In a recent conversation between leaders from SolarWinds and IDC, IT leaders across the region described this as the “cost of complexity” in a world where tool sprawl, data overload, and skills gaps quietly drain budgets and attention away from real innovation.
From AI Experiments to Ai-Fueled Operations
Across APJ, enterprises are moving from one-off experiments to AI woven into daily business operations. Yet the foundations are straining under the weight. IDC research shows that while about 59% of organizations are investing in AIOps solutions, roughly 75% of planned IT spending in 2025 is still tied up in “keeping the lights on” instead of driving innovation.
That tension shows up in familiar ways:
- Tool sprawl: Many IT teams juggle half a dozen or more monitoring and observability tools, each creating new silos while trying to solve old problems
- Data overload: 30% of organizations say rising data volumes and diversity are now a primary barrier to digital transformation and effective IT operations
The net effect is a high cost of complexity, where chief information officers (CIOs) allocate increasing budgets to maintaining a fragmented stack while business leaders ask why AI isn’t delivering the promised value.
What Is Aiops vs. DevOps?
DevOps and AIOps serve complementary but distinct roles in modern IT. DevOps focuses on accelerating software delivery by improving collaboration between development and operations teams; emphasizing continuous integration and continuous delivery; and automating build and deployment pipelines. AIOps, by contrast, applies AI and machine learning to IT operations data to detect anomalies, identify root causes, predict incidents, and automate remediation.
In short, DevOps helps organizations build and release software faster, while AIOps helps them run complex hybrid environments more intelligently and resiliently at scale.
What Are the Four Key Stages of Aiops?
AIOps typically evolves across four key stages:
- Data aggregation, where telemetry from infrastructure, applications, networks, and users is collected and unified
- Analysis and correlation, which involves using AI and machine learning to detect patterns, reduce noise, and identify anomalies across systems
- Insight and root cause identification, where the platform surfaces probable causes and recommends remediation steps
- Automation and optimization, where guided or autonomous actions are executed to resolve incidents, prevent recurrence, and continuously improve operational performance
The Cost of Complexity: Drowning in Data, Starving for Insight
No CIO walks in on Monday and says, “My environment is simpler than it was last year.” Hybrid and multi-cloud adoption has given teams more flexibility, but also more integration points, telemetry streams, and ways for incidents to ripple across the stack.
In the executive conversation, SolarWinds leadership summed it up bluntly: teams are “drowning in data but still starving for insight,” often managing from six to 16 different tools, each solving one problem while creating ten others. The IDC InfoBrief, “From Insight to Action: Enterprise Observability Powered by AI and Automation,” backs this up. 39% of enterprises now explicitly prioritize reducing tool sprawl as part of their cloud-native and observability strategy.
That complexity frustrates engineers while draining budgets.
- High implementation and maintenance costs for overlapping tools strain operating expenses
- Fragmented views slow down incident response and make it harder to prove the value of IT to the business
The question isn’t “do we have enough data?” It’s “can we turn that data into decisions fast enough to matter?”
From Visibility to Understanding: Observability That Changes Outcomes
Most organizations started their journey by chasing visibility—more dashboards, metrics, and logs. That was a necessary step, but it’s no longer sufficient.
The shift now is from seeing everything to understanding what it means for the business. In the webinar, the guidance to CIOs was clear: don’t chase visibility for its own sake; chase understanding—connecting what you see to what it really means for performance, resilience, and customer experience.
IDC data shows where leading teams are focusing:
- 43% of organizations identify AI-powered root cause analysis and guided remediation as the most valuable capabilities of AIOps tools
- Real-time observability and predictive incident prevention are rated critical for turning operations from reactive firefighting into proactive resilience
That means unifying signals from infrastructure, applications, networks, and users into a single operational narrative:
- Why is this spike happening now?
- What’s the real root cause across the stack?
- What’s the lowest-risk way to remediate before users feel it?
In practice, that’s where a platform approach to observability and AIOps, instead of a patchwork of tools, starts to pay off.
Agentic AI: From Cruise Control to Self-Driving It (With a Human in the Loop)
Automation got IT to “cruise control”; agentic AI is how IT moves closer to a self-driving model—without removing humans from the loop. The recent IDC APJ survey found:
- 40% of organizations say they are already deploying AI agents
- Nearly half see IT as the top beneficiary of agentic AI, ahead of marketing and sales
- 59% believe AI agents will shift IT from reactive management to self-governing systems, making trust and governance non-negotiable
In the webinar, SolarWinds described today’s AIOps platforms as skilled drivers using cruise control: the system can alert and adjust, but IT is still firmly behind the wheel. Agentic AI is the next step toward a self-driving car. It learns the roads, predicts sharp turns (cost spikes, traffic bursts, and new threats), and takes safer, smarter routes, but with clear rules, oversight, and explainability built in.
That’s why SolarWinds talks about AI by Design, built on four practical principles that resonated strongly with CIOs in the discussion:
- Privacy and security: Protecting the data that fuels AI and observability
- Accountability and fairness: Keeping humans in the cockpit and on the loop
- Transparency: Making AI-driven decisions explainable and auditable
- Simplicity: Ensuring the experience is usable for more than a handful of specialists
Agentic AI in IT isn’t about replacing teams; it’s about giving them a living, learning ecosystem that continuously reduces time-to-detect and time-to-resolve while freeing people to focus on higher-value work.
Skills, People, and the Tri-Factor of Resilience
The best platforms also fail if people can’t use them. Skills are already a major constraint in APJ:
- 43% of organizations report DevOps and cloud-native skill shortages that directly delay transformation
- 24% of IT leaders cite inadequate skills development as the greatest risk to operational resilience
The irony that came through in the webinar: as AI becomes more prominent, many senior IT professionals pivot their careers toward AI roles, leaving traditional ops skills even harder to find and retain. CIOs face a double bind: it’s hard to hire for new capabilities, and it’s also hard to keep people once you’ve trained them.
IDC and SolarWinds frame operational resilience as a tri-factor problem: people, process, and technology must move together.
- People: Continuous upskilling, cross-team collaboration, and clear career paths in modern ITOps
- Process: Standardized, automated workflows that handle the routine, so humans can focus on the unusual and the high impact
- Technology: Secure-by-design, AI-powered observability and incident response that turn data overload into a competitive advantage
Autonomy without trust is chaos; autonomy with accountability is the future of modern IT operations.
Where to Go Deeper
This is only a slice of the conversation. In the full “From Insight to Action: AI-Fueled IT Ops in APJ” session, IDC and SolarWinds dive into:
- How real APJ organizations are tackling tool sprawl, data overload, and rising costs
- Which concrete AIOps and observability use cases, such as root cause analysis, guided remediation, and predictive incident prevention, are already delivering value
- What agentic AI looks like in practice inside IT operations, and how to build trust and governance from day one
If you’re looking at your own hybrid environment and wondering how to move from reactive visibility to proactive understanding, watch the full on-demand webinar to hear about the latest IDC research and the practical perspective of SolarWinds on making that shift real.
