When people talk about service reliability, they often sum it up as a percentage, but there’s much more to it than meets the eye. With the rise of AI-driven tools, it’s worth asking several questions: How is AI impacting the field of reliability engineering? Can AI effectively replace software engineers in ensuring reliability, or is it better suited as a supporting tool? What are the trade-offs of integrating AI into service reliability strategies? These questions open up an important discussion on the evolving role of engineers in an AI-driven world.
Not All Downtime Is Equal
An 8-hour outage all at once impacts a business very differently than 480 brief one-minute outages, even though they add up to the same total downtime over a year. This difference is especially important when thinking about service level agreements (SLAs) and how they’re measured. AI tools can help monitor downtime patterns, but human judgment is crucial to understand the broader business implications.
The importance of downtime is ultimately determined by business requirements and priorities, which are human-driven decisions. Factors such as customer expectations, regulatory requirements, and revenue impact play a major role in deciding acceptable levels of downtime. While AI can assist in identifying patterns and predicting potential failures, it is the responsibility of business stakeholders and engineers to make informed decisions on what level of reliability is necessary and cost-effective.
Timing matters too. A few minutes of downtime during peak hours can be far more damaging than a longer outage when traffic is low. AI can predict peak usage and help automate responses, but engineers must fine-tune these models to align with business priorities.
The Cost of Extra “Nines”
Every extra nine in your uptime percentage (like going from 99.9% to 99.99%) usually means a huge increase in engineering effort and operational overhead. AI can assist with automation and scaling, but achieving higher reliability still demands thoughtful architecture.
Lets skim through some aspects that might be considered, mentioning AI opportunities as well:
- 99.9% Uptime (8 hours 45 minutes downtime per year) – source: uptimia.com
- A single-region setup might be fine, but it’s not recommended
- Basic failover strategies
- Basic healthchecks
- Active monitoring to detect common issues
- 99.99% Uptime (52 minutes 35 seconds downtime per year)
- Multi-region deployments become necessary
- Advanced health checks with AI-driven anomaly detection
- Automated failover systems with anything from the basic conditional automation to AI-assisted decision-making
- 99.999% Uptime (5 minutes 15 seconds downtime per year)
- Full redundancy across all layers
- Continuous real-time monitoring with 24/7/365(6) guard duty most likely assisted with predictive AI insights
- Active-active deployments managed with AI-enhanced load balancing
- 99.9999% Uptime (31 seconds downtime per year)
- Chaos engineering to prepare for failures
- Automated canary releases using AI-driven rollback strategies
- Sophisticated traffic routing with self-learning AI algorithms
It is obvious that AI-tools can significantly improve reliability and possibly cut costs, especially in pure operational parts of our processes.
However, while AI can significantly assist in reaching these goals, it cannot replace the strategic thinking and contextual understanding that experienced engineers provide.
Looking Beyond the Numbers
I had a discussion with a colleague I respect, related to my appreciation and leaning towards data driven decisions. Discussion was related to onboarding of a new senior team member. We came to conclusion that, while numbers are important, especially in software engineering, social aspects must not be ignored. Translated to our topic at hand…
Knowing the math behind uptime is useful, but the real challenge is managing the broader socio-technical environment that keeps services running. AI can help automate monitoring and responses, but sustaining high reliability still requires human expertise. Engineers and whoever is involved in service reliability processes must consider:
- Whether the organization is operationally mature
- How the team approaches failure and recovery
- The quality of automation and monitoring tools, including AI-driven ones
- Response plans for incidents and outages that AI alone can’t handle
AI is a powerful tool, but it works best as an assistant rather than a replacement. Next time you’re faced with a reliability target, don’t just focus on the numbers—consider the entire system and the skilled professionals who ensure its resilience.
Do We Need AI Everywhere?
Not every aspect of reliability requires AI. In many cases, simple automation with well-structured conditional logic can be sufficient. For example, implementing automated scaling rules based on predefined thresholds—such as CPU usage or memory consumption—can effectively manage system load without the complexity of AI. Engineers should evaluate whether AI-driven solutions are truly necessary or if simpler, more reliable automation methods will do the job just as well.
Summary
AI can play a significant role in improving service reliability by automating monitoring, scaling, and decision-making processes. However, experienced engineers are irreplaceable when it comes to strategic planning, contextual decision-making, and managing socio-technical complexities. AI is currently most effective at enhancing operational tasks rather than achieving full reliability on its own. Striking the right balance between automation and human oversight is crucial for maintaining robust, reliable systems. Ultimately, while AI can support engineers, it cannot fully replace their expertise in ensuring long-term service reliability.