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What I observed at Enterprise Connect was the disappointing specter of an industry that is collectively missing the opportunity to clarify and define its own future. Despite incredible technological advancements, many companies are instead playing defense against threats that they can’t (or won’t) articulate.

The shift roster has always been a place where strategy becomes personal. It decides who works, when they work and how predictable their lives can be from one week to the next. When scheduling becomes more automated, the consequences show up immediately in worker sentiment, manager workload, overtime spend and service levels. That is why the rise of artificial intelligence (AI)-driven scheduling feels different than other HR technology shifts. It is not just another feature in the stack, because it touches time, fairness and trust all at once.

Artificial intelligence (AI) just became the operating system for enterprise security. That was the clearest signal coming out of the RSAC 2026 Conference, and it materially changes how CIOs and CISOs should evaluate security platforms, operating models and investment priorities over the next 12–24 months.

Global capability centers (GCCs) have reached a strategic inflection point. What began as cost arbitrage vehicles is now firmly established as enterprise-grade hubs for innovation, digital transformation and long-term competitive advantages. This ISG Provider Lens® study evaluates the service provider landscape supporting GCCs across two dimensions: design and setup and ongoing optimization and enhancement. The findings reflect a market in transition, where execution capability is rapidly becoming the defining measure of leadership and the basis on which provider relevance will be judged.

The digital engineering space has undergone a fundamental change, shifting from an isolated and segmented value chain automation to a holistic model that drives enterprise-wide business value. In the U.S., this evolution has led to an increasing demand for augmented design and R&D services to achieve siliconto- service innovation, intelligent operations and connected experience services to create autonomous, informed value loops, and integrated platform and application services to ensure AI-enabled platform development across silos by leveraging agentic AI architectures. These services elevate engineering from a back-office function into a primary enabler of enterprise-wide resilience and a measurable ROI engine in a digital-human hybrid economy.

AI is redefining healthcare operations, shifting the focus from isolated innovation to enterprise-scale transformation. What began as experimentation with GenAI is now evolving into structured, outcome-driven adoption across clinical, administrative and payer workflows. The market is entering a phase where value realization, governance and scalability matter more than experimentation. At the center of this shift is the emergence of agentic AI and workflow-embedded intelligence, enabling healthcare organizations to move from assistive AI to accountable, action-oriented systems.

This study shows the life sciences services landscape progressing beyond function-specific transformation toward value-chain-wide modernization, with IT service providers and CROs both repositioning around integrated, AI-enabled and platform-led delivery. Across clinical development, patient engagement, pharmacovigilance, regulatory functions for CROs, and manufacturing, supply chain and commercial operations for service providers, the shift is moving from isolated digital interventions to connected operating models that integrate data, workflows, compliance and outcomes.

The growing strategic importance of sales and operations planning (S&OP) reflects the confluence of two major trends evolving outside of enterprise operations. One is the ongoing disintegration of the post-World War 2 liberal trade environment that began in the early 2010s and has accelerated since. The second is the increasing sophistication and approachability of S&OP software. These applications are designed to align an enterprise’s sales and marketing objectives with its operating environment (including its production and distribution assets as well as its supplier ecosystem) while respecting financial constraints. The availability of more accurate and nuanced machine learning (ML)-based forecasting systems and the growing availability of agents that shorten planning and execution cycles will enable enterprises using software to better adapt to change, anticipate risks and evaluate trade-offs to determine the best course of action faster with greater intelligence.

There is a growing disconnect between what digital commerce platforms were originally built to do and what modern revenue teams now need them to deliver. Most traditional platforms were designed to manage storefronts, catalogs and transactions. They perform well when the goal is to display products, enforce static pricing and process payments. But in today’s revenue environment, the challenge is no longer limited to completing a transaction. It is about orchestrating revenue across multiple moments, touchpoints and decision makers over time. As organizations adopt recurring revenue models, usage-based pricing and faster deal cycles, commerce technology that cannot adapt to these realities becomes a constraint on growth rather than a driver of it.

The mission to enable an autonomous enterprise, as I have articulated, remains aspirational for most organizations. Few have aligned business and technology leadership, strategy and execution to support autonomous operations at any scale. A shared definition is essential. An autonomous enterprise follows an operating model in which intelligent systems can sense conditions, decide actions, execute outcomes and continuously learn, that is orchestrated across the business, with humans setting intent, governance and accountability.

The U.S. healthcare environment is undergoing a structural shift as economic pressure, demographic change and technological acceleration reshape how payers, providers and life sciences organizations operate. With healthcare already representing 17 percent of the region’s GDP and advancing toward a 20 percent share by 2032, rising medical demand is paralleled by an unprecedented expansion in technology investment. Aging populations and the growing burden of chronic disease are increasing healthcare consumption, while intensifying cost pressures are heightening the urgency for new digital operating models that reduce friction and enable more sustainable delivery.

The manufacturing industry is undergoing a seismic shift, driven by the convergence of digital technologies, sustainability imperatives and evolving customer expectations. As manufacturers strive to modernize legacy systems, integrate emerging technologies and build resilient supply chains, they face a complex landscape of challenges and opportunities. This transformation is not uniform across geographies or company sizes. Mid-tier and specialist IT firms must navigate a complex landscape to deliver scalable, costefficient and domain-specific solutions.

This report evaluates media and entertainment (M&E) managed and next-gen IT service providers in North America, focusing on those that deliver cloud modernization, content supply chain engineering, AI-driven workflows, data and analytics management, platform operations, adtech, rights management and next-gen digital experience architecture.

This report evaluates the rapidly evolving content supply chain management (CSCM) landscape, focusing on platforms that enable media enterprises to ingest, manage, produce, localize, distribute and analyze rich media content efficiently across digital and linear channels. It benchmarks leading providers based on their ability to offer end-to-end orchestration, AI-driven automation, cloud-native scalability and metadata interoperability. The scope excludes web-only content marketing platforms and creative agencies.

The report evaluates integrated operations support systems (OSS)/business support systems (BSS)/customer experience (CX) platform vendors against modular, API-first, cloud-native criteria to guide telco modernization decisions. OSS/BSS is shifting from transactional to customercentric, automation-driven ecosystems, anchored in TM Forum ODA/Open APIs, microservices, event streaming and GenAI for real-time monetization and assurance.

The oil & gas industry in the Americas is at a pivotal juncture. The convergence of digital transformation, energy transition and geopolitical dynamics is reshaping the industry’s future. Companies are responding with strategic investments in technology, talent and sustainability, positioning themselves for long-term resilience and growth.

The term “sovereign AI and data” became increasingly prevalent in recent years, initially driven by cloud infrastructure providers responding to the need to support regional regulations with sovereign cloud offerings. However, the use of sovereign cloud infrastructure is not required to deliver compliance with data sovereignty regulations. In fact, our research indicates that having the autonomy to choose between different architectural options is increasingly important for enterprise sovereign AI and data strategies.

Threat modeling software has emerged as a distinct security category focused on identifying and mitigating design-level risks early in the software development lifecycle (SDLC). Unlike vulnerability scanners or runtime security controls that surface issues after deployment, threat modeling tools operate upstream, enabling organizations to analyze system architectures before code is written or released. For enterprise CIOs, CISOs and IT leaders balancing delivery velocity with risk exposure, this category supports a measurable shift-left security strategy grounded in design-time decision-making.

Enterprise customer experience (CX) usually focuses on the moment of communication between a customer and a business, assessing the customer’s state of mind and the outcomes of specific, limited interactions. But there’s another important context that drives customer behavior, and that’s the on-site experience of technical support. Field service is often out of the line of sight of CX planners, but it shouldn’t be.

Agentic AI is emerging as a transformative force that redefines how organizations think, decide and act. Unlike traditional automation or GenAI, agentic AI systems are designed to autonomously execute business processes, dynamically pursue goals and collaborate across workflows. This shift to agentic AI marks a new chapter in enterprise intelligence, where decision velocity, contextual awareness and orchestration become the cornerstones of competitive advantage. Agents are capable of breaking down objectives into smaller tasks, planning execution strategies, interacting with multiple applications, collaborating with other agents and adapting to feedback. In this sense, agentic AI is designed to function more like a digital employee than a static tool. Although still an emerging market, with experimentation outpacing scaled adoption, agentic AI has already begun to shape the future of how organizations think about productivity, decision-making and business transformation.