ISG Research On-Demand

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. 

Enterprise IT leaders face a dual mandate: maintain resilient operations while accelerating digital outcomes. AIOps software, where artificial intelligence and machine learning (AI/ML) models and automation converge across observability, incident response and IT service management, has moved from experimental pilots to foundational capabilities in today’s operations. As CIOs, CISOs and IT leaders look to balance business performance with technical rigor, AIOps offers measurable gains in reliability, velocity and cost control while laying the groundwork for GenAI-enabled workflows. This Analyst Perspective describes the benefits, pitfalls and practical steps for adopting AIOps at scale, with insights for both enterprise buyers and software provider product teams. For a deeper dive, see the 2025 ISG Buyers Guide for AIOps Executive Summary, available for download.

The global insurance industry is at a pivotal juncture, driven by economic volatility, evolving policyholder expectations, regulatory changes and potential AI innovations. Insurers are navigating a landscape that demands agility, personalization and operational excellence. The increasing number of digital-native consumers, climate-related risks and cybersecurity threats are reshaping traditional business models, compelling insurers to rethink their enterprise designs and core strategies.

Public cloud platforms form the core of enterprise AI ecosystems, offering the scalability, elasticity and specialized infrastructure needed to train and deploy large models efficiently. ISG research shows that combining on-premises control with cloudbased acceleration enables organizations to integrate AI-powered intelligence into existing workflows and streamline their operations. Enterprises have been able to reduce development complexity, accelerate time to value and scale innovations from predictive analytics to autonomous operations by leveraging cloud-native AI services, pretrained models and GPU-optimized instances.

Digital engineering has become the core capability that helps incumbent insurance enterprises modernize legacy systems, accelerate innovation and deliver seamless policyholder experiences across multiple channels. As these enterprises face growing competition from insurtech, neo insurers and digital insurers, shifting policyholder expectations and increasing regulatory complexities, digital engineering services offer the technical architecture, development expertise and operational frameworks needed to transform outdated monolithic systems into flexible, cloud-native, API-driven ecosystems. This change goes beyond technology; it involves fundamental shifts in how insurance organizations design, develop, deploy and maintain digital tools that are increasingly essential for staying competitive.

The insurance industry is facing its most significant technological transformation since the onset of digitization. GenAI and Agentic AI fundamentally reimagine the way insurance carriers, brokers and managing general agents (MGAs) operate, engage with customers, assess risk and deliver value. These technologies have matured from experimentation stage into production-ready platforms, reshaping the insurance competitive landscape.

Amid economic volatility, ongoing inflation, talent shortage, technological changes and rising competition, incumbent insurance enterprises encounter unprecedented challenges that demand both cost optimization and capability growth. In this environment, providers offer models such as build-operatetransfer (BOT), build-own-operate-transfer (BOOT), build-operate-transform and transfer (BOTT), capability center as a service (CaaS) or global capability centers (GCCs) as distinctive solutions. These models represent structured approaches, delivering both short-term and operational gains alongside long-term strategic benefits, effectively addressing the complex, and sometimes conflicting, needs of insurers.

In the current business landscape, enterprises continue to face disruptions such as tariffs, wars, political unrest and climate shocks. Hence, it has become mandatory for supply chain leaders to look beyond temporary fixes and pursue larger transformations to gain a competitive advantage. A siloed or process specific uplift provides short-term relief, but building future-ready, connected, intelligent and sustainable supply chains requires a comprehensive revamp, driven by technologies such as AI, analytics, supplier relationship management (SRM) and automation. This year’s assessment highlights a major shift in chief supplier officers’ approach, focusing more on enhancing process optimization and operational efficiency than merely targeting cost savings to stay ahead of the competition.

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.