If you’re hunting for CX infrastructure research that actually helps you run a more reliable contact center, you’re in the right place. Reliability is no longer just an IT concern. It’s a customer trust concern. It’s a revenue concern. And, in many organizations, it’s the difference between a calm day and a full-blown “all hands on deck” incident. This curated roundup pulls together five high-signal CX technology reports and ITSM reports that explain what’s changing in service management maturity, downtime risk, and the infrastructure reality behind modern customer experiences.
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Report 1: Cisco 2025 Networking Research
Cisco’s research is extremely relevant to CX reliability because it tackles the environment CX now lives inside. That means more cloud, more AI workloads, more latency-sensitive traffic, and the business impact when the network or connectivity layer degrades.
What this report reveals
- Outages are common. The report states that 77% of surveyed leaders faced major outages in the past two years.
- Revenue impact is a top storyline. More than half of respondents said revenue was the business area most impacted by disruptions.
- Reliability is getting harder as AI ramps up. The report frames AI-driven traffic as faster, more dynamic, and more sensitive to latency and interruptions.
Why it matters for contact center reliability
Your CX stack can be “fine” while the customer experience is not. If network congestion, misconfigurations, security events, or routing issues spike, you get the classic symptoms: dropped calls, laggy agent desktops, slow CRM loads, and broken authentication flows.
This report is useful when you need to explain to non-technical stakeholders why reliability is not just “an app issue.” It’s the full delivery path.
Report 2: Atlassian “State of AI in Incident Management”
This report is a goldmine for anyone building the operational muscle behind CX reliability. As one of the key ITSM reports, it focuses on incident management behavior, maturity, and how teams are adopting AI and automation in incident workflows.
What this report reveals
- Proactive incident practices are rising. The report shows 75% of organizations are classified as “proactive responders" in 2025, compared with just 35% in 2020.
- AI is becoming part of incident discovery. It states 78% of respondents used AI to discover incidents.
- Visibility is still a major pain point. The report highlights lack of full visibility across infrastructure as the biggest pain point, and shows it increasing year over year in the survey.
- Core metrics still rule. MTTR, MTTA, and related response metrics remain central for measuring success after incidents.
Why it matters for CX operations management
Contact center failures are rarely single-system failures. They are chain failures. A change rollout meets an integration dependency meets a network issue meets a queue spike.
What this report does well is show that reliability is an operating loop: detect, diagnose, coordinate, resolve, learn. If your loop is weak, your CX reliability is luck-based.
Report 3: Gartner Market Guide for IT Service Management Platforms
This Gartner Market Guide is a useful benchmark for buyers. It outlines what ITSM platforms are, what to prioritize, and where the market is headed.
What this report reveals
- ITSM is still the go-to system of record for incidents, requests, changes, knowledge, service levels, and configuration management.
- Many vendors are chasing AI and non-IT workflows rather than strengthening core usability, reporting, automation, and integration.
- Total cost of ownership can vary widely, especially due to bundling, implementation, and admin overhead.
- The market is top-heavy. Gartner notes the top end of the market is dominated, with leading vendors holding a large share of market revenue.
Why it matters for CX reliability
Even if your contact center platform is modern, your reliability outcomes depend on how incidents and changes are managed across all dependencies.
This report helps buyers avoid two classic mistakes:
- Buying a platform for shiny AI features, then struggling with basic reporting and workflow adoption.
- Underestimating implementation and admin needs, then blaming the tool when maturity stalls.
