New models, new agents, new capabilities. It seems like every week there’s a new must-have AI function. It’s no surprise that leaders are feeling pressure to move quickly. At a PagerDuty on Tour event, a customer joked that they couldn’t fathom having a five-year AI strategy; it makes way more sense to have a five-minute one.

There’s truth in that comment. The rapid emergence of new AI tools makes long-term planning nearly impossible, and organizations are pushing AI into production faster than their operational foundations can keep pace. But in the rush to deploy new tools and avoid getting left behind, many teams are missing an important question: What happens when the AI fails?

Each new AI tool, process, or integration introduces potential failure points that didn’t exist before. The success of AI at scale depends on understanding and preparing for these new risks.

If your organization cannot detect, diagnose, recover from, and learn from AI-related failures, your strategy may be leaving you more exposed than you realize.

The new, harder-to-detect failure modes AI introduces

Like any part of a technology ecosystem, AI can fail in ways both obvious and sly. For example, a third-party AI tool that your company relies on may be experiencing an incident and simply not work. Additionally, AI can degrade quietly, behave unpredictably, or take the wrong action without triggering a clear error. When a failure like this happens, it’s often challenging to identify the root cause, especially for organizations that haven’t established processes for AI-related failures.

• Failure in these instances might look like:
• An agent acting on incomplete or misinterpreted context
• A workflow taking too long to complete or not completing
• Actions that are completed correctly 10 times, then unpredictably on the 11th
• Model drift that goes unnoticed without long-term monitoring

Organizations recognize the challenge. In our most recent PagerDuty Survey, 85% of respondents said their organization needs better procedures to detect errors or failures in AI tools. This starts with understanding why AI fails, then building incident management processes explicitly designed for AI-related incidents.

Why AI strategies break

Humans learn to work around the weaknesses and inconsistencies in their systems. They adapt and apply judgment. But when you deploy AI across an enterprise that has accumulated the complexity typical of any large organization, you introduce the risk of new, consequential failures. 

These three forms of operational debt are where most AI strategies break down.

1. Technical and automation debt

Many enterprises have accumulated years of inconsistencies across systems, services, and workflows; manual steps that were never automated, and different processes for similar tasks across teams. 

AI can help here. Implemented carefully, it can analyze these workflows service by service, identify patterns across even highly complex systems, and suggest ways to automate manual processes. The key here is providing AI with the right data to examine how the system operates and draw correct conclusions. Over time, AI learns even more about how to build more efficiency within a system and the suggested automations are more valuable for teams looking to remove toil from processes. The result is time back to focus on the work that matters.

Imagine an AI agent tasked with deploying services across environments where delivery pipelines have been standardized with human-in-the-loop approvals. The agent prepares changes, validates configurations, and flags exceptions, while engineers review and approve actions at defined checkpoints. Because build scripts, approval steps, and configuration standards are consistent across teams, deployments become repeatable and auditable. The AI handles the execution at speed, and humans retain oversight where judgment is required.

2. Integration debt

AI can’t flourish inside silos. To deliver the ROI vendors promise, it needs to work across tools, services, and data sources to gather context, take action, and complete end-to-end workflows. Many organizations are adding dozens of AI tools across different teams and departments, but when nothing connects, results linger in isolated pockets that never scale. 

But when AI tools are deployed thoughtfully and intentionally integrated across an organization’s tech stack, they can become game-changers. Organizations are using MCP (Model Context Protocol) to give AI agents and assistants secure access to additional data sources and actions in real time. 

3. Human-AI partnership debt

You can’t apply AI effectively if you don’t understand what aspects of the work should be handled by humans versus AI. Every organization has three categories of operational tasks:

• Well-understood tasks that can be completely automated
• Partially understood tasks that benefit from human and AI collaboration
• Novel tasks that require primarily human expertise, with AI support in the background

When teams clearly understand their workflows, including the steps involved, the judgment required at each stage, and which tasks repeat reliably, they can apply AI with precision. They automate routine work confidently, use AI to support complex decisions, and focus human expertise where it matters most. As a result, teams move faster, AI delivers measurable value, and work becomes intentional rather than reactive.

How to build operational resiliency for AI

Improving resiliency doesn’t mean stifling innovation. In fact, it’s the opposite. When teams know how to detect,respond to, and prevent failure, they’re empowered to try more, learn faster, and expand AI into higher-value use cases.

Below are four ways to build resilience into your operations to support and scale AI.

1. Establish an incident management process for AI failures 

When AI systems behave incorrectly, the incident can impact multiple teams, services, and even business units. Often, ownership and subject matter experts for these types of incidents are unclear.

Who responds when an AI agent takes an unintended action? How do teams diagnose whether the issue stems from the model, the data, or a downstream dependency? How do you roll back an AI decision that has already triggered cascading changes?

The most resilient organizations treat AI incident response as a cross-functional discipline. They establish clear ownership and escalation paths, create runbooks for common AI failure patterns, and ensure that when AI systems fail, the response is a coordinated business effort. 

2. Clarify where AI should and shouldn’t act

Start by mapping the types of critical work using a three-tiered framework. Identify the well-understood tasks where automation provides safe, immediate value. Reserve human oversight for areas that are novel. 

3. Create observability for AI behavior

Treat AI like any other operational system that needs monitoring. Monitor usage patterns, error signals, unexpected outputs, action logs, and long-term performance. 

However, standard observability tools may not provide the capabilities you need to manage AI systems safely. LLMOps tools such as Arize are explicitly designed for AI behavior and can detect when models degrade or agents make decisions outside acceptable parameters before they cause operational impact. 

4. Commit to continuous learning 

Just like any incident, AI-related incidents offer teams opportunities to learn and improve. These learnings are key to threading back into processes. Perhaps MTTR was exacerbated by unclear AI tool ownership. Maybe an issue wasn’t discovered early enough due to an observability gap. Documenting these sticking points can help organizations move their AI initiatives forward and combat AI-related risks.

Build resilience into your AI strategy from day one

Successful leaders understand that resilience and speed require careful balance. They consider operational risk from the outset, asking: How much risk are we willing to accept to accelerate AI adoption? 

A resiliency-first approach ensures that when AI fails, and it will, your operational systems can absorb the complexity, mitigate the risk, and keep work flowing.

Learn how the PagerDuty Operations Cloud helps teams manage incidents from detection through triage and mitigation to continuous learning, including AI failures that span multiple systems and teams.