What You Need to Know about the Security Operations Center Market in Switzerland
Managed security services providers in Switzerland are seeing success with the security operations center (SOC) model.
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Learn MoreEnterprise 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.
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.
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.
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.
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.