responsiveness. Three converging forces are driving this shift: intensifying privacy regulations that turn consent into a core requirement, the end of third-party cookies that renders traditional ID-based targeting obsolete, and the growing need for systems capable of subsecond decisioning. Together, these changes are redefining how enterprise advertisers and ecosystem participants engage audiences, measure outcomes, and create value. This Viewpoint examines the implications of these structural shifts across the AdTech ecosystem, from advertisers and platforms to supply-side partners. It outlines how cloud providers and hyperscalers are stepping in with alternative solutions, offering both compliance-friendly architectures and differentiated performance capabilities. This report also explores best practices, metrics to track, and key questions to pressure-test readiness and prioritize investments before providers in the ecosystem embark on AdTech transformation.

As AI becomes central to enterprise strategy, a striking reality is emerging: most organizations are limited not by their AI investments, but by their data foundation strengths. Fragmented systems, inconsistent governance, and siloed ownership continue to undermine AI success, leaving only a small fraction of enterprises truly ready to scale. This Viewpoint unpacks what it means to be data ready in the AI-first world. Drawing on a survey of 123 enterprises and in-depth data and AI leader interviews, it explores how trusted, high-quality, and well-governed data has become the defining factor separating AI ambitions from enterprise-wide adoption. The report introduces Everest Group’s seven pillars of data readiness spanning strategy, quality, accessibility, governance, foundation, culture, and data products and provides a practical framework to operationalize these capabilities across technology, talent, and governance dimensions. It also identifies the most common execution pitfalls and emerging risks that enterprises must navigate as AI adoption accelerates. By combining market data, best practices, and enterprise case study-based insights, the report offers business and technology leaders a clear roadmap to close the readiness gap and build resilient data

Enterprises are rapidly exploring agentic AI to create more autonomous, context-aware workflows. While initial efforts have focused on single-agent systems, these often fall short in adaptability, reliability, and scale. Multi-agent systems represent the next frontier, enabling specialized agents to collaborate dynamically across tasks and environments. But unlocking their full potential requires orchestration: the capability to coordinate diverse agents around shared objectives, execution patterns, and governance frameworks. This report provides a strategic overview of why orchestration is essential and how enterprises can architect intelligent, scalable agentic systems. Key focus areas include the layered architecture of multi-agent systems, orchestration strategies based on execution and control models, and the core building blocks – from perception handling and task decomposition to agent discovery and reasoning. It also explores emerging communication protocols such as MCP, A2A, and ACP that enable secure and scalable agent collaboration. Importantly, the report outlines policy enforcement, feedback loop, and governance mechanisms – ensuring enterprise-grade trust and resilience. Readers will gain a roadmap to deploy agentic intelligence that is both adaptable and aligned with business goals.

Cloud adoption remains a central transformational pillar for mid-market enterprises, which have shown notable resilience amid economic uncertainty. Unlike their larger counterparts, these firms are focused on rapid value realization, agility, scalability, and more personalized engagement models. Traditional, enterprise-centric cloud services often fail to meet these needs. In response, providers are reconfiguring their service delivery to offer tailored support through consulting, infrastructure modernization, cost-effective operations, and AI-enhanced managed services. This report explores how the cloud services landscape is adapting to meet mid-market demands. Providers are increasingly investing in their own IP, expanding technology portfolios, enhancing delivery capabilities, and forming strategic partnerships. Key trends shaping the market include rising multi-cloud adoption, verticalized service offerings, and generative AI integration into delivery models. The compendium offers a detailed view of how 24 leading providers are positioning themselves to meet these evolving requirements and support mid-market enterprise transformation initiatives.

The retail industry is at an inflection point, shaped by rapidly evolving customer expectations, omnichannel complexity, and mounting competitive pressures. This Viewpoint explores the need for retail enterprises to embrace comprehensive, AI-driven transformation of their customer experience (CX) strategy through an end-to-end CX approach. It focuses on the cohesive orchestration of traditionally siloed business functions, such as merchandising, marketing, sales, and supply chain, to deliver frictionless, consistent, and emotionally resonant experiences across both digital and physical touchpoints. The report highlights key enablers of this transformation, including an integrated CX technology architecture that unifies data across channels, a dynamic KPI framework to measure both experience and business impact, and a representative 360-degree customer insights dashboard for retail enterprises. It also underscores the strategic role of CXM providers and ecosystem partnerships in empowering retailers through innovative engagement models, AI-led personalization, and operational excellence that bridges human empathy with automation. Additionally, it outlines key factors to consider when selecting the right CXM partner. Ultimately, this Viewpoint presents a roadmap for retail enterprises to move beyond isolated experience enhancements toward a connected and intelligent customer ecosystem, where every interaction is optimized for value, empathy, and agility.

As enterprises confront the growing demand for instant responsiveness, data streaming is becoming an essential business agility and intelligence enabler. Traditional batch-based systems are giving way to zero-lag infrastructures that continuously process and act on data as it flows. From fraud detection and hyper-personalization to predictive maintenance and real-time supply chain visibility, streaming is moving from pilot projects to enterprise-wide transformation. This report examines the rise of streaming as a foundation for real-time intelligence. It provides a clear view of why enterprises are embracing data in motion, where adoption is gaining momentum across industries and functions, and how technology providers are responding with new capabilities and platforms. It also looks at the organizational and technical challenges that continue to limit scale and identifies practical best practices to build resilient, scalable streaming architectures that drive measurable business impact. By combining a structured overview of the ecosystem with insights into use cases, market dynamics, and scaling practices, this report equips business and technology leaders to reimagine how decisions are made in the moment, and how enterprises can move from being reactive to predictive in the digital era

Enterprises worldwide are racing to unlock AI-driven productivity, insight, and growth, but most still run mission-critical applications on legacy, siloed environments that cannot support modern AI pipelines. As priorities shift from cost takeout and cloud adoption to full AI readiness, Application Transformation Services (ATS) for AI enablement have emerged as a strategic imperative. This wave of transformation goes beyond incremental modernization, focusing instead on re-architecting application estates to unlock the true potential of AI. ATS for AI enablement is centered on refactoring legacy code, migrating to cloud-native, API-first architectures, and building governed, real-time data flows that can support gen AI copilots, agentic automation, and advanced analytics at scale. The market is now at an inflection point; enterprises that succeed in building scalable, AI-ready application stacks are beginning to pull ahead of peers, turning their technology architecture into a source of sustainable competitive differentiation. This report assesses 33 service providers in the Application Transformation Services for AI-enablement PEAK Matrix® Assessment 2025, categorizing them as Leaders, Major Contenders, and Aspirants. It serves as a practical guide for CIOs, CTOs, application engineering leaders, and sourcing teams to structure RFPs, evaluate partner fit, and prioritize investments, and it provides providers with clear benchmarks to calibrate strategy and offerings.

The global AI chip market is expanding rapidly. As AI workloads become more complex and large scale, enterprises are demanding higher performance, lower latency, and integration capabilities that go beyond traditional compute approaches. Chip-level decisions are now being treated as a strategic component of enterprise infrastructure, with organizations seeking to balance workload flexibility, ecosystem integration, and silicon-level optimization. At the same time, the provider landscape is diversifying rapidly. Hyperscalers, established providers, and emerging participants are all introducing differentiated architectures, toolchains, and delivery models – creating a dynamic environment that requires enterprises to rethink infrastructure strategies. This shift is reshaping expectations around how AI workloads are deployed and scaled, and how enterprises derive value from AI-native infrastructure. In this report, we present a strategic outlook on the AI chip market, analyze provider segments, adoption shifts, and enterprise priorities. The report leverages Everest Group’s AIMPACT framework to offer a practical playbook for evaluating, selecting, and operationalizing AI-specific chips.

Enterprises accelerating their transition from legacy systems to cloud-native operating environments now face an intensified need for specialized, cloud-ready talent to sustain large-scale modernization. Organizations now require deep expertise in cloud-native development, multi-cloud and hybrid cloud adoption, cloud orchestration, cloud security, cloud sustainability, and FinOps. High attrition, rising wage pressures, and the accelerating obsolescence of technical skills further intensify this challenge. In response, organizations are increasingly seeking IT providers with mature and forward-looking talent development strategies tailored to cloud services’ evolving needs. Providers are making proactive investments to build future-ready talent pipelines by combining traditional learning approaches, digital platforms, and hands-on training. They are also prioritizing certifications across AWS, Microsoft Azure, and Google Cloud Platform (GCP) to strengthen workforce credibility and readiness. In line with future-of-work principles, many are embedding AI readiness into their workforce strategies, ensuring teams are equipped to work alongside intelligent tools and automation. At the same time, they are focusing on integrating Gen-Z talent by reshaping workplace culture, communication, and learning experiences to align with their digital-native expectations. To sustain long-term capability, providers are also enhancing employee engagement and retention through personalized career paths, continuous learning opportunities, and inclusive, purpose-driven work environments. In this report, we assess 32 IT providers featured on the Talent Readiness for Next-generation Cloud Services PEAK Matrix®. The research will help buyers select the right-fit cloud provider for their needs, while cloud providers will be able to benchmark themselves against each other.