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Autonomous commerce is no longer a distant prospect; it is actively transforming procurement systems, digital marketplaces and B2B revenue platforms today. Intelligent systems can now interpret buying intent, evaluate margin risk and negotiate terms with increasing sophistication. Yet, according to ISG’s State of Agentic AI Market Report, only 25% of artificial intelligence (AI) solutions operate fully autonomously, while 45% still rely on human oversight. This cautious pace highlights a critical leadership challenge: organizations that delay adoption risk falling behind in efficiency and revenue growth, while premature or poorly governed autonomy threatens brand reputation and customer trust. ISG asserts that through 2027, providers will develop agentic AI capabilities to automate much of the revenue life cycle for renewal and expansion offers, improving the customer experience and resulting in increased lifetime value.

There is a shift underway in how enterprises structure their software stacks in relation to contact centers. Multiple factors have coalesced to reduce the importance of the traditional call handling infrastructure (i.e., the ACD) as the foundational element of the tech stack. It no longer has to be the focus of buying or deployment decisions. It’s been superseded by a more extensible (but complex) combination of voice and data network providers, Communication-Platform-as-a-Service (CPaaS) tools and cloud-based enterprise platforms. Contact centers also need systems to control the movement and use of customer data, and this is affecting buying decisions.

Over the past two years, I’ve led and operated as a practitioner in a sustained, feels-like-a-seven-day cadence to build what I believe is the most comprehensive enterprise software research foundation in the industry. Joining ISG after two decades of being a CEO and Chief Research Officer running a specialized research and advisory firm—Ventana Research—the new mandate was clear: expand software coverage more than 5x from 25 to 125+ categories and industrialize delivery through the ISG Buyers Guides, now accessible in concise, readable and listenable formats. That mission is complete: we’ve scaled operations across more than five consecutive quarters and expanded to a highly efficient global research engine that consistently delivers breadth, depth and enterprise-grade perspective.

The AWS ecosystem in the U.S. is undergoing a significant transformation, with service providers increasingly focusing on delivering advanced, value-driven cloud capabilities. This evolution is marked by substantial investments in emerging technologies, particularly generative AI (GenAI), agentic AI and data analytics, alongside continued emphasis on large-scale cloud migration, modernization and enterprise application support. Providers are developing highly specialized, often industryspecific solutions, and deepening their strategic alignment with AWS to deliver measurable business outcomes for their clients. The market is characterized by a strong drive towards innovation, operational efficiency and a refined approach to cloud adoption, moving beyond basic infrastructure provision to holistic, AIinfused digital transformations.

Contact centers are rapidly evolving from traditional, transactional hubs focused on query resolution and support into strategic CoEs that play a pivotal role in driving business growth. This evolution is fueled by advanced technology that is redefining the scope and capabilities of contact centers, pushing enterprises to keep pace with the rapid industry changes. KPIs are swiftly shifting from simply measuring resolved queries to proactively identifying opportunities, strengthening client relationships and shaping brand perception. As contact centers ascend the value chain, they are becoming essential drivers of loyalty, innovation and long-term business success.

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.

In the U.S., enterprise service management (ESM) is increasingly seen as a strategic enabler of digital business, with a strong emphasis on integrating human-centered design, intelligent automation and digital strategy. This approach aims to transform ESM into the connective tissue of the modern enterprise that is adaptive, anticipatory and persistently value-driven. It has emerged from the shadow of ITSM and is increasingly recognized for its scope of functionality and strong value offered. The discipline helps enterprises speed up digital transformation from 2.0 (where they are and what they know) to 3.0 (where they aspire to be). ESM is driving this by enabling enterprises to bring traditional IT operations, disparate business services (such as CRM, inventory) and cross-functional services (HR, finance, facilities management and more) into a single, orchestrated service environment. Service providers have made impressive gains in this regard as they have notably improved their ESM architecture vision to enable business value. This mature approach to delivering business value has strengthened providers’ ability to engineer core elements that support continuous AI integration and support, multicloud execution and other features of modern systems. This fits well with how many U.S. enterprises are trying to transform their operations.

The global medical devices sector is at an inflection point in 2025, where the convergence of technology, regulation and patient expectations is reshaping how products are designed, approved and monitored. The industry is experiencing heightened digital adoption across three critical areas: digital engineering and product development; regulatory compliance, strategy and quality assurance; and post-market digital enablement.

In 2025, the U.S. public sector — especially state, local and education (SLED) organizations is undergoing a profound transformation in how digital workplace services are conceived, procured and implemented.

Including AI and related technologies has paved the way for reinventing the entire ADM lifecycle to explore possible methods and approaches to delivering services that realize significant benefits for both enterprises and service providers. These benefits realized have introduced new scenarios that they must consider; hence, they have reconsidered contractual terms to ensure that these scenarios are considered for mutual benefit.

In 2024, GenAI was a promising disruptor. In 2025, it is a foundational capability. U.S. enterprises are no longer piloting GenAI. They are embedding it across workplace services, from AI copilots and agentic automation to predictive analytics and immersive collaboration. According to ISG’s analysis of public research, over 75 percent of large enterprises use GenAI in at least one business function, with workplace services among the most active domains.

ESM is a nascent software category with extreme variability in how vendors approach the discipline and the functionalities offered in their software. A true ESM solution sits atop the enterprises’ technology stack and manages the ITSM system and all other enterprise applications and business systems, including ERP, CRM, finance, sales, OT big data CloudOps and more. Most packaged ESM software solutions are not at this level today, but they do provide the pathways to achieve it. How well developed their pathways toward an integrated environment are, differentiates ESM software vendors.

The digital workplace software market has undergone a significant transformation since FY24, driven by the convergence of hybrid work models, AI innovation and heightened expectations for EX. The post-pandemic normalization of remote and hybrid work has solidified the need for robust digital workplace infrastructures that support productivity, collaboration and security across distributed environments.

There’s a subtle but important truth I’ve seen play out across dozens of organizations: HR software doesn’t define your strategy—but it will expose whether you have one. Too often, organizations treat the HR software stack as a series of independent decisions: a new ATS here, a learning platform there, maybe a refreshed core HRIS every few years. Each decision is justified on its own. Each serves a real need. But when you step back, the result is a patchwork of applications that don’t align, don’t communicate and don’t reinforce a coherent strategy.

Enterprises are accelerating digital initiatives powered by APIs, services, IoT and agentic artificial intelligence (AI), expanding non-human identity (NHI) risk faster than legacy Identity Access Management (IAM) and Privileged Access Management (PAM) can govern. CIOs, CISOs and IT leaders need an identity strategy that treats NHIs as first-class identities, integrates with IAM/PAM and advances enterprise security posture management. Purpose-built NHI software provides visibility, lifecycle automation and least privilege across machine identities, reducing credential exposure and compliance gaps. This analyst perspective explains why specialized NHI solutions are required to complement IAM and unify governance across human and non-human identities.

There's a saying among military historians that amateurs debate strategy while professionals talk about logistics. In a similar vein, those rooted deeply in the workings of information technology (IT) pay close attention to data because it is fundamental to the proficiency and effectiveness of any IT system. Visionaries speak of data being the oil or gold of the information economy; practitioners recognize that it is the structural steel. Potentially, one of the unexpected benefits of the artificial intelligence (AI) revolution may be the ability and willingness to address the decades-long problems of data quality and availability. More powerful data management tools and the motivation to invest in and use them are likely to significantly reduce (but by no means eliminate) these issues. This, as much as the power of AI—in all its forms—to streamline and even automate business process execution, will lead to a step-function increase in the productivity of the Office of Finance.

Collaborative Supply Chain Forecasting (CSCF) is a business discipline involving multiple supply chain stakeholders, including suppliers, distributors and sellers, that is aimed at producing a more accurate forecast of future demand and supply. Participants share supply and demand data, forecasts, assumptions and insights to improve visibility, reduce inefficiencies and attenuate supply-demand mismatches. ISG asserts that by 2028, one-third of larger enterprises will have adopted some form of CSCF to reduce costs and improve customer service and supplier relations. However, there are serious challenges to widespread adoption.

The emergence of natural language analytics interfaces driven by generative artificial intelligence (GenAI) models has accelerated enterprise initiatives to enable data democratization—making data available to business decision-makers without the need to train them to use business intelligence (BI) tools. It has also heightened the need for agreed semantic models and business metrics, as well as technologies that facilitate the sharing and consumption of data as a product. As I previously discussed, data as a product is the process of applying product thinking to data initiatives to ensure the outcome—the data product—is designed to be shared and reused for multiple use cases across the business as it enables enterprises to streamline and accelerate the delivery of analytics and artificial intelligence (AI) initiatives. The market for software that enables the design and delivery of data products is evolving rapidly, especially among providers of data catalog-based data intelligence software.

The idea of customer experience (CX) as a business practice relies heavily on being able to understand and track customer journeys. And that can be tricky because there isn’t a standard definition as to what a journey consists of. For example, inside a contact center the journey is perceived in terms of the steps a customer takes to achieve a particular result. Sometimes that is broadened out to include aspects of the customer’s history, especially when the business wants to use historical data to craft an offer or change a tactic.

The advances in data, analytics, Generative AI (GenAI) and agentic AI are transforming how healthcare and life sciences (HCLS) organizations deliver care, accelerate research, manage operations and engage patients. These innovations are driving measurable improvements in efficiency, accuracy and patient outcomes; however, adoption is not without challenges. Regulatory complexity, talent shortages, operational pressures and the need for responsible AI use demand thoughtful strategies. Organizations that can simultaneously innovate, scale responsibly and work closely with analytics partners to translate analytics goals into tangible outcomes will be the ones to succeed.