Executive Insights for Supplier Intelligence

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. 

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.