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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.

Data governance is an issue that impacts all organizations large and small, new and old, in every industry, and every region of the world. Data governance ensures that an organization’s data can be cataloged, trusted and protected, improving business processes to accelerate analytics initiatives and support compliance with regulatory requirements. Not all data governance initiatives will be driven by regulatory compliance; however, the risk of falling foul of privacy (and human rights) laws ensures that regulatory compliance influences data-processing requirements and all data governance projects. Multinational organizations must be cognizant of the wide variety of regional data security and privacy requirements, not least the European Union’s General Data Protection Regulation (GDPR). The GDPR became enforceable in 2018, protects the privacy of personal or professional data, and carries with it the threat of fines of up to 20 million euros ($22 million) or 4% of a company’s global revenue. Europe is not alone in regulating against the use of personally identifiable information (other similar regulations include The California Consumer Privacy Act) but Ventana Research’s Data Governance Benchmark Research illustrates that there are differing attitudes and approaches to data governance on either side of the Atlantic.

Data governance is a hot topic these days. In fact, we are conducting benchmark research on the subject here. With increasing regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations face more external oversight of their data governance practices. The risk of significant fines associated with these and other regulations, coupled with organizations’ internal compliance requirements, has brought more attention to data governance practices. We anticipate through 2023, three-quarters of Chief Data Officers’ primary concerns will be governing the privacy and security of their organization’s data.

Let's be blunt: The pressure to adopt AI in HR is a panic button being hit by the C-suite. The mandate from the boardroom is clear, and the pressure is intensifying: HR must adopt AI to remain competitive. This directive often lands on the desks of HR leaders who are already managing complex environments, creating a dangerous disconnect between executive ambition and operational reality.

Revenue organizations are running out of room to hide. With increasingly complex buyer journeys, longer sales cycles and rising expectations for personalized outreach, today’s CROs face a mounting challenge: deliver predictable growth in a market that’s anything but predictable while simultaneously building a team that doesn’t burn out.

Requirements for sustainability reporting have been undergoing a political shift over the past year, including modifications to the EU’s reporting requirements. The difference between European and North American attitudes to sustainability as a priority, which were significant 15 years ago, began to converge toward the end of the ‘teens but now appear to be diverging. In the U.S. in particular, there have been legal challenges to state laws and a deregulatory emphasis at the Federal level. The scaling back of the scope and granularity of sustainability reporting is having its biggest impact on small and midsize establishments in the EU. Nonetheless, mandated reporting remains in place for larger enterprisers around the world, albeit to varying degrees. Moreover, there is still demand for enterprises to set and meet sustainability objectives to achieve increased operating efficiency and to associate their brand with responsible stewardship of the environment. For these enterprises, there remains the need to achieve efficient compliance with reporting mandates as well as to have processes and systems in place to set overall objectives and mechanisms to assign responsibility for meeting them across the enterprise.

ISG recently published the 2025 ISG Buyers Guides for DataOps, providing an assessment of 51 software providers offering products used by data engineers, data scientists, and data and AI professionals to facilitate the use of data for analytics and AI needs. The DataOps Buyers Guide research generated three reports and five quadrants assessing providers in relation to overall DataOps, Data Observability, Data Orchestration, Data Pipelines and Data Products. By providing an assessment of all software providers with tools in the portfolio of DataOps, the research offers a unique perspective on the extent to which emerging capabilities are being adopted by software providers. Given the amount of noise being made by providers about AI, it’s easy to assume that all providers have already delivered AI-driven capabilities that automate and accelerate DataOps use-cases. However, the DataOps Buyers Guide research illustrates that, for many providers, support for AI functionality remains a work in progress.

Agentic AI is moving from pilots to production systems that execute work across enterprise applications, data platforms and business processes. As I’ve argued before, the value of AI is realized in action, not just answers, and enterprises are investing accordingly. One of the key questions now is how to coordinate the actions among different agents. My colleague Matt Aslett’s perspective on Model Context Protocol (MCP) explains that software providers are quickly embracing MCP as a standardized way for models and agents to find and use trusted data. But context alone doesn’t orchestrate multi-agent workflows. That’s where Agent-to-Agent Protocol (A2A) comes in, enabling agents to discover each other, exchange capabilities and hand off work reliably. Together, MCP and A2A form complementary lanes for agentic systems to share information and coordinate actions.